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@@ -67,3 +67,9 @@ stack_exchange/IS/Information[[:space:]]Security[[:space:]]2016.csv filter=lfs d
 stack_exchange/IS/merged_data.csv filter=lfs diff=lfs merge=lfs -text
 stack_exchange/SE/merged_data.csv filter=lfs diff=lfs merge=lfs -text
 stack_exchange/combined_data.csv filter=lfs diff=lfs merge=lfs -text
+Phase1/Combined.csv filter=lfs diff=lfs merge=lfs -text
+Phase1/Combined_full.csv filter=lfs diff=lfs merge=lfs -text
+all_combined_data.csv.1 filter=lfs diff=lfs merge=lfs -text
+major-mid-report.pdf filter=lfs diff=lfs merge=lfs -text
+outputs/checkpoint-60/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+problem.pdf filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,4 @@
+venv/
+*.pyc
+__pycache__/
+data/

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Ayush Gupta
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LLM_training.ipynb

@@ -0,0 +1,704 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7e1651a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Input</th>\n",
+       "      <th>Tags</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Title: What is the effective differencial effe...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Title: Heat sensor with fan cooling Body: Can ...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Title: Outlet Installation--more wires than my...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Title: Buck Converter Operation Question Body:...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Title: Urgent help in area of ASIC design, ver...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               Input             Tags\n",
+       "0  Title: What is the effective differencial effe...  ['electronics']\n",
+       "1  Title: Heat sensor with fan cooling Body: Can ...  ['electronics']\n",
+       "2  Title: Outlet Installation--more wires than my...  ['electronics']\n",
+       "3  Title: Buck Converter Operation Question Body:...  ['electronics']\n",
+       "4  Title: Urgent help in area of ASIC design, ver...  ['electronics']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Read the CSV file\n",
+    "data = pd.read_csv('data/all_combined_data.csv')\n",
+    "\n",
+    "# Display the first few rows of the data\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c9057558",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Input</th>\n",
+       "      <th>Tags</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>162648</th>\n",
+       "      <td>Title: Is there an optimal strategy for placi...</td>\n",
+       "      <td>['algorithms', 'efficiency']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162649</th>\n",
+       "      <td>Title: Handling Events Across Multi-Server Ap...</td>\n",
+       "      <td>['c#', 'application-design']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162650</th>\n",
+       "      <td>Title: Is it good practice to use try catch f...</td>\n",
+       "      <td>['design', 'engineering']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162651</th>\n",
+       "      <td>Title: What do you call a process which trans...</td>\n",
+       "      <td>['python', 'naming', 'parsing', 'serialization']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162652</th>\n",
+       "      <td>Title: Testing : Why is it necessary?Body: He...</td>\n",
+       "      <td>['testing']</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    Input  \\\n",
+       "162648   Title: Is there an optimal strategy for placi...   \n",
+       "162649   Title: Handling Events Across Multi-Server Ap...   \n",
+       "162650   Title: Is it good practice to use try catch f...   \n",
+       "162651   Title: What do you call a process which trans...   \n",
+       "162652   Title: Testing : Why is it necessary?Body: He...   \n",
+       "\n",
+       "                                                    Tags  \n",
+       "162648                      ['algorithms', 'efficiency']  \n",
+       "162649                      ['c#', 'application-design']  \n",
+       "162650                         ['design', 'engineering']  \n",
+       "162651  ['python', 'naming', 'parsing', 'serialization']  \n",
+       "162652                                       ['testing']  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "297d09db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['Tags'] = data['Tags'].apply(lambda tags: tags + ['ignore'] if len(tags) == 1 else tags)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e3d5ffe0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Input</th>\n",
+       "      <th>Tags</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Title: What is the effective differencial effe...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Title: Heat sensor with fan cooling Body: Can ...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Title: Outlet Installation--more wires than my...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Title: Buck Converter Operation Question Body:...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Title: Urgent help in area of ASIC design, ver...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               Input             Tags\n",
+       "0  Title: What is the effective differencial effe...  ['electronics']\n",
+       "1  Title: Heat sensor with fan cooling Body: Can ...  ['electronics']\n",
+       "2  Title: Outlet Installation--more wires than my...  ['electronics']\n",
+       "3  Title: Buck Converter Operation Question Body:...  ['electronics']\n",
+       "4  Title: Urgent help in area of ASIC design, ver...  ['electronics']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9a1cba07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Input</th>\n",
+       "      <th>Tags</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Title: What is the effective differencial effe...</td>\n",
+       "      <td>[electronics, ignore]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Title: Heat sensor with fan cooling Body: Can ...</td>\n",
+       "      <td>[electronics, ignore]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Title: Outlet Installation--more wires than my...</td>\n",
+       "      <td>[electronics, ignore]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Title: Buck Converter Operation Question Body:...</td>\n",
+       "      <td>[electronics, ignore]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Title: Urgent help in area of ASIC design, ver...</td>\n",
+       "      <td>[electronics, ignore]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               Input                   Tags\n",
+       "0  Title: What is the effective differencial effe...  [electronics, ignore]\n",
+       "1  Title: Heat sensor with fan cooling Body: Can ...  [electronics, ignore]\n",
+       "2  Title: Outlet Installation--more wires than my...  [electronics, ignore]\n",
+       "3  Title: Buck Converter Operation Question Body:...  [electronics, ignore]\n",
+       "4  Title: Urgent help in area of ASIC design, ver...  [electronics, ignore]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b6a6eba",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'data' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 9\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m classification_report\n\u001b[0;32m----> 9\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInput\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTags\u001b[39m\u001b[38;5;124m'\u001b[39m]]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mast\u001b[39;00m\n\u001b[1;32m     12\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTags\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTags\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(ast\u001b[38;5;241m.\u001b[39mliteral_eval)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.multioutput import MultiOutputClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "\n",
+    "df = data[['Input', 'Tags']].copy()\n",
+    "import ast\n",
+    "\n",
+    "df[\"Tags\"] = df[\"Tags\"].apply(ast.literal_eval)\n",
+    "\n",
+    "# Step 1: One-hot encode the tags\n",
+    "mlb = MultiLabelBinarizer()\n",
+    "y = mlb.fit_transform(df[\"Tags\"])\n",
+    "\n",
+    "# Step 2: TF-IDF on Input column\n",
+    "tfidf = TfidfVectorizer(max_features=5000)\n",
+    "X = tfidf.fit_transform(df[\"Input\"])\n",
+    "\n",
+    "# Step 3: Train-test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Step 4: Train Random Forest in MultiOutputClassifier\n",
+    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "multi_rf = MultiOutputClassifier(rf)\n",
+    "multi_rf.fit(X_train, y_train)\n",
+    "\n",
+    "# Step 5: Predict and evaluate\n",
+    "y_pred = multi_rf.predict(X_test)\n",
+    "print(classification_report(y_test, y_pred, target_names=mlb.classes_))\n",
+    "# Step 6: Save the model\n",
+    "import joblib\n",
+    "joblib.dump(multi_rf, 'multi_rf_model.pkl')\n",
+    "# Step 7: Save the TF-IDF vectorizer\n",
+    "joblib.dump(tfidf, 'tfidf_vectorizer.pkl')\n",
+    "# Step 8: Save the label binarizer\n",
+    "joblib.dump(mlb, 'mlb.pkl')\n",
+    "# Step 9: Load the model and vectorizer\n",
+    "loaded_model = joblib.load('multi_rf_model.pkl')\n",
+    "loaded_vectorizer = joblib.load('tfidf_vectorizer.pkl')\n",
+    "loaded_mlb = joblib.load('mlb.pkl')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a11d48f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6312830d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Decoded tags: ('electronics',)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "print(\"Decoded tags:\", mlb.inverse_transform(np.array([y[0]]))[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bba02bd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ast\n",
+    "\n",
+    "df[\"Tags\"] = df[\"Tags\"].apply(ast.literal_eval)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "260ee444",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    [electronics]\n",
+      "1    [electronics]\n",
+      "2    [electronics]\n",
+      "3    [electronics]\n",
+      "4    [electronics]\n",
+      "Name: Tags, dtype: object\n",
+      "<class 'list'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Tags\"].head())\n",
+    "print(type(df[\"Tags\"].iloc[0]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "61f2205b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = mlb.fit_transform(df[\"Tags\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b6edbea6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Decoded tags: ('electronics',)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Decoded tags:\", mlb.inverse_transform(np.array([y[0]]))[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "60b80231",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, ..., 0, 0, 0], shape=(13158,))"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "30981b76",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Input    object\n",
+       "Tags     object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "132c7bfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Accuracy: 0.9140289373926052\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# Calculate accuracy for each label and then average\n",
+    "accuracies = [accuracy_score(y_test[:, i], y_pred[:, i]) for i in range(y_test.shape[1])]\n",
+    "average_accuracy = sum(accuracies) / len(accuracies)\n",
+    "\n",
+    "print(f\"Average Accuracy: {average_accuracy}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e09ede12",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Input, Tags]\n",
+      "Index: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "single_tag_rows = df[df['Tags'].apply(len) <= 3]\n",
+    "print(single_tag_rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "644cd3c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.35      0.67      0.46         9\n",
+      "           1       1.00      0.09      0.17        11\n",
+      "           2       0.91      0.71      0.80        14\n",
+      "\n",
+      "   micro avg       0.59      0.50      0.54        34\n",
+      "   macro avg       0.75      0.49      0.48        34\n",
+      "weighted avg       0.79      0.50      0.51        34\n",
+      " samples avg       0.51      0.48      0.45        34\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.multioutput import MultiOutputClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report\n",
+    "import numpy as np\n",
+    "\n",
+    "# Example: X has 100 samples, 20 features; y has 100 samples, 3 possible labels per sample\n",
+    "X = np.random.rand(100, 20)\n",
+    "y = np.random.randint(2, size=(100, 3))  # Binary presence/absence for 3 labels\n",
+    "\n",
+    "# Train-test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Initialize base RandomForest classifier\n",
+    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "\n",
+    "# Wrap it for multi-label classification\n",
+    "multi_rf = MultiOutputClassifier(rf)\n",
+    "\n",
+    "# Train the model\n",
+    "multi_rf.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict\n",
+    "y_pred = multi_rf.predict(X_test)\n",
+    "\n",
+    "# Evaluate\n",
+    "print(classification_report(y_test, y_pred))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "32e4d5bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 1])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2477b56",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "major02",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Methords/TFIDF.py

@@ -0,0 +1,53 @@
+import pandas as pd
+import pandas as pd
+import re
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from nltk.stem import WordNetLemmatizer
+
+# Download necessary NLTK resources
+# nltk.download('stopwords')
+# nltk.download('punkt')
+# nltk.download('wordnet')
+
+# Read the CSV file
+file_path = '/home/darth/#/SEQuestionClassifier/data/all_combined_data.csv'
+df = pd.read_csv(file_path)
+
+import ast
+df["Tags"] = df["Tags"].apply(ast.literal_eval)
+
+lemmatizer = WordNetLemmatizer()
+stop_words = set(stopwords.words('english'))
+
+def preprocess_text(text):
+    """Function to clean text and perform lemitisation"""
+    text = text.lower()
+    text = re.sub(r'[^\w\s]', '', text)
+    words = word_tokenize(text)
+    words = [lemmatizer.lemmatize(word) for word in words if word not in stop_words]
+    return " ".join(words)
+
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import MultiLabelBinarizer
+
+def vectorirse_text(text):
+    """ Recieves text as input and returns TF-IDF vectors"""
+    text = text.apply(preprocess_text)
+    tfidf = TfidfVectorizer(max_features=500000)
+    X = tfidf.fit_transform(text)
+    return X
+
+def label_encoding(input):
+    mlb = MultiLabelBinarizer()
+    return mlb.fit_transform(input)
+
+
+X = vectorirse_text(df['Input'])
+y = label_encoding(df['Tags'])
+
+
+

Phase1/Combined.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:91d244e21d42b9026b784510dd08cac0287828053e0177259c5fe4935d8ad54a
+size 24834935

Phase1/Combined_full.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b343bbc3c288ac26557ffceb8a8bff0b9d760832bea2493341e277bf6135b2a9
+size 24798095

all_combined_data.csv

@@ -1,3 +1,3 @@
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-oid sha256:29cfef6c46dcd2826d4ca866c576fb5f806be5fa79d292c6f5b9f7542c117053
-size 167805959
+oid sha256:d0d3216e994475f803750a0364a3c76ba4b52bda114360e8fab5390c73657f9b
+size 166700687

all_combined_data.csv.1

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29cfef6c46dcd2826d4ca866c576fb5f806be5fa79d292c6f5b9f7542c117053
+size 167805959

ensemble_phase1.ipynb

@@ -0,0 +1,1767 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a7a23542",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "dataset = pd.read_csv(\"data/hackerank/combined_data.csv\")\n",
+    "import ast\n",
+    "\n",
+    "def clean_tags(tag_string):\n",
+    "    # Convert the string to a list\n",
+    "    tag_list = ast.literal_eval(tag_string)\n",
+    "    # Join the list into a comma-separated string\n",
+    "    return ', '.join(tag_list)\n",
+    "\n",
+    "dataset['Tags']=dataset['Tags'].apply(clean_tags)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "da4491c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "362c543a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "def vectorirse_text(text):\n",
+    "    \"\"\" Recieves text as input and returns TF-IDF vectors\"\"\"\n",
+    "    tfidf = TfidfVectorizer(max_features=500000)\n",
+    "    X = tfidf.fit_transform(text)\n",
+    "    return X\n",
+    "\n",
+    "def label_encoding(input):\n",
+    "    label_encoder = LabelEncoder()\n",
+    "    return label_encoder.fit_transform(input)\n",
+    "\n",
+    "\n",
+    "X = vectorirse_text(df['Input'])\n",
+    "y = label_encoding(df['Tags'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "b86c2a03",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Accuracy on Test Set: 0.9124\n",
+      "\n",
+      "✅ Classification Report on Test Set:\n",
+      "\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.95      0.90      0.92       792\n",
+      "           1       0.86      0.85      0.85       709\n",
+      "           2       0.92      0.93      0.92       729\n",
+      "           3       0.94      0.94      0.94       830\n",
+      "           4       0.92      0.86      0.89       486\n",
+      "           5       0.94      0.94      0.94       680\n",
+      "           6       0.94      0.99      0.96       803\n",
+      "           7       0.85      0.88      0.86       634\n",
+      "           8       0.83      0.85      0.84       704\n",
+      "           9       0.97      0.95      0.96       684\n",
+      "\n",
+      "    accuracy                           0.91      7051\n",
+      "   macro avg       0.91      0.91      0.91      7051\n",
+      "weighted avg       0.91      0.91      0.91      7051\n",
+      "\n",
+      "✅ Confusion Matrix on Test Set:\n",
+      "\n",
+      "[[711  22   8   4   0   7   4  22  12   2]\n",
+      " [ 12 604   8   3   2  10   5  12  51   2]\n",
+      " [  7   2 679   5   7   7   8   8   6   0]\n",
+      " [  1   6   4 783  16   5   2   5   6   2]\n",
+      " [  1   6  10  21 418   1   4   4  17   4]\n",
+      " [  5  10   8   1   0 639  11   3   3   0]\n",
+      " [  0   0   6   0   0   3 793   1   0   0]\n",
+      " [  6  26   7   4   1   0  11 557  19   3]\n",
+      " [  7  27  10  11   4   4   4  36 597   4]\n",
+      " [  0   1   1   4   4   3   1  11   7 652]]\n",
+      "✅ Saved probabilities to Phase1/TFIDF.csv successfully!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>class_0</th>\n",
+       "      <th>class_1</th>\n",
+       "      <th>class_2</th>\n",
+       "      <th>class_3</th>\n",
+       "      <th>class_4</th>\n",
+       "      <th>class_5</th>\n",
+       "      <th>class_6</th>\n",
+       "      <th>class_7</th>\n",
+       "      <th>class_8</th>\n",
+       "      <th>class_9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.004856</td>\n",
+       "      <td>0.009864</td>\n",
+       "      <td>0.864422</td>\n",
+       "      <td>0.008940</td>\n",
+       "      <td>0.009183</td>\n",
+       "      <td>0.016639</td>\n",
+       "      <td>0.039277</td>\n",
+       "      <td>0.026571</td>\n",
+       "      <td>0.010824</td>\n",
+       "      <td>0.009424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.005297</td>\n",
+       "      <td>0.006558</td>\n",
+       "      <td>0.913401</td>\n",
+       "      <td>0.006262</td>\n",
+       "      <td>0.011597</td>\n",
+       "      <td>0.018983</td>\n",
+       "      <td>0.018909</td>\n",
+       "      <td>0.006744</td>\n",
+       "      <td>0.009302</td>\n",
+       "      <td>0.002946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.030078</td>\n",
+       "      <td>0.035363</td>\n",
+       "      <td>0.681136</td>\n",
+       "      <td>0.038987</td>\n",
+       "      <td>0.025477</td>\n",
+       "      <td>0.060935</td>\n",
+       "      <td>0.024796</td>\n",
+       "      <td>0.033198</td>\n",
+       "      <td>0.045627</td>\n",
+       "      <td>0.024402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.014440</td>\n",
+       "      <td>0.015472</td>\n",
+       "      <td>0.790973</td>\n",
+       "      <td>0.023952</td>\n",
+       "      <td>0.023529</td>\n",
+       "      <td>0.020345</td>\n",
+       "      <td>0.037677</td>\n",
+       "      <td>0.027091</td>\n",
+       "      <td>0.030567</td>\n",
+       "      <td>0.015954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.012646</td>\n",
+       "      <td>0.019750</td>\n",
+       "      <td>0.612334</td>\n",
+       "      <td>0.116218</td>\n",
+       "      <td>0.036532</td>\n",
+       "      <td>0.027983</td>\n",
+       "      <td>0.045918</td>\n",
+       "      <td>0.060600</td>\n",
+       "      <td>0.036102</td>\n",
+       "      <td>0.031918</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   y_true   class_0   class_1   class_2   class_3   class_4   class_5  \\\n",
+       "0       2  0.004856  0.009864  0.864422  0.008940  0.009183  0.016639   \n",
+       "1       2  0.005297  0.006558  0.913401  0.006262  0.011597  0.018983   \n",
+       "2       2  0.030078  0.035363  0.681136  0.038987  0.025477  0.060935   \n",
+       "3       2  0.014440  0.015472  0.790973  0.023952  0.023529  0.020345   \n",
+       "4       2  0.012646  0.019750  0.612334  0.116218  0.036532  0.027983   \n",
+       "\n",
+       "    class_6   class_7   class_8   class_9  \n",
+       "0  0.039277  0.026571  0.010824  0.009424  \n",
+       "1  0.018909  0.006744  0.009302  0.002946  \n",
+       "2  0.024796  0.033198  0.045627  0.024402  \n",
+       "3  0.037677  0.027091  0.030567  0.015954  \n",
+       "4  0.045918  0.060600  0.036102  0.031918  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "\n",
+    "# Assuming df is already loaded\n",
+    "# And vectorise_text and label_encoding are already available\n",
+    "\n",
+    "# Step 1: Preprocess\n",
+    "# X = vectorise_text(df['text'])\n",
+    "# y = label_encoding(df['label'])\n",
+    "\n",
+    "# Step 2: Train-test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Step 3: Train Logistic Regression\n",
+    "model = LogisticRegression(max_iter=1000)\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Step 4: Predict on the test set\n",
+    "y_preds = model.predict(X_test)\n",
+    "\n",
+    "# Step 5: Evaluate\n",
+    "acc = accuracy_score(y_test, y_preds)\n",
+    "print(f\"✅ Accuracy on Test Set: {acc:.4f}\\n\")\n",
+    "\n",
+    "print(\"✅ Classification Report on Test Set:\\n\")\n",
+    "print(classification_report(y_test, y_preds))\n",
+    "\n",
+    "print(\"✅ Confusion Matrix on Test Set:\\n\")\n",
+    "print(confusion_matrix(y_test, y_preds))\n",
+    "\n",
+    "# ----------------------------\n",
+    "# Step 6: Predict probabilities for FULL fX\n",
+    "# ----------------------------\n",
+    "\n",
+    "# Assuming fX and fy are available (full dataset for probability generation)\n",
+    "# fX = vectorise_text(df_full['text'])\n",
+    "# fy = label_encoding(df_full['label'])\n",
+    "\n",
+    "y_proba = model.predict_proba(X)\n",
+    "\n",
+    "# Step 7: Save probabilities\n",
+    "proba_df = pd.DataFrame(y_proba, columns=[f'class_{i}' for i in range(y_proba.shape[1])])\n",
+    "proba_df.insert(0, 'y_true', y)\n",
+    "\n",
+    "proba_df.to_csv('Phase1/TFIDF.csv', index=False)\n",
+    "\n",
+    "print(\"✅ Saved probabilities to Phase1/TFIDF.csv successfully!\")\n",
+    "proba_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bdabea9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35251"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "7114ae44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gensim\n",
+    "import numpy as np\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "df['tokens'] = df['Input'].apply(lambda x: x.split())\n",
+    "\n",
+    "w2v_model = gensim.models.Word2Vec(sentences=df['tokens'], vector_size=10000, window=5, min_count=2, workers=10)\n",
+    "\n",
+    "def get_sentence_embedding(tokens):\n",
+    "    vectors = [w2v_model.wv[word] for word in tokens if word in w2v_model.wv]\n",
+    "    return np.mean(vectors, axis=0) if vectors else np.zeros(100)\n",
+    "\n",
+    "X_w2v = np.array(df['Input'].apply(get_sentence_embedding).tolist())\n",
+    "y_encoded = label_encoding(df['Tags'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "de12ab7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = X_w2v\n",
+    "y = y_encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "c5777437",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/darth/.pyenv/versions/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [20:55:48] WARNING: /workspace/src/learner.cc:738: \n",
+      "Parameters: { \"use_label_encoder\" } are not used.\n",
+      "\n",
+      "  bst.update(dtrain, iteration=i, fobj=obj)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Accuracy on Test Set: 0.3624\n",
+      "\n",
+      "✅ Classification Report on Test Set:\n",
+      "\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.36      0.37      0.36       792\n",
+      "           1       0.23      0.21      0.22       709\n",
+      "           2       0.35      0.31      0.33       729\n",
+      "           3       0.36      0.42      0.39       830\n",
+      "           4       0.46      0.36      0.40       486\n",
+      "           5       0.37      0.33      0.35       680\n",
+      "           6       0.46      0.62      0.53       803\n",
+      "           7       0.29      0.27      0.28       634\n",
+      "           8       0.31      0.24      0.27       704\n",
+      "           9       0.40      0.44      0.42       684\n",
+      "\n",
+      "    accuracy                           0.36      7051\n",
+      "   macro avg       0.36      0.36      0.35      7051\n",
+      "weighted avg       0.36      0.36      0.36      7051\n",
+      "\n",
+      "✅ Confusion Matrix on Test Set:\n",
+      "\n",
+      "[[293  69  53  87  15  55  61  51  49  59]\n",
+      " [119 150  63 103  22  43  52  58  54  45]\n",
+      " [ 74  80 225  73  20  55  64  53  50  35]\n",
+      " [ 62  65  54 349  37  40  70  41  47  65]\n",
+      " [ 16  30  28  61 173  21  36  30  31  60]\n",
+      " [ 58  46  44  52  17 224 123  47  36  33]\n",
+      " [ 26  38  37  53  13  52 495  36  22  31]\n",
+      " [ 52  60  44  77  18  45  80 173  32  53]\n",
+      " [ 79  62  65  69  36  47  42  52 170  82]\n",
+      " [ 39  48  31  56  29  24  46  46  62 303]]\n",
+      "\n",
+      "✅ Also saved class probabilities to Phase1/Word2Vec_Proba.csv!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>class_0</th>\n",
+       "      <th>class_1</th>\n",
+       "      <th>class_2</th>\n",
+       "      <th>class_3</th>\n",
+       "      <th>class_4</th>\n",
+       "      <th>class_5</th>\n",
+       "      <th>class_6</th>\n",
+       "      <th>class_7</th>\n",
+       "      <th>class_8</th>\n",
+       "      <th>class_9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.001849</td>\n",
+       "      <td>0.025684</td>\n",
+       "      <td>0.877031</td>\n",
+       "      <td>0.003476</td>\n",
+       "      <td>0.035381</td>\n",
+       "      <td>0.001487</td>\n",
+       "      <td>0.005956</td>\n",
+       "      <td>0.028449</td>\n",
+       "      <td>0.009045</td>\n",
+       "      <td>0.011643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.015427</td>\n",
+       "      <td>0.064957</td>\n",
+       "      <td>0.766613</td>\n",
+       "      <td>0.013290</td>\n",
+       "      <td>0.003748</td>\n",
+       "      <td>0.015020</td>\n",
+       "      <td>0.071054</td>\n",
+       "      <td>0.031261</td>\n",
+       "      <td>0.015151</td>\n",
+       "      <td>0.003479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.002920</td>\n",
+       "      <td>0.028854</td>\n",
+       "      <td>0.895788</td>\n",
+       "      <td>0.003870</td>\n",
+       "      <td>0.001294</td>\n",
+       "      <td>0.027167</td>\n",
+       "      <td>0.016574</td>\n",
+       "      <td>0.002937</td>\n",
+       "      <td>0.015733</td>\n",
+       "      <td>0.004863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.042182</td>\n",
+       "      <td>0.080883</td>\n",
+       "      <td>0.610961</td>\n",
+       "      <td>0.017962</td>\n",
+       "      <td>0.013955</td>\n",
+       "      <td>0.062833</td>\n",
+       "      <td>0.094489</td>\n",
+       "      <td>0.048541</td>\n",
+       "      <td>0.008779</td>\n",
+       "      <td>0.019415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.047361</td>\n",
+       "      <td>0.035454</td>\n",
+       "      <td>0.254978</td>\n",
+       "      <td>0.578128</td>\n",
+       "      <td>0.000258</td>\n",
+       "      <td>0.002649</td>\n",
+       "      <td>0.003503</td>\n",
+       "      <td>0.044763</td>\n",
+       "      <td>0.030260</td>\n",
+       "      <td>0.002646</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   y_true   class_0   class_1   class_2   class_3   class_4   class_5  \\\n",
+       "0       2  0.001849  0.025684  0.877031  0.003476  0.035381  0.001487   \n",
+       "1       2  0.015427  0.064957  0.766613  0.013290  0.003748  0.015020   \n",
+       "2       2  0.002920  0.028854  0.895788  0.003870  0.001294  0.027167   \n",
+       "3       2  0.042182  0.080883  0.610961  0.017962  0.013955  0.062833   \n",
+       "4       2  0.047361  0.035454  0.254978  0.578128  0.000258  0.002649   \n",
+       "\n",
+       "    class_6   class_7   class_8   class_9  \n",
+       "0  0.005956  0.028449  0.009045  0.011643  \n",
+       "1  0.071054  0.031261  0.015151  0.003479  \n",
+       "2  0.016574  0.002937  0.015733  0.004863  \n",
+       "3  0.094489  0.048541  0.008779  0.019415  \n",
+       "4  0.003503  0.044763  0.030260  0.002646  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xgboost as xgb\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "# Step 1: Ensure Phase1 folder exists\n",
+    "os.makedirs('Phase1', exist_ok=True)\n",
+    "\n",
+    "# Step 2: Train-test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_w2v, y_encoded, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Step 3: Train XGBoost model\n",
+    "model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Step 4: Predict on the test set\n",
+    "y_preds = model.predict(X_test)\n",
+    "\n",
+    "# Step 5: Evaluate\n",
+    "acc = accuracy_score(y_test, y_preds)\n",
+    "print(f\"✅ Accuracy on Test Set: {acc:.4f}\\n\")\n",
+    "\n",
+    "print(\"✅ Classification Report on Test Set:\\n\")\n",
+    "print(classification_report(y_test, y_preds))\n",
+    "\n",
+    "print(\"✅ Confusion Matrix on Test Set:\\n\")\n",
+    "print(confusion_matrix(y_test, y_preds))\n",
+    "\n",
+    "# ---------------------------------\n",
+    "# Step 6: Predict probabilities on full data\n",
+    "# ---------------------------------\n",
+    "y_proba = model.predict_proba(X_w2v)\n",
+    "\n",
+    "proba_df = pd.DataFrame(y_proba, columns=[f'class_{i}' for i in range(y_proba.shape[1])])\n",
+    "proba_df.insert(0, 'y_true', y_encoded)\n",
+    "\n",
+    "proba_df.to_csv('Phase1/Word2Vec_Proba.csv', index=False)\n",
+    "\n",
+    "print(\"\\n✅ Also saved class probabilities to Phase1/Word2Vec_Proba.csv!\")\n",
+    "proba_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "22efac38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proba_df.to_csv('Phase1/Word2Vec.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "73fff7e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating GloVe Embeddings: 100%|██████████| 35251/35251 [00:01<00:00, 22919.42it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(35251, 300)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import gensim.downloader as api\n",
+    "\n",
+    "# Load Pretrained GloVe Model\n",
+    "glove_model = api.load(\"glove-wiki-gigaword-300\")  \n",
+    "\n",
+    "# Convert a sentence to a vector by averaging word embeddings\n",
+    "def get_sentence_embedding(sentence, model, dim=300):\n",
+    "    words = sentence.split()\n",
+    "    word_vectors = [model[word] for word in words if word in model]\n",
+    "    return np.mean(word_vectors, axis=0) if word_vectors else np.zeros(dim)\n",
+    "\n",
+    "text_samples = [\"GloVe embeddings capture word semantics\"]\n",
+    "embeddings = np.array([get_sentence_embedding(text, glove_model) for text in text_samples])\n",
+    "\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "# Generate GloVe embeddings for the entire dataset with a progress bar\n",
+    "df['glove_embeddings'] = df['Input'].apply(lambda x: get_sentence_embedding(x, glove_model))\n",
+    "\n",
+    "# Convert the list of embeddings to a numpy array\n",
+    "X_glove = np.array([get_sentence_embedding(text, glove_model) for text in tqdm(df['Input'], desc=\"Generating GloVe Embeddings\")])\n",
+    "\n",
+    "# Check the shape of the generated embeddings\n",
+    "print(X_glove.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "05754d5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df['glove_embeddings']\n",
+    "y = label_encoding(df['Tags']) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "3092399e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Accuracy on Test Set: 0.7103\n",
+      "\n",
+      "✅ Classification Report on Test Set:\n",
+      "\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.66      0.64      0.65       792\n",
+      "           1       0.45      0.50      0.47       709\n",
+      "           2       0.80      0.79      0.79       729\n",
+      "           3       0.70      0.70      0.70       830\n",
+      "           4       0.72      0.63      0.67       486\n",
+      "           5       0.81      0.83      0.82       680\n",
+      "           6       0.91      0.94      0.93       803\n",
+      "           7       0.68      0.74      0.70       634\n",
+      "           8       0.59      0.53      0.56       704\n",
+      "           9       0.78      0.76      0.77       684\n",
+      "\n",
+      "    accuracy                           0.71      7051\n",
+      "   macro avg       0.71      0.71      0.71      7051\n",
+      "weighted avg       0.71      0.71      0.71      7051\n",
+      "\n",
+      "✅ Confusion Matrix on Test Set:\n",
+      "\n",
+      "[[506 137  14  16   5  24   5  31  42  12]\n",
+      " [123 354  16  20   7  31  12  37  88  21]\n",
+      " [ 17  22 575  28  22  25   9  14  14   3]\n",
+      " [ 19  35  15 581  45  16   5  27  47  40]\n",
+      " [  4  26  24  63 308  10  11   8  18  14]\n",
+      " [ 19  29  17  11   5 565  13   7   2  12]\n",
+      " [  1   4  10   5   4   7 758   9   4   1]\n",
+      " [ 24  37  19  19   7   5  11 466  24  22]\n",
+      " [ 39 108  28  48  12   5   6  59 376  23]\n",
+      " [  9  30   5  43  13   8   3  30  24 519]]\n",
+      "\n",
+      "✅ Saved probabilities to Phase1/GloVe.csv!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>class_0</th>\n",
+       "      <th>class_1</th>\n",
+       "      <th>class_2</th>\n",
+       "      <th>class_3</th>\n",
+       "      <th>class_4</th>\n",
+       "      <th>class_5</th>\n",
+       "      <th>class_6</th>\n",
+       "      <th>class_7</th>\n",
+       "      <th>class_8</th>\n",
+       "      <th>class_9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.351565e-08</td>\n",
+       "      <td>3.210468e-08</td>\n",
+       "      <td>0.999735</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>0.000204</td>\n",
+       "      <td>0.000004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5.428256e-04</td>\n",
+       "      <td>5.070415e-05</td>\n",
+       "      <td>0.998922</td>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>0.000039</td>\n",
+       "      <td>0.000233</td>\n",
+       "      <td>0.000101</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>0.000066</td>\n",
+       "      <td>0.000011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.161756e-03</td>\n",
+       "      <td>1.901476e-03</td>\n",
+       "      <td>0.992795</td>\n",
+       "      <td>0.000341</td>\n",
+       "      <td>0.000429</td>\n",
+       "      <td>0.000718</td>\n",
+       "      <td>0.000008</td>\n",
+       "      <td>0.000079</td>\n",
+       "      <td>0.000842</td>\n",
+       "      <td>0.001723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5.475805e-02</td>\n",
+       "      <td>1.380654e-01</td>\n",
+       "      <td>0.112860</td>\n",
+       "      <td>0.074255</td>\n",
+       "      <td>0.013319</td>\n",
+       "      <td>0.010117</td>\n",
+       "      <td>0.116254</td>\n",
+       "      <td>0.105238</td>\n",
+       "      <td>0.271900</td>\n",
+       "      <td>0.103235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.400376e-02</td>\n",
+       "      <td>7.604539e-02</td>\n",
+       "      <td>0.115192</td>\n",
+       "      <td>0.251055</td>\n",
+       "      <td>0.087261</td>\n",
+       "      <td>0.082912</td>\n",
+       "      <td>0.030533</td>\n",
+       "      <td>0.185033</td>\n",
+       "      <td>0.074275</td>\n",
+       "      <td>0.083690</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   y_true       class_0       class_1   class_2   class_3   class_4   class_5  \\\n",
+       "0       2  3.351565e-08  3.210468e-08  0.999735  0.000002  0.000007  0.000002   \n",
+       "1       2  5.428256e-04  5.070415e-05  0.998922  0.000006  0.000039  0.000233   \n",
+       "2       2  1.161756e-03  1.901476e-03  0.992795  0.000341  0.000429  0.000718   \n",
+       "3       2  5.475805e-02  1.380654e-01  0.112860  0.074255  0.013319  0.010117   \n",
+       "4       2  1.400376e-02  7.604539e-02  0.115192  0.251055  0.087261  0.082912   \n",
+       "\n",
+       "    class_6   class_7   class_8   class_9  \n",
+       "0  0.000029  0.000017  0.000204  0.000004  \n",
+       "1  0.000101  0.000028  0.000066  0.000011  \n",
+       "2  0.000008  0.000079  0.000842  0.001723  \n",
+       "3  0.116254  0.105238  0.271900  0.103235  \n",
+       "4  0.030533  0.185033  0.074275  0.083690  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import numpy as np\n",
+    "\n",
+    "# Step 0: Ensure Phase1 folder exists\n",
+    "os.makedirs('Phase1', exist_ok=True)\n",
+    "\n",
+    "# Step 1: Fix X if needed\n",
+    "if isinstance(X.iloc[0], np.ndarray):\n",
+    "    X = np.vstack(X.values)\n",
+    "\n",
+    "# Step 2: Train-test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Step 3: Train SVM model\n",
+    "model = SVC(kernel='linear', probability=True)\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Step 4: Predict on the test set\n",
+    "y_preds = model.predict(X_test)\n",
+    "\n",
+    "# Step 5: Evaluate\n",
+    "acc = accuracy_score(y_test, y_preds)\n",
+    "print(f\"✅ Accuracy on Test Set: {acc:.4f}\\n\")\n",
+    "\n",
+    "print(\"✅ Classification Report on Test Set:\\n\")\n",
+    "print(classification_report(y_test, y_preds))\n",
+    "\n",
+    "print(\"✅ Confusion Matrix on Test Set:\\n\")\n",
+    "print(confusion_matrix(y_test, y_preds))\n",
+    "\n",
+    "# ---------------------------------\n",
+    "# Step 6: Predict probabilities on full data\n",
+    "# ---------------------------------\n",
+    "y_proba = model.predict_proba(X)\n",
+    "\n",
+    "# Step 7: Save probability scores\n",
+    "proba_df = pd.DataFrame(y_proba, columns=[f'class_{i}' for i in range(y_proba.shape[1])])\n",
+    "proba_df.insert(0, 'y_true', y)\n",
+    "\n",
+    "proba_df.to_csv('Phase1/GloVe.csv', index=False)\n",
+    "\n",
+    "print(\"\\n✅ Saved probabilities to Phase1/GloVe.csv!\")\n",
+    "proba_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "046eeb56",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting transformers\n",
+      "  Using cached transformers-4.51.3-py3-none-any.whl (10.4 MB)\n",
+      "Requirement already satisfied: filelock in /home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages (from transformers) (3.18.0)\n",
+      "Collecting requests\n",
+      "  Using cached requests-2.32.3-py3-none-any.whl (64 kB)\n",
+      "Collecting pyyaml>=5.1\n",
+      "  Using cached PyYAML-6.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (751 kB)\n",
+      "Collecting huggingface-hub<1.0,>=0.30.0\n",
+      "  Using cached huggingface_hub-0.30.2-py3-none-any.whl (481 kB)\n",
+      "Collecting tokenizers<0.22,>=0.21\n",
+      "  Using cached tokenizers-0.21.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.0 MB)\n",
+      "Collecting safetensors>=0.4.3\n",
+      "  Using cached safetensors-0.5.3-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (471 kB)\n",
+      "Requirement already satisfied: numpy>=1.17 in /home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages (from transformers) (1.26.4)\n",
+      "Requirement already satisfied: tqdm>=4.27 in /home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages (from transformers) (4.67.1)\n",
+      "Requirement already satisfied: packaging>=20.0 in /home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages (from transformers) (25.0)\n",
+      "Requirement already satisfied: regex!=2019.12.17 in /home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages (from transformers) (2024.11.6)\n",
+      "Requirement already satisfied: fsspec>=2023.5.0 in /home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (2025.3.2)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (4.13.2)\n",
+      "Collecting idna<4,>=2.5\n",
+      "  Using cached idna-3.10-py3-none-any.whl (70 kB)\n",
+      "Collecting certifi>=2017.4.17\n",
+      "  Using cached certifi-2025.4.26-py3-none-any.whl (159 kB)\n",
+      "Collecting urllib3<3,>=1.21.1\n",
+      "  Using cached urllib3-2.4.0-py3-none-any.whl (128 kB)\n",
+      "Collecting charset-normalizer<4,>=2\n",
+      "  Using cached charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (146 kB)\n",
+      "Installing collected packages: urllib3, safetensors, pyyaml, idna, charset-normalizer, certifi, requests, huggingface-hub, tokenizers, transformers\n",
+      "Successfully installed certifi-2025.4.26 charset-normalizer-3.4.1 huggingface-hub-0.30.2 idna-3.10 pyyaml-6.0.2 requests-2.32.3 safetensors-0.5.3 tokenizers-0.21.1 transformers-4.51.3 urllib3-2.4.0\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76540636",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'label_encoding' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m X \u001b[38;5;241m=\u001b[39m embeddings\n\u001b[0;32m----> 2\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mlabel_encoding\u001b[49m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTags\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'label_encoding' is not defined"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "335daa95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing: 100%|██████████| 2204/2204 [02:07<00:00, 17.25it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "from transformers import AlbertTokenizer, AlbertModel\n",
+    "\n",
+    "# Load ALBERT tokenizer and model\n",
+    "tokenizer = AlbertTokenizer.from_pretrained(\"albert-base-v2\")\n",
+    "model = AlbertModel.from_pretrained(\"albert-base-v2\")\n",
+    "\n",
+    "# Set device (GPU if available, otherwise CPU)\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model.to(device)\n",
+    "\n",
+    "# Load your dataset (Ensure df['text'] contains the text data)\n",
+    "# df = pd.read_csv(\"your_dataset.csv\")  # Replace with your dataset file\n",
+    "texts = df[\"Input\"].tolist()\n",
+    "\n",
+    "# Function to generate embeddings\n",
+    "def get_albert_embeddings(texts, batch_size=16):\n",
+    "    model.eval()  # Set model to evaluation mode\n",
+    "    embeddings_list = []\n",
+    "\n",
+    "    for i in tqdm(range(0, len(texts), batch_size), desc=\"Processing\"):\n",
+    "        batch_texts = texts[i : i + batch_size]\n",
+    "        \n",
+    "        # Tokenize the batch\n",
+    "        inputs = tokenizer(batch_texts, return_tensors=\"pt\", padding=True, truncation=True, max_length=512)\n",
+    "        inputs = {key: val.to(device) for key, val in inputs.items()}  # Move to GPU/CPU\n",
+    "        \n",
+    "        # Get embeddings\n",
+    "        with torch.no_grad():\n",
+    "            outputs = model(**inputs)\n",
+    "        \n",
+    "        # Extract [CLS] token representation (sentence embedding)\n",
+    "        cls_embeddings = outputs.last_hidden_state[:, 0, :].cpu().numpy()\n",
+    "        embeddings_list.append(cls_embeddings)\n",
+    "\n",
+    "    return np.vstack(embeddings_list)  # Stack all embeddings\n",
+    "\n",
+    "# Generate embeddings for all texts\n",
+    "embeddings = get_albert_embeddings(texts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "4cca424b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "def vectorirse_text(text):\n",
+    "    \"\"\" Recieves text as input and returns TF-IDF vectors\"\"\"\n",
+    "    tfidf = TfidfVectorizer(max_features=500000)\n",
+    "    X = tfidf.fit_transform(text)\n",
+    "    return X\n",
+    "\n",
+    "def label_encoding(input):\n",
+    "    label_encoder = LabelEncoder()\n",
+    "    return label_encoder.fit_transform(input)\n",
+    "\n",
+    "X = embeddings\n",
+    "y = label_encoding(df['Tags'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "c7f18d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/darth/.pyenv/versions/major02/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Accuracy on Test Set: 0.7949\n",
+      "\n",
+      "✅ Classification Report on Test Set:\n",
+      "\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.76      0.73      0.74       792\n",
+      "           1       0.66      0.66      0.66       709\n",
+      "           2       0.84      0.82      0.83       729\n",
+      "           3       0.79      0.82      0.80       830\n",
+      "           4       0.79      0.76      0.77       486\n",
+      "           5       0.90      0.92      0.91       680\n",
+      "           6       0.98      0.99      0.98       803\n",
+      "           7       0.73      0.75      0.74       634\n",
+      "           8       0.65      0.63      0.64       704\n",
+      "           9       0.82      0.86      0.84       684\n",
+      "\n",
+      "    accuracy                           0.79      7051\n",
+      "   macro avg       0.79      0.79      0.79      7051\n",
+      "weighted avg       0.79      0.79      0.79      7051\n",
+      "\n",
+      "✅ Confusion Matrix on Test Set:\n",
+      "\n",
+      "[[575  95  18  10   3  12   0  33  28  18]\n",
+      " [ 84 467  12  11   5  16   1  28  69  16]\n",
+      " [ 13   9 595  26  26  21   3  12  23   1]\n",
+      " [  6  15  11 677  39   3   0  15  41  23]\n",
+      " [  5  10  11  40 370   1   6   8  21  14]\n",
+      " [  9  12  16   4   4 623   3   3   1   5]\n",
+      " [  1   1   3   1   1   0 791   4   0   1]\n",
+      " [ 25  27  16  23   6   3   5 478  35  16]\n",
+      " [ 34  56  24  38  13   6   1  58 441  33]\n",
+      " [  6  17   3  26   3   5   0  13  23 588]]\n",
+      "\n",
+      "✅ Saved class probabilities to Phase1/Albert.csv!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>class_0</th>\n",
+       "      <th>class_1</th>\n",
+       "      <th>class_2</th>\n",
+       "      <th>class_3</th>\n",
+       "      <th>class_4</th>\n",
+       "      <th>class_5</th>\n",
+       "      <th>class_6</th>\n",
+       "      <th>class_7</th>\n",
+       "      <th>class_8</th>\n",
+       "      <th>class_9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.046189e-06</td>\n",
+       "      <td>1.673384e-06</td>\n",
+       "      <td>0.996728</td>\n",
+       "      <td>8.242731e-04</td>\n",
+       "      <td>0.002150</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>2.555716e-07</td>\n",
+       "      <td>8.192644e-05</td>\n",
+       "      <td>0.000194</td>\n",
+       "      <td>8.416093e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.162968e-07</td>\n",
+       "      <td>8.667423e-07</td>\n",
+       "      <td>0.999940</td>\n",
+       "      <td>3.937645e-07</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>6.603543e-08</td>\n",
+       "      <td>2.003625e-07</td>\n",
+       "      <td>0.000051</td>\n",
+       "      <td>2.772808e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.016335e-04</td>\n",
+       "      <td>3.334410e-04</td>\n",
+       "      <td>0.998280</td>\n",
+       "      <td>5.001128e-05</td>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>0.000730</td>\n",
+       "      <td>2.283566e-07</td>\n",
+       "      <td>1.428000e-06</td>\n",
+       "      <td>0.000497</td>\n",
+       "      <td>7.577643e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.920308e-03</td>\n",
+       "      <td>1.006953e-02</td>\n",
+       "      <td>0.955455</td>\n",
+       "      <td>2.492423e-03</td>\n",
+       "      <td>0.006655</td>\n",
+       "      <td>0.000183</td>\n",
+       "      <td>3.224371e-04</td>\n",
+       "      <td>1.568429e-02</td>\n",
+       "      <td>0.006673</td>\n",
+       "      <td>5.461269e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.279302e-04</td>\n",
+       "      <td>3.630354e-05</td>\n",
+       "      <td>0.944598</td>\n",
+       "      <td>4.439209e-02</td>\n",
+       "      <td>0.002457</td>\n",
+       "      <td>0.000046</td>\n",
+       "      <td>6.339463e-06</td>\n",
+       "      <td>7.960528e-03</td>\n",
+       "      <td>0.000233</td>\n",
+       "      <td>4.337906e-05</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   y_true       class_0       class_1   class_2       class_3   class_4  \\\n",
+       "0       2  4.046189e-06  1.673384e-06  0.996728  8.242731e-04  0.002150   \n",
+       "1       2  2.162968e-07  8.667423e-07  0.999940  3.937645e-07  0.000004   \n",
+       "2       2  1.016335e-04  3.334410e-04  0.998280  5.001128e-05  0.000006   \n",
+       "3       2  1.920308e-03  1.006953e-02  0.955455  2.492423e-03  0.006655   \n",
+       "4       2  2.279302e-04  3.630354e-05  0.944598  4.439209e-02  0.002457   \n",
+       "\n",
+       "    class_5       class_6       class_7   class_8       class_9  \n",
+       "0  0.000016  2.555716e-07  8.192644e-05  0.000194  8.416093e-09  \n",
+       "1  0.000004  6.603543e-08  2.003625e-07  0.000051  2.772808e-09  \n",
+       "2  0.000730  2.283566e-07  1.428000e-06  0.000497  7.577643e-07  \n",
+       "3  0.000183  3.224371e-04  1.568429e-02  0.006673  5.461269e-04  \n",
+       "4  0.000046  6.339463e-06  7.960528e-03  0.000233  4.337906e-05  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "# Step 0: Ensure Phase1 folder exists\n",
+    "os.makedirs('Phase1', exist_ok=True)\n",
+    "\n",
+    "# Step 1: Train-test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Step 2: Train Logistic Regression on training set\n",
+    "model = LogisticRegression(max_iter=1000)\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Step 3: Predict on test set\n",
+    "y_preds = model.predict(X_test)\n",
+    "\n",
+    "# Step 4: Evaluate\n",
+    "acc = accuracy_score(y_test, y_preds)\n",
+    "print(f\"✅ Accuracy on Test Set: {acc:.4f}\\n\")\n",
+    "\n",
+    "print(\"✅ Classification Report on Test Set:\\n\")\n",
+    "print(classification_report(y_test, y_preds))\n",
+    "\n",
+    "print(\"✅ Confusion Matrix on Test Set:\\n\")\n",
+    "print(confusion_matrix(y_test, y_preds))\n",
+    "\n",
+    "# --------------------------------\n",
+    "# Step 5: Predict probabilities on full dataset\n",
+    "# --------------------------------\n",
+    "y_proba = model.predict_proba(X)\n",
+    "\n",
+    "# Step 6: Save probabilities\n",
+    "proba_df = pd.DataFrame(y_proba, columns=[f'class_{i}' for i in range(y_proba.shape[1])])\n",
+    "proba_df.insert(0, 'y_true', y)\n",
+    "\n",
+    "proba_df.to_csv('Phase1/Albert.csv', index=False)\n",
+    "\n",
+    "print(\"\\n✅ Saved class probabilities to Phase1/Albert.csv!\")\n",
+    "proba_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b9c1929",
+   "metadata": {},
+   "source": [
+    "## Ensemble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "4e6433ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y_true</th>\n",
+       "      <th>class_0</th>\n",
+       "      <th>class_1</th>\n",
+       "      <th>class_2</th>\n",
+       "      <th>class_3</th>\n",
+       "      <th>class_4</th>\n",
+       "      <th>class_5</th>\n",
+       "      <th>class_6</th>\n",
+       "      <th>class_7</th>\n",
+       "      <th>class_8</th>\n",
+       "      <th>...</th>\n",
+       "      <th>class_0</th>\n",
+       "      <th>class_1</th>\n",
+       "      <th>class_2</th>\n",
+       "      <th>class_3</th>\n",
+       "      <th>class_4</th>\n",
+       "      <th>class_5</th>\n",
+       "      <th>class_6</th>\n",
+       "      <th>class_7</th>\n",
+       "      <th>class_8</th>\n",
+       "      <th>class_9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.004856</td>\n",
+       "      <td>0.009864</td>\n",
+       "      <td>0.864422</td>\n",
+       "      <td>0.008940</td>\n",
+       "      <td>0.009183</td>\n",
+       "      <td>0.016639</td>\n",
+       "      <td>0.039277</td>\n",
+       "      <td>0.026571</td>\n",
+       "      <td>0.010824</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.046189e-06</td>\n",
+       "      <td>1.673384e-06</td>\n",
+       "      <td>0.996728</td>\n",
+       "      <td>8.242731e-04</td>\n",
+       "      <td>0.002150</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>2.555716e-07</td>\n",
+       "      <td>8.192644e-05</td>\n",
+       "      <td>0.000194</td>\n",
+       "      <td>8.416093e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.005297</td>\n",
+       "      <td>0.006558</td>\n",
+       "      <td>0.913401</td>\n",
+       "      <td>0.006262</td>\n",
+       "      <td>0.011597</td>\n",
+       "      <td>0.018983</td>\n",
+       "      <td>0.018909</td>\n",
+       "      <td>0.006744</td>\n",
+       "      <td>0.009302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.162968e-07</td>\n",
+       "      <td>8.667423e-07</td>\n",
+       "      <td>0.999940</td>\n",
+       "      <td>3.937645e-07</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>6.603543e-08</td>\n",
+       "      <td>2.003625e-07</td>\n",
+       "      <td>0.000051</td>\n",
+       "      <td>2.772808e-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.030078</td>\n",
+       "      <td>0.035363</td>\n",
+       "      <td>0.681136</td>\n",
+       "      <td>0.038987</td>\n",
+       "      <td>0.025477</td>\n",
+       "      <td>0.060935</td>\n",
+       "      <td>0.024796</td>\n",
+       "      <td>0.033198</td>\n",
+       "      <td>0.045627</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.016335e-04</td>\n",
+       "      <td>3.334410e-04</td>\n",
+       "      <td>0.998280</td>\n",
+       "      <td>5.001128e-05</td>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>0.000730</td>\n",
+       "      <td>2.283566e-07</td>\n",
+       "      <td>1.428000e-06</td>\n",
+       "      <td>0.000497</td>\n",
+       "      <td>7.577643e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.014440</td>\n",
+       "      <td>0.015472</td>\n",
+       "      <td>0.790973</td>\n",
+       "      <td>0.023952</td>\n",
+       "      <td>0.023529</td>\n",
+       "      <td>0.020345</td>\n",
+       "      <td>0.037677</td>\n",
+       "      <td>0.027091</td>\n",
+       "      <td>0.030567</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.920308e-03</td>\n",
+       "      <td>1.006953e-02</td>\n",
+       "      <td>0.955455</td>\n",
+       "      <td>2.492423e-03</td>\n",
+       "      <td>0.006655</td>\n",
+       "      <td>0.000183</td>\n",
+       "      <td>3.224371e-04</td>\n",
+       "      <td>1.568429e-02</td>\n",
+       "      <td>0.006673</td>\n",
+       "      <td>5.461269e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.012646</td>\n",
+       "      <td>0.019750</td>\n",
+       "      <td>0.612334</td>\n",
+       "      <td>0.116218</td>\n",
+       "      <td>0.036532</td>\n",
+       "      <td>0.027983</td>\n",
+       "      <td>0.045918</td>\n",
+       "      <td>0.060600</td>\n",
+       "      <td>0.036102</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.279302e-04</td>\n",
+       "      <td>3.630354e-05</td>\n",
+       "      <td>0.944598</td>\n",
+       "      <td>4.439209e-02</td>\n",
+       "      <td>0.002457</td>\n",
+       "      <td>0.000046</td>\n",
+       "      <td>6.339463e-06</td>\n",
+       "      <td>7.960528e-03</td>\n",
+       "      <td>0.000233</td>\n",
+       "      <td>4.337906e-05</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   y_true   class_0   class_1   class_2   class_3   class_4   class_5  \\\n",
+       "0       2  0.004856  0.009864  0.864422  0.008940  0.009183  0.016639   \n",
+       "1       2  0.005297  0.006558  0.913401  0.006262  0.011597  0.018983   \n",
+       "2       2  0.030078  0.035363  0.681136  0.038987  0.025477  0.060935   \n",
+       "3       2  0.014440  0.015472  0.790973  0.023952  0.023529  0.020345   \n",
+       "4       2  0.012646  0.019750  0.612334  0.116218  0.036532  0.027983   \n",
+       "\n",
+       "    class_6   class_7   class_8  ...       class_0       class_1   class_2  \\\n",
+       "0  0.039277  0.026571  0.010824  ...  4.046189e-06  1.673384e-06  0.996728   \n",
+       "1  0.018909  0.006744  0.009302  ...  2.162968e-07  8.667423e-07  0.999940   \n",
+       "2  0.024796  0.033198  0.045627  ...  1.016335e-04  3.334410e-04  0.998280   \n",
+       "3  0.037677  0.027091  0.030567  ...  1.920308e-03  1.006953e-02  0.955455   \n",
+       "4  0.045918  0.060600  0.036102  ...  2.279302e-04  3.630354e-05  0.944598   \n",
+       "\n",
+       "        class_3   class_4   class_5       class_6       class_7   class_8  \\\n",
+       "0  8.242731e-04  0.002150  0.000016  2.555716e-07  8.192644e-05  0.000194   \n",
+       "1  3.937645e-07  0.000004  0.000004  6.603543e-08  2.003625e-07  0.000051   \n",
+       "2  5.001128e-05  0.000006  0.000730  2.283566e-07  1.428000e-06  0.000497   \n",
+       "3  2.492423e-03  0.006655  0.000183  3.224371e-04  1.568429e-02  0.006673   \n",
+       "4  4.439209e-02  0.002457  0.000046  6.339463e-06  7.960528e-03  0.000233   \n",
+       "\n",
+       "        class_9  \n",
+       "0  8.416093e-09  \n",
+       "1  2.772808e-09  \n",
+       "2  7.577643e-07  \n",
+       "3  5.461269e-04  \n",
+       "4  4.337906e-05  \n",
+       "\n",
+       "[5 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Read the CSV files\n",
+    "tfidf_df = pd.read_csv('Phase1/TFIDF.csv')\n",
+    "word2vec_df = pd.read_csv('Phase1/Word2Vec.csv')\n",
+    "glove_df = pd.read_csv('Phase1/GloVe.csv')\n",
+    "albert_df = pd.read_csv('Phase1/Albert.csv')\n",
+    "\n",
+    "# Concatenate the DataFrames along the columns (axis=1)\n",
+    "combined_df = pd.concat([tfidf_df, word2vec_df.drop([\"y_true\"], axis=1), glove_df.drop([\"y_true\"], axis=1), albert_df.drop([\"y_true\"], axis=1)], axis=1)\n",
+    "\n",
+    "# Optionally, save the combined DataFrame to a new CSV file\n",
+    "# combined_df.to_csv('Phase1/Combined.csv', index=False)\n",
+    "\n",
+    "# Display the combined DataFrame\n",
+    "combined_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "cfd48211",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combined_df.to_csv('Phase1/Combined.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "3a6d7971",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35251"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(combined_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "eaa4cbff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = combined_df.drop([\"y_true\"], axis =1 )\n",
+    "y = combined_df[\"y_true\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "b7ed0d29",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logistic Regression:\n",
+      "Accuracy: 0.7984683023684583\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.76      0.79      0.77       792\n",
+      "           1       0.69      0.71      0.70       709\n",
+      "           2       0.84      0.82      0.83       729\n",
+      "           3       0.76      0.88      0.82       830\n",
+      "           4       0.84      0.74      0.79       486\n",
+      "           5       0.87      0.88      0.88       680\n",
+      "           6       0.96      0.98      0.97       803\n",
+      "           7       0.71      0.66      0.68       634\n",
+      "           8       0.70      0.57      0.63       704\n",
+      "           9       0.83      0.88      0.86       684\n",
+      "\n",
+      "    accuracy                           0.80      7051\n",
+      "   macro avg       0.80      0.79      0.79      7051\n",
+      "weighted avg       0.80      0.80      0.80      7051\n",
+      "\n",
+      "\n",
+      "Decision Tree Classifier:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/darth/.pyenv/versions/major02/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.4355410580059566\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.56      0.52      0.54       792\n",
+      "           1       0.43      0.39      0.41       709\n",
+      "           2       0.34      0.33      0.34       729\n",
+      "           3       0.44      0.50      0.47       830\n",
+      "           4       0.52      0.31      0.39       486\n",
+      "           5       0.33      0.46      0.39       680\n",
+      "           6       0.54      0.68      0.60       803\n",
+      "           7       0.33      0.34      0.34       634\n",
+      "           8       0.29      0.27      0.28       704\n",
+      "           9       0.68      0.46      0.55       684\n",
+      "\n",
+      "    accuracy                           0.44      7051\n",
+      "   macro avg       0.45      0.43      0.43      7051\n",
+      "weighted avg       0.45      0.44      0.43      7051\n",
+      "\n",
+      "\n",
+      "Random Forest Classifier:\n",
+      "Accuracy: 0.5664444759608566\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.51      0.57      0.54       792\n",
+      "           1       0.37      0.35      0.36       709\n",
+      "           2       0.56      0.56      0.56       729\n",
+      "           3       0.57      0.65      0.60       830\n",
+      "           4       0.63      0.48      0.55       486\n",
+      "           5       0.65      0.63      0.64       680\n",
+      "           6       0.83      0.97      0.90       803\n",
+      "           7       0.47      0.42      0.44       634\n",
+      "           8       0.40      0.30      0.34       704\n",
+      "           9       0.58      0.62      0.60       684\n",
+      "\n",
+      "    accuracy                           0.57      7051\n",
+      "   macro avg       0.56      0.56      0.55      7051\n",
+      "weighted avg       0.56      0.57      0.56      7051\n",
+      "\n",
+      "\n",
+      "Support Vector Machine (SVM):\n",
+      "Accuracy: 0.7730818323642037\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.73      0.76      0.74       792\n",
+      "           1       0.64      0.70      0.67       709\n",
+      "           2       0.81      0.80      0.80       729\n",
+      "           3       0.73      0.86      0.79       830\n",
+      "           4       0.81      0.64      0.72       486\n",
+      "           5       0.88      0.85      0.86       680\n",
+      "           6       0.94      0.99      0.96       803\n",
+      "           7       0.70      0.67      0.69       634\n",
+      "           8       0.66      0.53      0.59       704\n",
+      "           9       0.82      0.86      0.84       684\n",
+      "\n",
+      "    accuracy                           0.77      7051\n",
+      "   macro avg       0.77      0.76      0.77      7051\n",
+      "weighted avg       0.77      0.77      0.77      7051\n",
+      "\n",
+      "\n",
+      "k-Nearest Neighbors (k-NN):\n",
+      "Accuracy: 0.83562615231882\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.81      0.86      0.83       792\n",
+      "           1       0.71      0.77      0.74       709\n",
+      "           2       0.88      0.86      0.87       729\n",
+      "           3       0.82      0.91      0.86       830\n",
+      "           4       0.87      0.77      0.81       486\n",
+      "           5       0.91      0.88      0.90       680\n",
+      "           6       0.97      0.99      0.98       803\n",
+      "           7       0.75      0.75      0.75       634\n",
+      "           8       0.73      0.62      0.67       704\n",
+      "           9       0.90      0.89      0.90       684\n",
+      "\n",
+      "    accuracy                           0.84      7051\n",
+      "   macro avg       0.84      0.83      0.83      7051\n",
+      "weighted avg       0.84      0.84      0.83      7051\n",
+      "\n",
+      "\n",
+      "Naïve Bayes Classifier:\n",
+      "Accuracy: 0.882428024393703\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.90      0.85      0.88       792\n",
+      "           1       0.78      0.82      0.80       709\n",
+      "           2       0.93      0.88      0.91       729\n",
+      "           3       0.90      0.91      0.90       830\n",
+      "           4       0.88      0.84      0.86       486\n",
+      "           5       0.93      0.94      0.93       680\n",
+      "           6       0.98      0.99      0.98       803\n",
+      "           7       0.81      0.83      0.82       634\n",
+      "           8       0.77      0.80      0.78       704\n",
+      "           9       0.94      0.92      0.93       684\n",
+      "\n",
+      "    accuracy                           0.88      7051\n",
+      "   macro avg       0.88      0.88      0.88      7051\n",
+      "weighted avg       0.88      0.88      0.88      7051\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from models import *\n",
+    "print(\"Logistic Regression:\")\n",
+    "report, acc = logistic_regression(X, y)\n",
+    "print(\"Accuracy:\", acc)\n",
+    "print(report)\n",
+    "\n",
+    "print(\"\\nDecision Tree Classifier:\")\n",
+    "report, acc = decision_tree(X, y)\n",
+    "print(\"Accuracy:\", acc)\n",
+    "print(report)\n",
+    "\n",
+    "print(\"\\nRandom Forest Classifier:\")\n",
+    "report, acc = random_forest(X, y)\n",
+    "print(\"Accuracy:\", acc)\n",
+    "print(report)\n",
+    "\n",
+    "print(\"\\nSupport Vector Machine (SVM):\")\n",
+    "report, acc = support_vector_machine(X, y)\n",
+    "print(\"Accuracy:\", acc)\n",
+    "print(report)\n",
+    "\n",
+    "print(\"\\nk-Nearest Neighbors (k-NN):\")\n",
+    "report, acc = knn(X, y, k=5)\n",
+    "print(\"Accuracy:\", acc)\n",
+    "print(report)\n",
+    "\n",
+    "print(\"\\nNaïve Bayes Classifier:\")\n",
+    "report, acc = naive_bayes(X, y)\n",
+    "print(\"Accuracy:\", acc)\n",
+    "print(report)\n",
+    "\n",
+    "# print(\"\\nGradient Boosting (XGBoost):\")\n",
+    "# report, acc = xgboost_classifier(X, y)\n",
+    "# print(\"Accuracy:\", acc)\n",
+    "# print(report)\n",
+    "\n",
+    "# print(\"\\nMulti-Layer Perceptron (MLP - Neural Network):\")\n",
+    "# report, acc = mlp_classifier(X, y)\n",
+    "\n",
+    "# print(\"Accuracy:\", acc)\n",
+    "# print(report)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd8e808d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "major02",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

major-mid-report.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5f7088e3b28b5c14889d50b88b9ad40e489e55f2529690b23e04b0378a5c39fd
+size 653546

models.py

@@ -0,0 +1,172 @@
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import classification_report, accuracy_score
+
+# Importing Classifiers
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+from sklearn.svm import SVC
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.neural_network import MLPClassifier
+from xgboost import XGBClassifier
+
+
+# Function to preprocess data (optional scaling for certain models)
+def preprocess_data(X, scale=False):
+    if scale:
+        scaler = StandardScaler()
+        X = scaler.fit_transform(X)
+    return X
+
+# Logistic Regression
+def logistic_regression(X, y):
+    # X = preprocess_data(X, scale=True)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = LogisticRegression(multi_class='multinomial', solver='lbfgs', max_iter=1000)
+    model.fit(X_train, y_train)
+    
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# Decision Tree Classifier
+def decision_tree(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = DecisionTreeClassifier()
+    model.fit(X_train, y_train)
+    
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# Random Forest Classifier
+def random_forest(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = RandomForestClassifier(n_estimators=100, random_state=42)
+    model.fit(X_train, y_train)
+    
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# Support Vector Machine (SVM)
+def support_vector_machine(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    model = SVC(kernel='rbf', probability=True)  # Enable progress output
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# k-Nearest Neighbors (k-NN)
+def knn(X, y, k=5):
+    # X = preprocess_data(X, scale=True)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = KNeighborsClassifier(n_neighbors=k)
+    model.fit(X_train, y_train)
+    
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# Naïve Bayes
+def naive_bayes(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = MultinomialNB()
+
+    model.fit(X_train, y_train)
+    
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# Gradient Boosting Classifier (XGBoost)
+def xgboost_classifier(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')
+    model.fit(X_train, y_train)
+    
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# Multi-Layer Perceptron (MLP - Neural Network)
+# def mlp_classifier(X, y):
+#     # X = preprocess_data(X, scale=True)
+#     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+#     model = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=500, random_state=42)
+#     model.fit(X_train, y_train)
+    
+#     y_pred = model.predict(X_test)
+#     return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import classification_report, accuracy_score
+
+def mlp_classifier(X, y, epochs=500, lr=0.01):
+    # Normalize data without centering (for sparse matrices)
+    scaler = StandardScaler(with_mean=False)  # Fix applied here
+    X = scaler.fit_transform(X)
+
+    # Convert to PyTorch tensors
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    X_train, X_test = torch.tensor(X_train.toarray(), dtype=torch.float32), torch.tensor(X_test.toarray(), dtype=torch.float32)
+    y_train, y_test = torch.tensor(y_train, dtype=torch.long), torch.tensor(y_test, dtype=torch.long)
+
+    # Move to GPU if available
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    X_train, X_test, y_train, y_test = X_train.to(device), X_test.to(device), y_train.to(device), y_test.to(device)
+
+    # Define MLP model
+    class MLP(nn.Module):
+        def __init__(self, input_size, hidden_sizes, output_size):
+            super(MLP, self).__init__()
+            self.fc1 = nn.Linear(input_size, hidden_sizes[0])
+            self.fc2 = nn.Linear(hidden_sizes[0], hidden_sizes[1])
+            self.fc3 = nn.Linear(hidden_sizes[1], output_size)
+            self.relu = nn.ReLU()
+        
+        def forward(self, x):
+            x = self.relu(self.fc1(x))
+            x = self.relu(self.fc2(x))
+            x = self.fc3(x)
+            return x
+
+    # Initialize model
+    model = MLP(input_size=X.shape[1], hidden_sizes=[100, 50], output_size=len(set(y))).to(device)
+
+    # Loss function and optimizer
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=lr)
+
+    # Train model
+    for epoch in range(epochs):
+        model.train()
+        optimizer.zero_grad()
+        outputs = model(X_train)
+        loss = criterion(outputs, y_train)
+        loss.backward()
+        optimizer.step()
+
+    # Evaluate model
+    model.eval()
+    with torch.no_grad():
+        y_pred = model(X_test)
+        y_pred = torch.argmax(y_pred, dim=1).cpu().numpy()
+
+    # Return classification report and accuracy score
+    return classification_report(y_test.cpu().numpy(), y_pred), accuracy_score(y_test.cpu().numpy(), y_pred)
+
+# Example usage:
+# report, acc = logistic_regression(X, y)
+# print("Accuracy:", acc)
+# print(report)

multi_label_models.py

@@ -0,0 +1,94 @@
+from sklearn.multioutput import MultiOutputClassifier
+from sklearn.multiclass import OneVsRestClassifier
+from sklearn.metrics import classification_report, accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.svm import SVC
+from xgboost import XGBClassifier
+from sklearn.neural_network import MLPClassifier
+import numpy as np
+import pandas as pd
+
+# Logistic Regression (use OneVsRest)
+def multilabel_logistic_regression(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = OneVsRestClassifier(LogisticRegression(solver='lbfgs', max_iter=1000))
+    model.fit(X_train, y_train)
+    
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# Decision Tree
+def multilabel_decision_tree(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = MultiOutputClassifier(DecisionTreeClassifier())
+    model.fit(X_train, y_train)
+    
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# Random Forest
+def multilabel_random_forest(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = MultiOutputClassifier(RandomForestClassifier(n_estimators=100, random_state=42))
+    model.fit(X_train, y_train)
+    
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# SVM (with OneVsRest)
+def multilabel_svm(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    model = OneVsRestClassifier(SVC(kernel='rbf', probability=True))
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# k-NN (KNeighborsClassifier supports multi-label directly)
+def multilabel_knn(X, y, k=5):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    model = KNeighborsClassifier(n_neighbors=k)
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# Naive Bayes (MultinomialNB with OneVsRest)
+def multilabel_naive_bayes(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    model = OneVsRestClassifier(MultinomialNB())
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# XGBoost (with OneVsRest)
+def multilabel_xgboost(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    model = OneVsRestClassifier(XGBClassifier(use_label_encoder=False, eval_metric='logloss'))
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)
+
+# MLP (Neural Net)
+def multilabel_mlp(X, y):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    model = MultiOutputClassifier(MLPClassifier(hidden_layer_sizes=(100,), max_iter=500))
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+    return classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)

outputs/checkpoint-60/README.md

@@ -0,0 +1,202 @@
+---
+base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.15.2

outputs/checkpoint-60/adapter_config.json

@@ -0,0 +1,39 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "o_proj",
+    "down_proj",
+    "v_proj",
+    "up_proj",
+    "q_proj",
+    "gate_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

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outputs/checkpoint-60/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
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+  },
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+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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outputs/checkpoint-60/tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+  "stateful_callbacks": {
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+        "should_evaluate": false,
+        "should_log": false,
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+  "trial_name": null,
+  "trial_params": null
+}

outputs/checkpoint-60/training_args.bin

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phase_1_ensemble.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "884cc4c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Input</th>\n",
+       "      <th>Tags</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Title: What is the effective differencial effe...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Title: Heat sensor with fan cooling Body: Can ...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Title: Outlet Installation--more wires than my...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Title: Buck Converter Operation Question Body:...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Title: Urgent help in area of ASIC design, ver...</td>\n",
+       "      <td>['electronics']</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               Input             Tags\n",
+       "0  Title: What is the effective differencial effe...  ['electronics']\n",
+       "1  Title: Heat sensor with fan cooling Body: Can ...  ['electronics']\n",
+       "2  Title: Outlet Installation--more wires than my...  ['electronics']\n",
+       "3  Title: Buck Converter Operation Question Body:...  ['electronics']\n",
+       "4  Title: Urgent help in area of ASIC design, ver...  ['electronics']"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "dataset = pd.read_csv(\"/home/darth/#/SEQuestionClassifier/data/hackerank/combined_data.csv\")\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d9597ebd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6626fbc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ast\n",
+    "\n",
+    "def clean_tags(tag_string):\n",
+    "    # Convert the string to a list\n",
+    "    tag_list = ast.literal_eval(tag_string)\n",
+    "    # Join the list into a comma-separated string\n",
+    "    return ', '.join(tag_list)\n",
+    "\n",
+    "df['Tags'] = df['Tags'].apply(clean_tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c32d48e",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52801f74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "def vectorirse_text(text):\n",
+    "    \"\"\" Recieves text as input and returns TF-IDF vectors\"\"\"\n",
+    "    tfidf = TfidfVectorizer(max_features=500000)\n",
+    "    X = tfidf.fit_transform(text)\n",
+    "    return X\n",
+    "\n",
+    "def label_encoding(input):\n",
+    "    label_encoder = LabelEncoder()\n",
+    "    return label_encoder.fit_transform(input)\n",
+    "\n",
+    "\n",
+    "X = vectorirse_text(df['Input'])\n",
+    "y = label_encoding(df['Tags'])\n",
+    "\n",
+    "# Import necessary libraries\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1380ee74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "\n",
+    "# Assuming df is already loaded\n",
+    "# And the following functions are available from your preprocessing notebook:\n",
+    "# - vectorise_text\n",
+    "# - label_encoding\n",
+    "\n",
+    "# Step 1: Preproces\n",
+    "\n",
+    "# Step 2: Train Logistic Regression\n",
+    "model = LogisticRegression(max_iter=1000)\n",
+    "model.fit(X, y)\n",
+    "\n",
+    "# Step 3: Predict on the entire dataset\n",
+    "y_preds = model.predict(X)\n",
+    "\n",
+    "# Step 4: Evaluate\n",
+    "acc = accuracy_score(y, y_preds)\n",
+    "print(f\"✅ Accuracy: {acc:.4f}\\n\")\n",
+    "\n",
+    "print(\"✅ Classification Report:\\n\")\n",
+    "print(classification_report(y, y_preds))\n",
+    "\n",
+    "print(\"✅ Confusion Matrix:\\n\")\n",
+    "print(confusion_matrix(y, y_preds))\n",
+    "\n",
+    "# Step 5: Save results to a CSV\n",
+    "output_df = pd.DataFrame({\n",
+    "    'y_true': y,\n",
+    "    'y_pred': y_preds\n",
+    "})\n",
+    "\n",
+    "output_df.to_csv('part-1.csv', index=False)\n",
+    "\n",
+    "print(\"\\n✅ part-1.csv saved successfully!\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79783f66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(2)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80fb05a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "major02",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

problem.pdf

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requirements.txt

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+accelerate==1.6.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.11.18
+aiosignal==1.3.2
+asttokens==3.0.0
+async-timeout==5.0.1
+attrs==25.3.0
+bitsandbytes==0.45.5
+certifi==2025.4.26
+charset-normalizer==3.4.1
+comm==0.2.2
+cut-cross-entropy==25.1.1
+datasets==3.5.0
+debugpy==1.8.14
+decorator==5.2.1
+dill==0.3.8
+exceptiongroup==1.2.2
+executing==2.2.0
+filelock==3.18.0
+frozenlist==1.6.0
+fsspec==2024.12.0
+hf_transfer==0.1.9
+huggingface-hub==0.30.2
+idna==3.10
+ipykernel==6.29.5
+ipython==8.36.0
+jedi==0.19.2
+Jinja2==3.1.6
+joblib==1.4.2
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+mpmath==1.3.0
+multidict==6.4.3
+multiprocess==0.70.16
+nest-asyncio==1.6.0
+networkx==3.4.2
+numpy==2.0.2
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cuda-cupti-cu12==12.6.80
+nvidia-cuda-nvrtc-cu12==12.6.77
+nvidia-cuda-runtime-cu12==12.6.77
+nvidia-cudnn-cu12==9.5.1.17
+nvidia-cufft-cu12==11.3.0.4
+nvidia-cufile-cu12==1.11.1.6
+nvidia-curand-cu12==10.3.7.77
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cusparse-cu12==12.5.4.2
+nvidia-cusparselt-cu12==0.6.3
+nvidia-nccl-cu12==2.26.2
+nvidia-nvjitlink-cu12==12.6.85
+nvidia-nvtx-cu12==12.6.77
+packaging==25.0
+pandas==2.2.3
+parso==0.8.4
+peft==0.15.2
+pexpect==4.9.0
+pillow==11.2.1
+platformdirs==4.3.7
+prompt_toolkit==3.0.51
+propcache==0.3.1
+protobuf==6.30.2
+psutil==7.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pyarrow==20.0.0
+Pygments==2.19.1
+python-dateutil==2.9.0.post0
+pytz==2025.2
+PyYAML==6.0.2
+pyzmq==26.4.0
+regex==2024.11.6
+requests==2.32.3
+rich==14.0.0
+safetensors==0.5.3
+scikit-learn==1.6.1
+scipy==1.15.2
+sentencepiece==0.2.0
+six==1.17.0
+stack-data==0.6.3
+sympy==1.14.0
+threadpoolctl==3.6.0
+tokenizers==0.21.1
+torch==2.7.0
+tornado==6.4.2
+tqdm==4.67.1
+traitlets==5.14.3
+transformers==4.51.3
+triton==3.3.0
+trl==0.15.2
+typing_extensions==4.13.2
+tzdata==2025.2
+unsloth==2025.4.1
+unsloth_zoo==2025.4.1
+urllib3==2.4.0
+wcwidth==0.2.13
+xformers==0.0.29.post3
+xxhash==3.5.0
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unsloth_compiled_cache/UnslothBCOTrainer.py

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+"""
+2025.4.1
+2025.4.1
+4.51.3
+0.15.2
+__UNSLOTH_VERSIONING__
+"""
+from torch import Tensor
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from trl.trainer.bco_trainer import (Any, AutoModelForCausalLM, BCOConfig, BCOTrainer, BaseImageProcessor, CLF_NAME, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, Literal, LogisticRegression, Optional, PartialState, PeftModel, PreTrainedModel, PreTrainedModelWrapper, PreTrainedTokenizerBase, ProcessorMixin, RUNNING_NAME, RunningMoments, SequentialSampler, Trainer, TrainerCallback, TrainingArguments, Union, _process_tokens, _tokenize, amp, contextmanager, create_reference_model, deepcopy, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, has_length, inspect, is_comet_available, is_peft_available, is_sklearn_available, is_wandb_available, itemgetter, log_table_to_comet_experiment, maybe_apply_chat_template, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_model_for_kbit_training, random, textwrap, torch, tqdm, transformers, version, warnings, F, Optional, PeftModel, PreTrainedModel, Trainer, is_peft_available, os, torch)
+
+
+import os
+from typing import *
+from dataclasses import dataclass, field
+from packaging.version import Version
+import torch
+import numpy as np
+from contextlib import nullcontext
+from torch.nn import functional as F
+from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
+
+torch_compile_options = {
+    "epilogue_fusion"   : True,
+    "max_autotune"      : False,
+    "shape_padding"     : True,
+    "trace.enabled"     : False,
+    "triton.cudagraphs" : False,
+}
+
+@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
+def selective_log_softmax(logits, index):
+    logits = logits.to(torch.float32)
+    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
+    # loop to reduce peak mem consumption
+    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
+    logsumexp_values = torch.logsumexp(logits, dim = -1)
+    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
+    return per_token_logps
+@dataclass
+class UnslothBCOConfig(BCOConfig):
+    """
+    
+    Configuration class for the [`BCOTrainer`].
+
+    Using [`~transformers.HfArgumentParser`] we can turn this class into
+    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
+    command line.
+
+    Parameters:
+        max_length (`int` or `None`, *optional*, defaults to `1024`):
+            Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want
+            to use the default data collator.
+        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
+            Maximum length of the prompt. This argument is required if you want to use the default data collator.
+        max_completion_length (`int` or `None`, *optional*, defaults to `None`):
+            Maximum length of the completion. This argument is required if you want to use the default data collator
+            and your model is an encoder-decoder.
+        beta (`float`, *optional*, defaults to `0.1`):
+            Parameter controlling the deviation from the reference model. Higher β means less deviation from the
+            reference model.
+        label_pad_token_id (`int`,  *optional*, defaults to `-100`):
+            Label pad token id. This argument is required if you want to use the default data collator.
+        padding_value (`int` or `None`, *optional*, defaults to `None`):
+            Padding value to use. If `None`, the padding value of the tokenizer is used.
+        truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
+            Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`.
+            This argument is required if you want to use the default data collator.
+        disable_dropout (`bool`, *optional*, defaults to `True`):
+            Whether to disable dropout in the model and reference model.
+        generate_during_eval (`bool`, *optional*, defaults to `False`):
+            If `True`, generates and logs completions from both the model and the reference model to W&B or Comet during
+            evaluation.
+        is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`):
+            When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument,
+            you need to specify if the model returned by the callable is an encoder-decoder model.
+        precompute_ref_log_probs (`bool`, *optional*, defaults to `False`):
+            Whether to precompute reference model log probabilities for training and evaluation datasets. This is
+            useful when training without the reference model to reduce the total GPU memory needed.
+        model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
+            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
+            string.
+        ref_model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
+            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the reference model
+            from a string.
+        dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
+            Number of processes to use for processing the dataset.
+        prompt_sample_size (`int`, *optional*, defaults to `1024`):
+            Number of prompts that are fed to density ratio classifier.
+        min_density_ratio (`float`, *optional*, defaults to `0.5`):
+            Minimum value of the density ratio. The estimated density ratio is clamped to this value.
+        max_density_ratio (`float`, *optional*, defaults to `10.0`):
+            Maximum value of the density ratio. The estimated density ratio is clamped to this value.
+    
+    """
+    vllm_sampling_params: Optional[Any] = field(
+        default = None,
+        metadata = {'help': 'vLLM SamplingParams'},
+    )
+    unsloth_num_chunks : Optional[int] = field(
+        default = -1,
+        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
+    )
+    def __init__(
+        self,
+        output_dir = None,
+        overwrite_output_dir = None,
+        do_train = False,
+        do_eval = False,
+        do_predict = False,
+        eval_strategy = 'no',
+        prediction_loss_only = False,
+        per_device_train_batch_size = 4,
+        per_device_eval_batch_size = 4,
+        per_gpu_train_batch_size = None,
+        per_gpu_eval_batch_size = None,
+        gradient_accumulation_steps = 2,
+        eval_accumulation_steps = 2,
+        eval_delay = 0,
+        torch_empty_cache_steps = 250,
+        learning_rate = 5e-05,
+        weight_decay = 0.01,
+        adam_beta1 = 0.9,
+        adam_beta2 = 0.999,
+        adam_epsilon = 1e-08,
+        max_grad_norm = 1.0,
+        num_train_epochs = 3.0,
+        max_steps = -1,
+        lr_scheduler_type = 'linear',
+        warmup_ratio = 0.1,
+        warmup_steps = 0,
+        log_level = 'passive',
+        log_level_replica = 'warning',
+        log_on_each_node = True,
+        logging_dir = None,
+        logging_strategy = 'steps',
+        logging_first_step = False,
+        logging_steps = 1,
+        logging_nan_inf_filter = False,
+        save_strategy = 'steps',
+        save_steps = 500,
+        save_total_limit = None,
+        save_safetensors = True,
+        save_on_each_node = False,
+        save_only_model = False,
+        restore_callback_states_from_checkpoint = False,
+        no_cuda = False,
+        use_cpu = False,
+        use_mps_device = False,
+        seed = 3407,
+        data_seed = 3407,
+        jit_mode_eval = False,
+        use_ipex = False,
+        bf16 = False,
+        fp16 = False,
+        fp16_opt_level = 'O1',
+        half_precision_backend = 'auto',
+        bf16_full_eval = False,
+        fp16_full_eval = False,
+        tf32 = None,
+        local_rank = -1,
+        ddp_backend = None,
+        tpu_num_cores = None,
+        tpu_metrics_debug = False,
+        debug = '',
+        dataloader_drop_last = False,
+        eval_steps = None,
+        dataloader_num_workers = 0,
+        dataloader_prefetch_factor = None,
+        past_index = -1,
+        run_name = None,
+        disable_tqdm = None,
+        remove_unused_columns = True,
+        label_names = None,
+        load_best_model_at_end = False,
+        metric_for_best_model = None,
+        greater_is_better = None,
+        ignore_data_skip = False,
+        fsdp = '',
+        fsdp_min_num_params = 0,
+        fsdp_config = None,
+        tp_size = 0,
+        fsdp_transformer_layer_cls_to_wrap = None,
+        accelerator_config = None,
+        deepspeed = None,
+        label_smoothing_factor = 0.0,
+        optim = 'adamw_8bit',
+        optim_args = None,
+        adafactor = False,
+        group_by_length = False,
+        length_column_name = 'length',
+        report_to = None,
+        ddp_find_unused_parameters = None,
+        ddp_bucket_cap_mb = None,
+        ddp_broadcast_buffers = None,
+        dataloader_pin_memory = True,
+        dataloader_persistent_workers = False,
+        skip_memory_metrics = True,
+        use_legacy_prediction_loop = False,
+        push_to_hub = False,
+        resume_from_checkpoint = None,
+        hub_model_id = None,
+        hub_strategy = 'every_save',
+        hub_token = None,
+        hub_private_repo = None,
+        hub_always_push = False,
+        gradient_checkpointing = False,
+        gradient_checkpointing_kwargs = None,
+        include_inputs_for_metrics = False,
+        eval_do_concat_batches = True,
+        fp16_backend = 'auto',
+        push_to_hub_model_id = None,
+        push_to_hub_organization = None,
+        push_to_hub_token = None,
+        mp_parameters = '',
+        auto_find_batch_size = False,
+        full_determinism = False,
+        torchdynamo = None,
+        ray_scope = 'last',
+        ddp_timeout = 1800,
+        torch_compile = False,
+        torch_compile_backend = None,
+        torch_compile_mode = None,
+        include_tokens_per_second = False,
+        include_num_input_tokens_seen = False,
+        neftune_noise_alpha = None,
+        optim_target_modules = None,
+        batch_eval_metrics = False,
+        eval_on_start = False,
+        use_liger_kernel = False,
+        eval_use_gather_object = False,
+        average_tokens_across_devices = False,
+        max_length = 1024,
+        max_prompt_length = 512,
+        max_completion_length = None,
+        beta = 0.1,
+        label_pad_token_id = -100,
+        padding_value = None,
+        truncation_mode = 'keep_end',
+        disable_dropout = True,
+        generate_during_eval = False,
+        is_encoder_decoder = None,
+        precompute_ref_log_probs = False,
+        model_init_kwargs = None,
+        ref_model_init_kwargs = None,
+        dataset_num_proc = None,
+        prompt_sample_size = 1024,
+        min_density_ratio = 0.5,
+        max_density_ratio = 10.0,
+        vllm_sampling_params = None,
+        unsloth_num_chunks = -1,
+        **kwargs,
+    ):
+        if learning_rate < 1e-7: raise FloatingPointError(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
+        if learning_rate > 1: raise OverflowError(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
+        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
+            output_dir = 'unsloth_training_checkpoints'
+            save_strategy = 'no'
+        if dataset_num_proc is None:
+            from multiprocessing import cpu_count
+            dataset_num_proc = cpu_count()
+        
+        super().__init__(
+            output_dir = output_dir,
+            overwrite_output_dir = overwrite_output_dir,
+            do_train = do_train,
+            do_eval = do_eval,
+            do_predict = do_predict,
+            eval_strategy = eval_strategy,
+            prediction_loss_only = prediction_loss_only,
+            per_device_train_batch_size = per_device_train_batch_size,
+            per_device_eval_batch_size = per_device_eval_batch_size,
+            per_gpu_train_batch_size = per_gpu_train_batch_size,
+            per_gpu_eval_batch_size = per_gpu_eval_batch_size,
+            gradient_accumulation_steps = gradient_accumulation_steps,
+            eval_accumulation_steps = eval_accumulation_steps,
+            eval_delay = eval_delay,
+            torch_empty_cache_steps = torch_empty_cache_steps,
+            learning_rate = learning_rate,
+            weight_decay = weight_decay,
+            adam_beta1 = adam_beta1,
+            adam_beta2 = adam_beta2,
+            adam_epsilon = adam_epsilon,
+            max_grad_norm = max_grad_norm,
+            num_train_epochs = num_train_epochs,
+            max_steps = max_steps,
+            lr_scheduler_type = lr_scheduler_type,
+            warmup_ratio = warmup_ratio,
+            warmup_steps = warmup_steps,
+            log_level = log_level,
+            log_level_replica = log_level_replica,
+            log_on_each_node = log_on_each_node,
+            logging_dir = logging_dir,
+            logging_strategy = logging_strategy,
+            logging_first_step = logging_first_step,
+            logging_steps = logging_steps,
+            logging_nan_inf_filter = logging_nan_inf_filter,
+            save_strategy = save_strategy,
+            save_steps = save_steps,
+            save_total_limit = save_total_limit,
+            save_safetensors = save_safetensors,
+            save_on_each_node = save_on_each_node,
+            save_only_model = save_only_model,
+            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
+            no_cuda = no_cuda,
+            use_cpu = use_cpu,
+            use_mps_device = use_mps_device,
+            seed = seed,
+            data_seed = data_seed,
+            jit_mode_eval = jit_mode_eval,
+            use_ipex = use_ipex,
+            bf16 = bf16,
+            fp16 = fp16,
+            fp16_opt_level = fp16_opt_level,
+            half_precision_backend = half_precision_backend,
+            bf16_full_eval = bf16_full_eval,
+            fp16_full_eval = fp16_full_eval,
+            tf32 = tf32,
+            local_rank = local_rank,
+            ddp_backend = ddp_backend,
+            tpu_num_cores = tpu_num_cores,
+            tpu_metrics_debug = tpu_metrics_debug,
+            debug = debug,
+            dataloader_drop_last = dataloader_drop_last,
+            eval_steps = eval_steps,
+            dataloader_num_workers = dataloader_num_workers,
+            dataloader_prefetch_factor = dataloader_prefetch_factor,
+            past_index = past_index,
+            run_name = run_name,
+            disable_tqdm = disable_tqdm,
+            remove_unused_columns = remove_unused_columns,
+            label_names = label_names,
+            load_best_model_at_end = load_best_model_at_end,
+            metric_for_best_model = metric_for_best_model,
+            greater_is_better = greater_is_better,
+            ignore_data_skip = ignore_data_skip,
+            fsdp = fsdp,
+            fsdp_min_num_params = fsdp_min_num_params,
+            fsdp_config = fsdp_config,
+            tp_size = tp_size,
+            fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
+            accelerator_config = accelerator_config,
+            deepspeed = deepspeed,
+            label_smoothing_factor = label_smoothing_factor,
+            optim = optim,
+            optim_args = optim_args,
+            adafactor = adafactor,
+            group_by_length = group_by_length,
+            length_column_name = length_column_name,
+            report_to = report_to,
+            ddp_find_unused_parameters = ddp_find_unused_parameters,
+            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
+            ddp_broadcast_buffers = ddp_broadcast_buffers,
+            dataloader_pin_memory = dataloader_pin_memory,
+            dataloader_persistent_workers = dataloader_persistent_workers,
+            skip_memory_metrics = skip_memory_metrics,
+            use_legacy_prediction_loop = use_legacy_prediction_loop,
+            push_to_hub = push_to_hub,
+            resume_from_checkpoint = resume_from_checkpoint,
+            hub_model_id = hub_model_id,
+            hub_strategy = hub_strategy,
+            hub_token = hub_token,
+            hub_private_repo = hub_private_repo,
+            hub_always_push = hub_always_push,
+            gradient_checkpointing = gradient_checkpointing,
+            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
+            include_inputs_for_metrics = include_inputs_for_metrics,
+            eval_do_concat_batches = eval_do_concat_batches,
+            fp16_backend = fp16_backend,
+            push_to_hub_model_id = push_to_hub_model_id,
+            push_to_hub_organization = push_to_hub_organization,
+            push_to_hub_token = push_to_hub_token,
+            mp_parameters = mp_parameters,
+            auto_find_batch_size = auto_find_batch_size,
+            full_determinism = full_determinism,
+            torchdynamo = torchdynamo,
+            ray_scope = ray_scope,
+            ddp_timeout = ddp_timeout,
+            torch_compile = torch_compile,
+            torch_compile_backend = torch_compile_backend,
+            torch_compile_mode = torch_compile_mode,
+            include_tokens_per_second = include_tokens_per_second,
+            include_num_input_tokens_seen = include_num_input_tokens_seen,
+            neftune_noise_alpha = neftune_noise_alpha,
+            optim_target_modules = optim_target_modules,
+            batch_eval_metrics = batch_eval_metrics,
+            eval_on_start = eval_on_start,
+            use_liger_kernel = use_liger_kernel,
+            eval_use_gather_object = eval_use_gather_object,
+            average_tokens_across_devices = average_tokens_across_devices,
+            max_length = max_length,
+            max_prompt_length = max_prompt_length,
+            max_completion_length = max_completion_length,
+            beta = beta,
+            label_pad_token_id = label_pad_token_id,
+            padding_value = padding_value,
+            truncation_mode = truncation_mode,
+            disable_dropout = disable_dropout,
+            generate_during_eval = generate_during_eval,
+            is_encoder_decoder = is_encoder_decoder,
+            precompute_ref_log_probs = precompute_ref_log_probs,
+            model_init_kwargs = model_init_kwargs,
+            ref_model_init_kwargs = ref_model_init_kwargs,
+            dataset_num_proc = dataset_num_proc,
+            prompt_sample_size = prompt_sample_size,
+            min_density_ratio = min_density_ratio,
+            max_density_ratio = max_density_ratio,**kwargs)
+        self.vllm_sampling_params = vllm_sampling_params
+        self.unsloth_num_chunks = unsloth_num_chunks
+pass
+
+class _UnslothBCOTrainer(Trainer):
+    r""""""
+
+    _tag_names = ["trl", "bco"]
+
+    def __init__(
+        self,
+        model: Union[PreTrainedModel, nn.Module, str] = None,
+        ref_model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
+        args: BCOConfig = None,
+        train_dataset: Optional[Dataset] = None,
+        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
+        processing_class: Optional[
+            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
+        ] = None,
+        data_collator: Optional[DataCollator] = None,
+        model_init: Optional[Callable[[], PreTrainedModel]] = None,
+        callbacks: Optional[list[TrainerCallback]] = None,
+        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
+        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+        peft_config: Optional[dict] = None,
+        compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None,
+        model_adapter_name: Optional[str] = None,
+        ref_adapter_name: Optional[str] = None,
+        embedding_func: Optional[Callable] = None,
+        embedding_tokenizer: Optional[PreTrainedTokenizerBase] = None,
+    ):
+        if not is_sklearn_available():
+            raise ImportError(
+                "BCOTrainer requires the scikit-learn library. Please install it with `pip install scikit-learn`."
+            )
+
+        if type(args) is TrainingArguments:
+            raise ValueError("Please use `BCOConfig` instead `TrainingArguments`.")
+
+        if not isinstance(model, str) and ref_model is model:
+            raise ValueError(
+                "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the "
+                "same as `model`, you must mass a copy of it, or `None` if you use peft."
+            )
+
+        if args.model_init_kwargs is None:
+            model_init_kwargs = {}
+        elif not isinstance(model, str):
+            raise ValueError("You passed model_kwargs to the BCOTrainer. But your model is already instantiated.")
+        else:
+            model_init_kwargs = args.model_init_kwargs
+            torch_dtype = model_init_kwargs.get("torch_dtype")
+            if torch_dtype is not None:
+                # Convert to `torch.dtype` if an str is passed
+                if isinstance(torch_dtype, str) and torch_dtype != "auto":
+                    torch_dtype = getattr(torch, torch_dtype)
+                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
+                    raise ValueError(
+                        f"Invalid `torch_dtype` passed to the BCOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
+                    )
+                model_init_kwargs["torch_dtype"] = torch_dtype
+
+        if args.ref_model_init_kwargs is None:
+            ref_model_init_kwargs = {}
+        elif not isinstance(ref_model, str):
+            raise ValueError(
+                "You passed ref_model_kwargs to the BCOTrainer. But your ref_model is already instantiated."
+            )
+        else:
+            ref_model_init_kwargs = args.ref_model_init_kwargs
+            torch_dtype = ref_model_init_kwargs.get("torch_dtype")
+            if torch_dtype is not None:
+                # Convert to `torch.dtype` if an str is passed
+                if isinstance(torch_dtype, str) and torch_dtype != "auto":
+                    torch_dtype = getattr(torch, torch_dtype)
+                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
+                    raise ValueError(
+                        f"Invalid `torch_dtype` passed to the BCOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
+                    )
+                ref_model_init_kwargs["torch_dtype"] = torch_dtype
+
+        if isinstance(model, str):
+            model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)
+
+        if isinstance(ref_model, str):
+            ref_model = AutoModelForCausalLM.from_pretrained(ref_model, **ref_model_init_kwargs)
+
+        # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16`
+        # has been called in order to properly call autocast if needed.
+        self._peft_has_been_casted_to_bf16 = False
+
+        if not is_peft_available() and peft_config is not None:
+            raise ValueError(
+                "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it with `pip install peft` to use the PEFT models"
+            )
+        elif is_peft_available() and peft_config is not None:
+            # if model is a peft model and we have a peft_config, we merge and unload it first
+            if isinstance(model, PeftModel):
+                model = model.merge_and_unload()
+
+            if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
+                _support_gc_kwargs = hasattr(
+                    args, "gradient_checkpointing_kwargs"
+                ) and "gradient_checkpointing_kwargs" in list(
+                    inspect.signature(prepare_model_for_kbit_training).parameters
+                )
+
+                prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
+
+                if _support_gc_kwargs:
+                    prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
+
+                model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)
+            elif getattr(args, "gradient_checkpointing", False):
+                # For backward compatibility with older versions of transformers
+                if hasattr(model, "enable_input_require_grads"):
+                    model.enable_input_require_grads()
+                else:
+
+                    def make_inputs_require_grad(module, input, output):
+                        output.requires_grad_(True)
+
+                    model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+            # get peft model with the given config
+            model = model
+            if args.bf16 and getattr(model, "is_loaded_in_4bit", False):
+                peft_module_casting_to_bf16(model)
+                # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager
+                self._peft_has_been_casted_to_bf16 = True
+
+        # For models that use gradient_checkpointing, we need to attach a hook that enables input
+        # to explicitly have `requires_grad=True`, otherwise training will either silently
+        # fail or completely fail.
+        elif getattr(args, "gradient_checkpointing", False):
+            # For backward compatibility with older versions of transformers
+            if hasattr(model, "enable_input_require_grads"):
+                model.enable_input_require_grads()
+            else:
+
+                def make_inputs_require_grad(module, input, output):
+                    output.requires_grad_(True)
+
+                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+        if args.generate_during_eval and not (is_wandb_available() or is_comet_available()):
+            raise ValueError(
+                "`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
+                " Please install `wandb` or `comet-ml` to resolve."
+            )
+
+        if model is not None:
+            self.is_encoder_decoder = model.config.is_encoder_decoder
+        elif args.is_encoder_decoder is None:
+            raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.")
+        else:
+            self.is_encoder_decoder = args.is_encoder_decoder
+
+        self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)
+        self.model_adapter_name = model_adapter_name
+        self.ref_adapter_name = ref_adapter_name
+
+        if ref_model:
+            self.ref_model = ref_model
+        elif self.is_peft_model or args.precompute_ref_log_probs:
+            # The `model` with adapters turned off will be used as the reference model
+            self.ref_model = None
+        else:
+            self.ref_model = create_reference_model(model)
+
+        if processing_class is None:
+            raise ValueError(
+                "max_length or a processing_class must be specified when using the default DPODataCollatorWithPadding"
+            )
+        if args.max_length is None:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding, you should set `max_length` in the `BCOConfig`. "
+                "It will be set to `512` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_length = 512
+        if args.max_length is not None:
+            max_length = args.max_length
+
+        if args.max_prompt_length is None:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the `BCOConfig`. "
+                "It will be set to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_prompt_length = 128
+        if args.max_prompt_length is not None:
+            max_prompt_length = args.max_prompt_length
+
+        max_completion_length = None
+        if args.max_completion_length is None and self.is_encoder_decoder:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding with an encoder decoder architecture, you should set `max_completion_length` in the BCOTrainer's init"
+                " it will be set to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_completion_length = 128
+        if args.max_completion_length is not None and self.is_encoder_decoder:
+            max_completion_length = args.max_completion_length
+
+        if data_collator is None:
+            data_collator = DPODataCollatorWithPadding(
+                pad_token_id=processing_class.pad_token_id,
+                label_pad_token_id=args.label_pad_token_id,
+                is_encoder_decoder=self.is_encoder_decoder,
+            )
+
+            if args.remove_unused_columns:
+                args.remove_unused_columns = False
+                # warn users
+                warnings.warn(
+                    "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your BCOConfig"
+                    " we have set it for you, but you should do it yourself in the future.",
+                    UserWarning,
+                )
+
+            self.use_dpo_data_collator = True
+        else:
+            self.use_dpo_data_collator = False
+
+        # Disable dropout in the model and reference model
+        if args.disable_dropout:
+            disable_dropout_in_model(model)
+            if self.ref_model is not None:
+                disable_dropout_in_model(self.ref_model)
+
+        self.max_length = max_length
+        self.generate_during_eval = args.generate_during_eval
+        self.label_pad_token_id = args.label_pad_token_id
+        self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id
+        self.max_prompt_length = max_prompt_length
+        self.truncation_mode = args.truncation_mode
+        self.max_completion_length = max_completion_length
+        self.precompute_ref_log_probs = args.precompute_ref_log_probs
+
+        # Since ref_logs are precomputed on the first call to get_train/eval_dataloader
+        # keep track of first called to avoid computation of future calls
+        self._precomputed_train_ref_log_probs = False
+        self._precomputed_eval_ref_log_probs = False
+
+        # metric
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+
+        # BCO parameter
+        self.beta = args.beta
+        self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)
+        self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0)
+        if self.aux_loss_enabled and self.aux_loss_coef == 0.0:
+            warnings.warn(
+                "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to "
+                "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value "
+                "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary "
+                "loss.",
+                UserWarning,
+            )
+
+        # Underlying Distribution Matching argument
+        self.embedding_func = embedding_func
+        self.embedding_tokenizer = embedding_tokenizer
+
+        # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
+        # input tensor associated with the key "input_ids". However, in BCO, the sampled data does not include the
+        # "input_ids" key. Instead, the available keys are "prompt_input_ids" and "completion_input_ids". As a result,
+        # the trainer issues the warning: "Could not estimate the number of tokens of the input, floating-point
+        # operations will not be computed." To suppress this warning, we set the "estimate_tokens" key in the model's
+        # "warnings_issued" dictionary to True. This acts as a flag to indicate that the warning has already been
+        # issued.
+        model.warnings_issued["estimate_tokens"] = True
+
+        with PartialState().local_main_process_first():
+            # Apply the chat template if needed
+            train_dataset = train_dataset.map(
+                maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}, num_proc=args.dataset_num_proc
+            )
+            if eval_dataset is not None:
+                eval_dataset = eval_dataset.map(
+                    maybe_apply_chat_template,
+                    fn_kwargs={"tokenizer": processing_class},
+                    num_proc=args.dataset_num_proc,
+                )
+            # Shuffle the datasets
+            train_dataset = train_dataset.shuffle(seed=args.data_seed)
+            if eval_dataset is not None:
+                eval_dataset = eval_dataset.shuffle(seed=args.data_seed)
+            # Tokenize and prepare the training datasets
+            train_dataset = train_dataset.map(
+                _tokenize,
+                batched=True,
+                fn_kwargs={"tokenizer": processing_class, "embedding_tokenizer": self.embedding_tokenizer},
+                num_proc=args.dataset_num_proc,
+                desc="Tokenizing train dataset",
+            )
+
+            # Prepare the datasets
+            fn_kwargs = {
+                "prefix": "",
+                "is_encoder_decoder": self.is_encoder_decoder,
+                "tokenizer": processing_class,
+                "max_length": self.max_length,
+                "truncation_mode": self.truncation_mode,
+                "label_pad_token_id": self.label_pad_token_id,
+                "max_prompt_length": self.max_prompt_length,
+                "max_completion_length": self.max_completion_length,
+            }
+            train_dataset = train_dataset.map(
+                _process_tokens,
+                fn_kwargs=fn_kwargs,
+                num_proc=args.dataset_num_proc,
+                desc="Processing tokenized train dataset",
+            )
+
+            if eval_dataset is not None:
+                # Tokenize
+                eval_dataset = eval_dataset.map(
+                    _tokenize,
+                    fn_kwargs={"tokenizer": processing_class, "embedding_tokenizer": self.embedding_tokenizer},
+                    batched=True,
+                    num_proc=args.dataset_num_proc,
+                    desc="Tokenizing eval dataset",
+                )
+
+                # Process
+                fn_kwargs = {
+                    "prefix": "",
+                    "is_encoder_decoder": self.is_encoder_decoder,
+                    "tokenizer": processing_class,
+                    "max_length": self.max_length,
+                    "truncation_mode": self.truncation_mode,
+                    "label_pad_token_id": self.label_pad_token_id,
+                    "max_prompt_length": self.max_prompt_length,
+                    "max_completion_length": self.max_completion_length,
+                }
+                eval_dataset = eval_dataset.map(
+                    _process_tokens,
+                    fn_kwargs=fn_kwargs,
+                    num_proc=args.dataset_num_proc,
+                    desc="Processing tokenized eval dataset",
+                )
+
+            desirable = train_dataset.filter(
+                lambda x: x["label"], num_proc=args.dataset_num_proc, desc="Filtering desirable examples"
+            )
+            undesirable = train_dataset.filter(
+                lambda x: not x["label"], num_proc=args.dataset_num_proc, desc="Filtering undesirable examples"
+            )
+
+            desirable = desirable.shuffle(seed=args.data_seed)
+            undesirable = undesirable.shuffle(seed=args.data_seed)
+
+        super().__init__(
+            model=model,
+            args=args,
+            data_collator=data_collator,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            processing_class=processing_class,
+            model_init=model_init,
+            compute_metrics=compute_metrics,
+            callbacks=callbacks,
+            optimizers=optimizers,
+            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+        )
+
+        # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the
+        # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set
+        # self.model_accepts_loss_kwargs to False to enable scaling.
+        self.model_accepts_loss_kwargs = False
+
+        # Add tags for models that have been loaded with the correct transformers version
+        if hasattr(self.model, "add_model_tags"):
+            self.model.add_model_tags(self._tag_names)
+
+        if not hasattr(self, "accelerator"):
+            raise AttributeError(
+                "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`."
+            )
+
+        # Deepspeed Zero-3 does not support precompute_ref_log_probs
+        if self.is_deepspeed_enabled:
+            if self.accelerator.state.deepspeed_plugin.zero_stage == 3 and self.precompute_ref_log_probs:
+                raise ValueError(
+                    "You cannot use `precompute_ref_log_probs=True` with Deepspeed ZeRO-3. Please set `precompute_ref_log_probs=False`."
+                )
+
+        if self.ref_model is None:
+            if not (self.is_peft_model or self.precompute_ref_log_probs):
+                raise ValueError(
+                    "No reference model and model is not a Peft model. Try setting `precompute_ref_log_probs=True`"
+                )
+        else:
+            if self.is_deepspeed_enabled:
+                self.ref_model = self._prepare_deepspeed(self.ref_model)
+            else:
+                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
+
+        self.running = RunningMoments(accelerator=self.accelerator)
+
+        if self.embedding_func is None:
+            return
+
+        chosen_embeddings = self._get_sample_prompt_embeddings(desirable, sample_size=self.args.prompt_sample_size)
+        rejected_embeddings = self._get_sample_prompt_embeddings(undesirable, sample_size=self.args.prompt_sample_size)
+
+        embeddings = torch.cat((chosen_embeddings, rejected_embeddings), dim=0)
+        labels = torch.cat(
+            (torch.ones_like(chosen_embeddings[:, 0]), torch.zeros_like(rejected_embeddings[:, 0])), dim=0
+        )
+
+        self.clf = LogisticRegression(class_weight="balanced").fit(
+            embeddings.cpu().float().numpy(), labels.cpu().numpy()
+        )
+
+    @property
+    def match_underlying_distribution(self):
+        return self.embedding_func is not None and self.embedding_tokenizer is not None
+
+    def _get_chosen_prob(self, prompt_embeddings: torch.FloatTensor) -> torch.FloatTensor:
+        """
+        Calculates the probability if the given prompt embedding is from desirable dataset.
+        This function calculates the probability in the process and ensemble across processes.
+        """
+        dtype = prompt_embeddings.dtype
+        device = prompt_embeddings.device
+        rank = self.accelerator.process_index
+
+        padded_prompt_embeddings = self.accelerator.pad_across_processes(
+            prompt_embeddings, pad_index=self.embedding_tokenizer.pad_token_id
+        )
+        sample_size = padded_prompt_embeddings.shape[0]
+        nonzero = padded_prompt_embeddings.mean(dim=1) != self.embedding_tokenizer.pad_token_id
+        prompt_embeddings = self.accelerator.gather(padded_prompt_embeddings)
+
+        # cannot predict for all empty values
+        if prompt_embeddings.shape[0] == 0:
+            return torch.tensor([], device=device, dtype=dtype)
+
+        prob = self.clf.predict_proba(prompt_embeddings.cpu().float().numpy())[:, 1]
+        prob = torch.as_tensor(prob, dtype=dtype, device=device)
+        prob = self.accelerator.reduce(prob, reduction="mean")
+
+        prob = prob[sample_size * rank : sample_size * (rank + 1)]
+        prob = prob[nonzero]
+
+        return prob
+
+    def _vectorize_prompt(self, input_ids: torch.LongTensor, attention_mask: torch.LongTensor) -> torch.FloatTensor:
+        """
+        Replaces processing_class.pad_token_id to embedding_tokenizer.pad_token_id
+        and applies self.embedding_func
+        """
+        input_ids = torch.where(
+            input_ids == self.processing_class.pad_token_id,
+            self.embedding_tokenizer.pad_token_id,
+            input_ids,
+        )
+
+        with torch.no_grad():
+            embeddings = self.embedding_func(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+            )
+
+        return embeddings
+
+    def _get_prompt_embeddings(
+        self, batch: dict[str, Union[list, torch.LongTensor]]
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
+        """Extract embeddings from frozen embedding model"""
+
+        if not self.match_underlying_distribution:
+            return None, None
+
+        embeddings = self._vectorize_prompt(
+            input_ids=batch["embedding_input_ids"],
+            attention_mask=batch["embedding_attention_mask"],
+        )
+
+        chosen_idx = [i for i in range(len(batch["label"])) if batch["label"][i] is True]
+        rejected_idx = [i for i in range(len(batch["label"])) if batch["label"][i] is False]
+
+        chosen_embeddings = embeddings[chosen_idx, ...]
+        rejected_embeddings = embeddings[rejected_idx, ...]
+
+        return (chosen_embeddings, rejected_embeddings)
+
+    def _get_sample_prompt_embeddings(self, dataset: Dataset, sample_size: int = 512) -> torch.FloatTensor:
+        """
+        Sample instances from dataset and get prompt embeddings.
+        Used for density ratio classifier training.
+        """
+        n_samples = min(len(dataset), sample_size)
+        rand_indices = np.random.choice(len(dataset), size=(n_samples,))
+
+        embedding_dataset = dataset.select(rand_indices)
+
+        dataloader_params = {
+            "batch_size": self.args.per_device_train_batch_size,
+            "collate_fn": self.data_collator,
+            "num_workers": self.args.dataloader_num_workers,
+            "pin_memory": self.args.dataloader_pin_memory,
+            "shuffle": False,
+        }
+
+        # prepare dataloader
+        data_loader = self.accelerator.prepare(DataLoader(embedding_dataset, **dataloader_params))
+
+        with torch.no_grad():
+            all_embeddings = torch.empty(0)
+            for padded_batch in tqdm(iterable=data_loader, desc="Building sample prompt embeddings"):
+                embeddings = self._vectorize_prompt(
+                    input_ids=padded_batch["embedding_input_ids"],
+                    attention_mask=padded_batch["embedding_attention_mask"],
+                )
+                embeddings = self.accelerator.gather_for_metrics(embeddings)
+                all_embeddings = torch.cat((all_embeddings, embeddings.cpu()))
+
+        return all_embeddings
+
+    def _prepare_deepspeed(self, model: PreTrainedModelWrapper):
+        # Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
+        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
+        config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config)
+
+        if model is not None:
+            if hasattr(model, "config"):
+                hidden_size = (
+                    max(model.config.hidden_sizes)
+                    if getattr(model.config, "hidden_sizes", None)
+                    else getattr(model.config, "hidden_size", None)
+                )
+                if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3:
+                    # Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0`
+                    # This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081
+                    config_kwargs.update(
+                        {
+                            "zero_optimization.reduce_bucket_size": hidden_size * hidden_size,
+                            "zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size,
+                            "zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size,
+                        }
+                    )
+
+        # If ZeRO-3 is used, we shard both the active and reference model.
+        # Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0)
+        if config_kwargs["zero_optimization"]["stage"] != 3:
+            config_kwargs["zero_optimization"]["stage"] = 0
+        model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
+        model.eval()
+        return model
+
+    def _save_optimizer_and_scheduler(self, output_dir):
+        super()._save_optimizer_and_scheduler(output_dir)
+
+        # When saving optimizer and scheduler to checkpoint, save also the running delta object.
+        output_dir = output_dir if output_dir is not None else self.args.output_dir
+
+        self.running.save_to_json(os.path.join(output_dir, RUNNING_NAME))
+
+        if self.match_underlying_distribution:
+            torch.save(self.clf.get_params(), os.path.join(output_dir, CLF_NAME))
+
+    def _load_optimizer_and_scheduler(self, checkpoint):
+        super()._load_optimizer_and_scheduler(checkpoint)
+
+        if checkpoint is None:
+            return
+        # when loading optimizer and scheduler from checkpoint, also load the running delta object.
+        running_file = os.path.join(checkpoint, RUNNING_NAME)
+        if os.path.isfile(running_file):
+            self.running = RunningMoments.load_from_json(self.accelerator, running_file)
+
+        if self.match_underlying_distribution:
+            clf_file = os.path.join(checkpoint, CLF_NAME)
+            if os.path.isfile(running_file):
+                self.clf.set_params(**torch.load(clf_file, weights_only=True, map_location="cpu"))
+
+    @contextmanager
+    def null_ref_context(self):
+        """Context manager for handling null reference model (that is, peft adapter manipulation)."""
+        with (
+            self.accelerator.unwrap_model(self.model).disable_adapter()
+            if self.is_peft_model and not self.ref_adapter_name
+            else nullcontext()
+        ):
+            if self.ref_adapter_name:
+                self.model.set_adapter(self.ref_adapter_name)
+            yield
+            if self.ref_adapter_name:
+                self.model.set_adapter(self.model_adapter_name or "default")
+
+    def get_train_dataloader(self) -> DataLoader:
+        """
+        Returns the training [`~torch.utils.data.DataLoader`].
+
+        Subclass of transformers.src.transformers.trainer.get_train_dataloader to precompute `ref_log_probs`.
+        """
+
+        if self.precompute_ref_log_probs and not self._precomputed_train_ref_log_probs:
+            dataloader_params = {
+                "batch_size": self.args.per_device_train_batch_size,
+                "collate_fn": self.data_collator,
+                "num_workers": self.args.dataloader_num_workers,
+                "pin_memory": self.args.dataloader_pin_memory,
+                "shuffle": False,
+            }
+
+            # prepare dataloader
+            data_loader = self.accelerator.prepare(DataLoader(self.train_dataset, **dataloader_params))
+            reference_completion_logps = []
+
+            for padded_batch in tqdm(iterable=data_loader, desc="Train dataset reference log probs"):
+                reference_completion_logp = self.compute_reference_log_probs(padded_batch)
+
+                reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp)
+                reference_completion_logps.append(reference_completion_logp.cpu())
+
+            self.train_dataset = self.train_dataset.add_column(
+                name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy()
+            )
+
+            self._precomputed_train_ref_log_probs = True
+
+        return super().get_train_dataloader()
+
+    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
+        """
+        Returns the evaluation [`~torch.utils.data.DataLoader`].
+
+        Subclass of transformers.src.transformers.trainer.get_eval_dataloader to precompute `ref_log_probs`.
+
+        Args:
+            eval_dataset (`torch.utils.data.Dataset`, *optional*):
+                If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted
+                by the `model.forward()` method are automatically removed. It must implement `__len__`.
+        """
+        if eval_dataset is None and self.eval_dataset is None:
+            raise ValueError("Trainer: evaluation requires an eval_dataset.")
+        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
+
+        if self.precompute_ref_log_probs and not self._precomputed_eval_ref_log_probs:
+            dataloader_params = {
+                "batch_size": self.args.per_device_eval_batch_size,
+                "collate_fn": self.data_collator,
+                "num_workers": self.args.dataloader_num_workers,
+                "pin_memory": self.args.dataloader_pin_memory,
+                "shuffle": False,
+            }
+
+            # prepare dataloader
+            data_loader = self.accelerator.prepare(DataLoader(eval_dataset, **dataloader_params))
+
+            reference_completion_logps = []
+
+            for padded_batch in tqdm(iterable=data_loader, desc="Eval dataset reference log probs"):
+                reference_completion_logp = self.compute_reference_log_probs(padded_batch)
+
+                reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp)
+                reference_completion_logps.append(reference_completion_logp.cpu())
+
+            eval_dataset = eval_dataset.add_column(
+                name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy()
+            )
+
+            # Save calculated reference_chosen_logps and reference_rejected_logps to the eval_dataset for subsequent runs
+            if self.eval_dataset is not None:
+                self.eval_dataset = eval_dataset
+            self._precomputed_eval_ref_log_probs = True
+
+        return super().get_eval_dataloader(eval_dataset=eval_dataset)
+
+    def compute_reference_log_probs(self, padded_batch: dict) -> dict:
+        """Computes log probabilities of the reference model for a single padded batch of a BCO specific dataset."""
+        with torch.no_grad():
+            if self.ref_model is None:
+                with self.null_ref_context():
+                    if self.is_encoder_decoder:
+                        completion_logits = self.model(
+                            padded_batch["prompt_input_ids"],
+                            attention_mask=padded_batch["prompt_attention_mask"],
+                            decoder_input_ids=padded_batch.get("completion_decoder_input_ids"),
+                            labels=padded_batch["completion_labels"],
+                        ).logits
+
+                    else:
+                        completion_logits = self.model(
+                            padded_batch["completion_input_ids"],
+                            attention_mask=padded_batch["completion_attention_mask"],
+                        ).logits
+
+            else:
+                if self.is_encoder_decoder:
+                    completion_logits = self.ref_model(
+                        padded_batch["prompt_input_ids"],
+                        attention_mask=padded_batch["prompt_attention_mask"],
+                        decoder_input_ids=padded_batch.get("completion_decoder_input_ids"),
+                        labels=padded_batch["completion_labels"],
+                    ).logits
+
+                else:
+                    completion_logits = self.ref_model(
+                        padded_batch["completion_input_ids"], attention_mask=padded_batch["completion_attention_mask"]
+                    ).logits
+
+        completion_logps = self.get_batch_logps(
+            completion_logits,
+            padded_batch["completion_labels"],
+            average_log_prob=False,
+            is_encoder_decoder=self.is_encoder_decoder,
+            label_pad_token_id=self.label_pad_token_id,
+        )
+
+        return completion_logps
+
+    @staticmethod
+    def get_batch_logps(
+        logits: torch.FloatTensor,
+        labels: torch.LongTensor,
+        average_log_prob: bool = False,
+        label_pad_token_id: int = -100,
+        is_encoder_decoder: bool = False,
+    ) -> torch.FloatTensor:
+        """Compute the log probabilities of the given labels under the given logits.
+
+        Args:
+            logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)
+            labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length)
+            average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.
+
+        Returns:
+            A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.
+        """
+        if logits.shape[:-1] != labels.shape:
+            raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.")
+
+        if not is_encoder_decoder:
+            labels = labels[:, 1:].clone()
+            logits = logits[:, :-1, :]
+        else:
+            # Fixes end-dec RuntimeError
+            labels = labels.clone()
+
+        loss_mask = labels != label_pad_token_id
+
+        # dummy token; we'll ignore the losses on these tokens later
+        labels[labels == label_pad_token_id] = 0
+
+        per_token_logps = selective_log_softmax(logits, labels)
+
+        if average_log_prob:
+            return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
+        else:
+            return (per_token_logps * loss_mask).sum(-1)
+
+    def forward(
+        self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]]
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+        model_kwargs = (
+            {
+                "labels": batch["completion_labels"],
+                "decoder_input_ids": batch.get("completion_decoder_input_ids"),
+            }
+            if self.is_encoder_decoder
+            else {}
+        )
+        if self.aux_loss_enabled:
+            model_kwargs["output_router_logits"] = True
+
+        outputs = model(
+            batch["completion_input_ids"],
+            attention_mask=batch["completion_attention_mask"],
+            **model_kwargs,
+        )
+        completion_logits = outputs.logits
+
+        completion_logps = self.get_batch_logps(
+            completion_logits,
+            batch["completion_labels"],
+            average_log_prob=False,
+            is_encoder_decoder=self.is_encoder_decoder,
+            label_pad_token_id=self.label_pad_token_id,
+        )
+
+        if completion_logps.shape[0] != len(batch["label"]):
+            raise ValueError(
+                "There is a mismatch between the number of examples in this batch and the number of "
+                "examples for which an output sequence was predicted."
+            )
+
+        chosen_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is True]
+        rejected_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is False]
+
+        chosen_logps = completion_logps[chosen_idx, ...]
+        rejected_logps = completion_logps[rejected_idx, ...]
+
+        chosen_logits = completion_logits[chosen_idx, ...]
+        rejected_logits = completion_logits[rejected_idx, ...]
+
+        if self.aux_loss_enabled:
+            return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, outputs.aux_loss)
+        else:
+            return (chosen_logps, rejected_logps, chosen_logits, rejected_logits)
+
+    def _get_udm_weight(self, rejected_embeddings: torch.FloatTensor) -> torch.FloatTensor:
+        prob_desirable = self._get_chosen_prob(rejected_embeddings)
+        min_ratio = self.args.min_density_ratio
+        max_ratio = self.args.max_density_ratio
+
+        weight = (prob_desirable / (1 - prob_desirable + 1e-8)).clamp(min=min_ratio, max=max_ratio)
+
+        return weight
+
+    def bco_loss(
+        self,
+        policy_chosen_logps: torch.FloatTensor,
+        policy_rejected_logps: torch.FloatTensor,
+        reference_chosen_logps: torch.FloatTensor,
+        reference_rejected_logps: torch.FloatTensor,
+        chosen_embeddings: Optional[torch.FloatTensor],
+        rejected_embeddings: Optional[torch.FloatTensor],
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+        """Compute the BCO loss for a batch of policy and reference model log probabilities.
+
+        Args:
+            policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (num(chosen) in batch_size,)
+            policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (num(rejected) in batch_size,)
+            reference_chosen_logps: Log probabilities of the reference model for the chosen responses. Shape: (num(chosen) in batch_size,)
+            reference_rejected_logps: Log probabilities of the reference model for the rejected responses. Shape: (num(rejected) in batch_size,)
+            chosen_embeddings: embeddings of desirable prompts
+            rejected_embeddings: embeddings of undesirable prompts
+
+        Returns:
+            A tuple of four tensors: (losses, chosen_rewards, rejected_rewards, delta).
+            The losses tensor contains the BCO loss for each example in the batch.
+            The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.
+            The delta value contains the moving average of all implicit rewards.
+        """
+
+        if policy_chosen_logps.shape[0] != 0 or reference_chosen_logps.shape[0] != 0:
+            chosen_logratios = policy_chosen_logps - reference_chosen_logps
+            chosen_rewards = self.beta * chosen_logratios
+        else:
+            # lists can't be empty -- if they are, then accelerate.gather will hang
+            chosen_losses = torch.Tensor([]).to(self.accelerator.device)
+            chosen_rewards = torch.Tensor([]).to(self.accelerator.device)
+
+        if policy_rejected_logps.shape[0] != 0 or reference_rejected_logps.shape[0] != 0:
+            rejected_logratios = policy_rejected_logps - reference_rejected_logps
+            rejected_rewards = self.beta * rejected_logratios
+        else:
+            # lists can't be empty -- if they are, then accelerate.gather will hang
+            rejected_losses = torch.Tensor([]).to(self.accelerator.device)
+            rejected_rewards = torch.Tensor([]).to(self.accelerator.device)
+
+        rewards = torch.cat((chosen_rewards, rejected_rewards), 0).mean().detach()
+        self.running.update(rewards)
+        delta = self.running.mean
+
+        if policy_chosen_logps.shape[0] != 0 or reference_chosen_logps.shape[0] != 0:
+            chosen_losses = -F.logsigmoid(chosen_rewards - delta)
+
+        if policy_rejected_logps.shape[0] != 0 or reference_rejected_logps.shape[0] != 0:
+            rejected_losses = -F.logsigmoid(-(rejected_rewards - delta))
+
+        if self.match_underlying_distribution:
+            chosen_weight = torch.ones_like(chosen_losses)
+            rejected_weight = self._get_udm_weight(rejected_embeddings)
+
+            losses = torch.cat((chosen_weight * chosen_losses, rejected_weight * rejected_losses), dim=0)
+        else:
+            losses = torch.cat((chosen_losses, rejected_losses), dim=0)
+
+        return losses, chosen_rewards, rejected_rewards, torch.as_tensor(delta)
+
+    def get_batch_loss_metrics(
+        self,
+        model,
+        batch: dict[str, Union[list, torch.LongTensor]],
+    ):
+        """Compute the BCO loss and other metrics for the given batch of inputs for train or test."""
+        metrics = {}
+        batch = {k: (v.to(self.accelerator.device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()}
+
+        forward_output = self.forward(model, batch)
+        (
+            policy_chosen_logps,
+            policy_rejected_logps,
+            policy_chosen_logits,
+            policy_rejected_logits,
+        ) = forward_output[:4]
+        if self.aux_loss_enabled:
+            aux_loss = forward_output[4]
+
+        # if reference_logps in batch use them, otherwise use the reference model
+        if "reference_logps" in batch:
+            chosen_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is True]
+            rejected_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is False]
+
+            reference_chosen_logps = batch["reference_logps"][chosen_idx, ...]
+            reference_rejected_logps = batch["reference_logps"][rejected_idx, ...]
+        else:
+            with torch.no_grad():
+                if self.ref_model is None:
+                    with self.null_ref_context():
+                        (
+                            reference_chosen_logps,
+                            reference_rejected_logps,
+                            _,
+                            _,
+                        ) = self.forward(self.model, batch)[:4]
+                else:
+                    (
+                        reference_chosen_logps,
+                        reference_rejected_logps,
+                        _,
+                        _,
+                    ) = self.forward(self.ref_model, batch)[:4]
+
+        chosen_embeddings, rejected_embeddings = self._get_prompt_embeddings(batch)
+
+        losses, chosen_rewards, rejected_rewards, delta = self.bco_loss(
+            policy_chosen_logps,
+            policy_rejected_logps,
+            reference_chosen_logps,
+            reference_rejected_logps,
+            chosen_embeddings,
+            rejected_embeddings,
+        )
+        metrics["delta"] = self.accelerator.gather_for_metrics(delta).mean().item()
+
+        num_chosen = torch.Tensor([len(chosen_rewards)]).to(self.accelerator.device)
+        num_rejected = torch.Tensor([len(rejected_rewards)]).to(self.accelerator.device)
+
+        all_num_chosen = self.accelerator.gather_for_metrics(num_chosen).sum().item()
+        all_num_rejected = self.accelerator.gather_for_metrics(num_rejected).sum().item()
+
+        if all_num_chosen > 0:
+            metrics["rewards/chosen_sum"] = (
+                self.accelerator.gather_for_metrics(chosen_rewards.nansum()).nansum().item()
+            )
+            metrics["logps/chosen_sum"] = (
+                self.accelerator.gather_for_metrics(policy_chosen_logps.nansum()).nansum().item()
+            )
+            metrics["logits/chosen_sum"] = (
+                self.accelerator.gather_for_metrics(policy_chosen_logits.nansum()).nansum().item()
+            )
+            metrics["count/chosen"] = all_num_chosen
+
+        if all_num_rejected > 0:
+            metrics["rewards/rejected_sum"] = (
+                self.accelerator.gather_for_metrics(rejected_rewards.nansum()).nansum().item()
+            )
+            metrics["logps/rejected_sum"] = (
+                self.accelerator.gather_for_metrics(policy_rejected_logps.nansum()).nansum().item()
+            )
+            metrics["logits/rejected_sum"] = (
+                self.accelerator.gather_for_metrics(policy_rejected_logits.nansum()).nansum().item()
+            )
+            metrics["count/rejected"] = all_num_rejected
+
+        loss = losses.nanmean()
+        if self.aux_loss_enabled:
+            loss += self.aux_loss_coef * aux_loss
+
+        return loss, metrics
+
+    def compute_loss(
+        self,
+        model: Union[PreTrainedModel, nn.Module],
+        inputs: dict[str, Union[torch.Tensor, Any]],
+        return_outputs=False,
+        num_items_in_batch=None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]:
+        compute_loss_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+
+        with compute_loss_context_manager:
+            loss, metrics = self.get_batch_loss_metrics(model, inputs)
+
+        # Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class:
+        loss = loss.to(self.args.device)
+        # force log the metrics
+        if self.accelerator.is_main_process:
+            self.store_metrics(metrics, train_eval="train")
+
+        if return_outputs:
+            return (loss, metrics)
+        return loss
+
+    def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None:
+        for key, value in metrics.items():
+            self._stored_metrics[train_eval][key].append(value)
+
+    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
+        if self.train_dataset is None or not has_length(self.train_dataset):
+            return None
+        return SequentialSampler(self.train_dataset)
+
+    def generate_from_model_and_ref(self, model, batch: dict[str, torch.LongTensor]) -> tuple[str, str]:
+        """Generate samples from the model and reference model for the given batch of inputs."""
+
+        # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with
+        # the torch cuda amp context manager as some hidden states are silently casted to full precision.
+        generate_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+        with generate_context_manager:
+            policy_output = model.generate(
+                input_ids=batch["prompt_input_ids"],
+                attention_mask=batch["prompt_attention_mask"],
+                max_length=self.max_length,
+                do_sample=True,
+                pad_token_id=self.processing_class.pad_token_id,
+            )
+
+            # if reference_output in batch use that otherwise use the reference model
+            if "reference_output" in batch:
+                reference_output = batch["reference_output"]
+            else:
+                if self.ref_model is None:
+                    with self.null_ref_context():
+                        reference_output = self.model.generate(
+                            input_ids=batch["prompt_input_ids"],
+                            attention_mask=batch["prompt_attention_mask"],
+                            max_length=self.max_length,
+                            do_sample=True,
+                            pad_token_id=self.processing_class.pad_token_id,
+                        )
+                else:
+                    reference_output = self.ref_model.generate(
+                        input_ids=batch["prompt_input_ids"],
+                        attention_mask=batch["prompt_attention_mask"],
+                        max_length=self.max_length,
+                        do_sample=True,
+                        pad_token_id=self.processing_class.pad_token_id,
+                    )
+
+        policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id)
+        policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True)
+
+        reference_output = pad_to_length(reference_output, self.max_length, self.processing_class.pad_token_id)
+        reference_output_decoded = self.processing_class.batch_decode(reference_output, skip_special_tokens=True)
+
+        return policy_output_decoded, reference_output_decoded
+
+    def prediction_step(
+        self,
+        model: Union[PreTrainedModel, nn.Module],
+        inputs: dict[str, Union[torch.Tensor, Any]],
+        prediction_loss_only: bool,
+        ignore_keys: Optional[list[str]] = None,
+    ):
+        if ignore_keys is None:
+            if hasattr(model, "config"):
+                ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", [])
+            else:
+                ignore_keys = []
+
+        prediction_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+        with torch.no_grad(), prediction_context_manager:
+            loss, metrics = self.get_batch_loss_metrics(model, inputs)
+
+        # force log the metrics
+        if self.accelerator.is_main_process:
+            self.store_metrics(metrics, train_eval="eval")
+
+        if prediction_loss_only:
+            return (loss.detach(), None, None)
+
+        # logits for the chosen and rejected samples from model
+        logits_dict = {
+            "eval_logits/chosen": metrics["logits/chosen"],
+            "eval_logits/rejected": metrics["logits/rejected"],
+        }
+        logits = tuple(v.unsqueeze(dim=0) for k, v in logits_dict.items() if k not in ignore_keys)
+        logits = torch.stack(logits).mean(axis=1).to(self.accelerator.device)
+        labels = torch.zeros(logits.shape[0], device=self.accelerator.device)
+
+        return (loss.detach(), logits, labels)
+
+    def evaluation_loop(
+        self,
+        dataloader: DataLoader,
+        description: str,
+        prediction_loss_only: Optional[bool] = None,
+        ignore_keys: Optional[list[str]] = None,
+        metric_key_prefix: str = "eval",
+    ) -> EvalLoopOutput:
+        """
+        Overriding built-in evaluation loop to store metrics for each batch.
+        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.
+
+        Works both with or without labels.
+        """
+
+        # Sample and save to game log if requested (for one batch to save time)
+        if self.generate_during_eval:
+            # Generate random indices within the range of the total number of samples
+            num_samples = len(dataloader.dataset)
+            random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size)
+
+            # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader
+            random_batch_dataset = dataloader.dataset.select(random_indices)
+            random_batch = self.data_collator(random_batch_dataset)
+            random_batch = self._prepare_inputs(random_batch)
+
+            target_indicies = [i for i in range(len(random_batch["label"])) if random_batch["label"][i] is False]
+            target_batch = {
+                "prompt_input_ids": random_batch["prompt_input_ids"][target_indicies],
+                "prompt_attention_mask": random_batch["prompt_attention_mask"][target_indicies],
+                "prompt": itemgetter(*target_indicies)(random_batch["prompt"]),
+            }
+            policy_output_decoded, ref_output_decoded = self.generate_from_model_and_ref(self.model, target_batch)
+
+            table = pd.DataFrame(
+                columns=["Prompt", "Policy", "Ref Model"],
+                data=[
+                    [prompt, pol[len(prompt) :], ref[len(prompt) :]]
+                    for prompt, pol, ref in zip(target_batch["prompt"], policy_output_decoded, ref_output_decoded)
+                ],
+            )
+            if "wandb" in self.args.report_to:
+                wandb.log({"game_log": wandb.Table(data=table)})
+
+            if "comet_ml" in self.args.report_to:
+                log_table_to_comet_experiment(
+                    name="game_log.csv",
+                    table=table,
+                )
+
+        # Base evaluation
+        initial_output = super().evaluation_loop(
+            dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix
+        )
+
+        return initial_output
+
+    def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None:
+        """
+        Log `logs` on the various objects watching training, including stored metrics.
+
+        Args:
+            logs (`dict[str, float]`):
+                The values to log.
+            start_time (`float` or `None`, *optional*, defaults to `None`):
+                Start time of the training.
+        """
+        # logs either has 'loss' or 'eval_loss'
+        train_eval = "train" if "loss" in logs else "eval"
+        # train metrics should have no prefix, eval should have 'eval_'
+        prefix = "eval_" if train_eval == "eval" else ""
+        # accumulate average metrics from sums and lengths
+        for split in ["chosen", "rejected"]:
+            if f"count/{split}" in self._stored_metrics[train_eval]:
+                count_sum = torch.Tensor(self._stored_metrics[train_eval][f"count/{split}"]).sum().item()
+                for metric in ["rewards", "logps", "logits"]:
+                    logs[f"{prefix}{metric}/{split}"] = (
+                        torch.Tensor(self._stored_metrics[train_eval][f"{metric}/{split}_sum"]).sum().item()
+                        / count_sum
+                    )
+                    # delete obsolete metric
+                    del self._stored_metrics[train_eval][f"{metric}/{split}_sum"]
+                del self._stored_metrics[train_eval][f"count/{split}"]
+        # calculate reward margin
+        if f"{prefix}rewards/chosen" in logs and f"{prefix}rewards/rejected" in logs:
+            logs[f"{prefix}rewards/margins"] = logs[f"{prefix}rewards/chosen"] - logs[f"{prefix}rewards/rejected"]
+        # Add averaged stored metrics to logs
+        for key, metrics in self._stored_metrics[train_eval].items():
+            logs[f"{prefix}{key}"] = torch.Tensor(metrics).mean().item()
+        del self._stored_metrics[train_eval]
+
+        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
+            return super().log(logs, start_time)
+        else:  # transformers<=4.46
+            return super().log(logs)
+
+    def create_model_card(
+        self,
+        model_name: Optional[str] = None,
+        dataset_name: Optional[str] = None,
+        tags: Union[str, list[str], None] = None,
+    ):
+        """
+        Creates a draft of a model card using the information available to the `Trainer`.
+
+        Args:
+            model_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the model.
+            dataset_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the dataset used for training.
+            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
+                Tags to be associated with the model card.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
+            base_model = self.model.config._name_or_path
+        else:
+            base_model = None
+
+        tags = tags or []
+        if isinstance(tags, str):
+            tags = [tags]
+
+        if hasattr(self.model.config, "unsloth_version"):
+            tags.append("unsloth")
+
+        citation = textwrap.dedent("""\
+        @article{jung2024binary,
+            title        = {{Binary Classifier Optimization for Large Language Model Alignment}},
+            author       = {Seungjae Jung and Gunsoo Han and Daniel Wontae Nam and Kyoung{-}Woon On},
+            year         = 2024,
+            eprint       = {arXiv:2404.04656}
+        }""")
+
+        model_card = generate_model_card(
+            base_model=base_model,
+            model_name=model_name,
+            hub_model_id=self.hub_model_id,
+            dataset_name=dataset_name,
+            tags=tags,
+            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
+            comet_url=get_comet_experiment_url(),
+            trainer_name="BCO",
+            trainer_citation=citation,
+            paper_title="Binary Classifier Optimization for Large Language Model Alignment",
+            paper_id="2404.04656",
+        )
+
+        model_card.save(os.path.join(self.args.output_dir, "README.md"))
+class UnslothBCOTrainer(_UnslothBCOTrainer):
+    """
+    
+    Initialize BCOTrainer from [BCO](https://huggingface.co/papers/2404.04656) paper.
+
+    Args:
+        model (`transformers.PreTrainedModel`):
+            The model to train, preferably an `AutoModelForSequenceClassification`.
+        ref_model (`PreTrainedModelWrapper`):
+            Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no
+            reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized.
+        args (`BCOConfig`):
+            The arguments to use for training.
+        train_dataset (`datasets.Dataset`):
+            The dataset to use for training.
+        eval_dataset (`datasets.Dataset`):
+            The dataset to use for evaluation.
+        processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
+            Processing class used to process the data. If provided, will be used to automatically process the inputs
+            for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
+            reuse the fine-tuned model.
+        data_collator (`transformers.DataCollator`, *optional*, defaults to `None`):
+            The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used
+            which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
+        model_init (`Callable[[], transformers.PreTrainedModel]`):
+            The model initializer to use for training. If None is specified, the default model initializer will be used.
+        callbacks (`list[transformers.TrainerCallback]`):
+            The callbacks to use for training.
+        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
+            The optimizer and scheduler to use for training.
+        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
+            The function to use to preprocess the logits before computing the metrics.
+        peft_config (`dict`, defaults to `None`):
+            The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.
+        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
+            The function to use to compute the metrics. Must take a `EvalPrediction` and return
+            a dictionary string to metric values.
+        model_adapter_name (`str`, defaults to `None`):
+            Name of the train target PEFT adapter, when using LoRA with multiple adapters.
+        ref_adapter_name (`str`, defaults to `None`):
+            Name of the reference PEFT adapter, when using LoRA with multiple adapters.
+    
+    """
+    def __init__(
+        self,
+        model = None,
+        ref_model = None,
+        args = None,
+        train_dataset = None,
+        eval_dataset = None,
+        processing_class = None,
+        data_collator = None,
+        model_init = None,
+        callbacks = None,
+        preprocess_logits_for_metrics = None,
+        peft_config = None,
+        compute_metrics = None,
+        model_adapter_name = None,
+        ref_adapter_name = None,
+        embedding_func = None,
+        embedding_tokenizer = None,
+        **kwargs
+    ):
+        if args is None: args = UnslothBCOConfig()
+        use_bf16 = getattr(args, 'bf16', False)
+        use_fp16 = getattr(args, 'fp16', False)
+        force_float32 = False
+        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':
+            print('Unsloth: Switching to float32 training since model cannot work with float16')
+            force_float32 = True
+        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
+        dtype = getattr(model.config, 'torch_dtype', None)
+        if dtype is None: dtype = model.get_input_embeddings().dtype
+        from unsloth_zoo.utils import _get_dtype
+        dtype = _get_dtype(dtype)
+        float16 = dtype == torch.float16
+        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
+        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
+        if force_float32:
+            args.fp16 = False
+            args.bf16 = False
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
+        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
+            args.fp16 = float16
+            args.bf16 = not float16
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
+        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
+            args.eval_strategy = 'steps'
+            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
+        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
+        if ga_steps is not None and ga_steps > 1:
+            from transformers import __version__ as transformers_version
+            if Version(transformers_version) <= Version('4.45.2'):
+                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
+                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
+        if getattr(args, 'eval_strategy', 'no') != 'no':
+            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
+            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
+            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
+        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
+        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
+        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
+        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
+        if force_float32:
+            args.bf16_full_eval = False
+            args.fp16_full_eval = False
+        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
+            args.bf16_full_eval = True
+            args.fp16_full_eval = False
+        elif not bf16_full_eval and not fp16_full_eval:
+            args.bf16_full_eval = args.bf16
+            args.fp16_full_eval = args.fp16
+        _output_logits = False
+        if locals().get('compute_metrics', None) is not None: _output_logits = True
+        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
+        if _output_logits:
+            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
+        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
+            pass
+        else:
+            model_max_seq_length = getattr(model, 'max_seq_length', None)
+            args_max_seq_length  = getattr(args,  'max_seq_length', None)
+            if args_max_seq_length is None and model_max_seq_length is not None:
+                max_seq_length = model.max_seq_length
+                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
+        if model is not None and hasattr(model, 'for_training'):
+            model.for_training()
+        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
+        if 'processing_class' in locals():
+            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
+            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
+        __tokenizer = processing_class if 'processing_class' in locals() else tokenizer
+        from unsloth_zoo.vision_utils import UnslothVisionDataCollator
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
+                data_collator = DataCollatorForLanguageModeling(__tokenizer, mlm = False)
+            elif isinstance(data_collator, DataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
+                data_collator = DataCollatorForSeq2Seq(__tokenizer)
+        else:
+            if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
+            if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
+            if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
+                if isinstance(data_collator, DataCollatorForSeq2Seq):
+                    data_collator = DataCollatorForSeq2Seq(__tokenizer.tokenizer)
+                else:
+                    data_collator = DataCollatorForLanguageModeling(__tokenizer.tokenizer, mlm = False)
+        other_metrics = []
+        
+        from unsloth_zoo.logging_utils import PatchRLStatistics
+        PatchRLStatistics('bco_trainer', other_metrics)
+        
+        super().__init__(
+            model = model,
+            ref_model = ref_model,
+            args = args,
+            train_dataset = train_dataset,
+            eval_dataset = eval_dataset,
+            processing_class = processing_class,
+            data_collator = data_collator,
+            model_init = model_init,
+            callbacks = callbacks,
+            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
+            peft_config = peft_config,
+            compute_metrics = compute_metrics,
+            model_adapter_name = model_adapter_name,
+            ref_adapter_name = ref_adapter_name,
+            embedding_func = embedding_func,
+            embedding_tokenizer = embedding_tokenizer,**kwargs)
+        if hasattr(self, 'neftune_hook_handle'):
+            self.neftune_hook_handle.remove()
+            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
+        if getattr(args, 'neftune_noise_alpha', None) is not None:
+            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
+        pass
+        
+pass

unsloth_compiled_cache/UnslothCPOTrainer.py

@@ -0,0 +1,1551 @@
+"""
+2025.4.1
+2025.4.1
+4.51.3
+0.15.2
+__UNSLOTH_VERSIONING__
+"""
+from torch import Tensor
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from trl.trainer.cpo_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, CPOConfig, CPOTrainer, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, Literal, Optional, PartialState, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, Trainer, TrainerCallback, Union, add_bos_token_if_needed, add_eos_token_if_needed, amp, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, inspect, is_comet_available, is_peft_available, is_torch_fx_proxy, is_wandb_available, log_table_to_comet_experiment, maybe_apply_chat_template, maybe_extract_prompt, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_model_for_kbit_training, random, textwrap, torch, transformers, version, warnings)
+
+
+import os
+from typing import *
+from dataclasses import dataclass, field
+from packaging.version import Version
+import torch
+import numpy as np
+from contextlib import nullcontext
+from torch.nn import functional as F
+from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
+
+torch_compile_options = {
+    "epilogue_fusion"   : True,
+    "max_autotune"      : False,
+    "shape_padding"     : True,
+    "trace.enabled"     : False,
+    "triton.cudagraphs" : False,
+}
+
+@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
+def selective_log_softmax(logits, index):
+    logits = logits.to(torch.float32)
+    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
+    # loop to reduce peak mem consumption
+    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
+    logsumexp_values = torch.logsumexp(logits, dim = -1)
+    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
+    return per_token_logps
+@dataclass
+class UnslothCPOConfig(CPOConfig):
+    """
+    
+    Configuration class for the [`CPOTrainer`].
+
+    Using [`~transformers.HfArgumentParser`] we can turn this class into
+    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
+    command line.
+
+    Parameters:
+        learning_rate (`float`, *optional*, defaults to `1e-6`):
+            Initial learning rate for [`AdamW`] optimizer. The default value replaces that of
+            [`~transformers.TrainingArguments`].
+        max_length (`int` or `None`, *optional*, defaults to `1024`):
+            Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want
+            to use the default data collator.
+        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
+            Maximum length of the prompt. This argument is required if you want to use the default data collator.
+        max_completion_length (`int` or `None`, *optional*, defaults to `None`):
+            Maximum length of the completion. This argument is required if you want to use the default data collator
+            and your model is an encoder-decoder.
+        beta (`float`, *optional*, defaults to `0.1`):
+            Parameter controlling the deviation from the reference model. Higher β means less deviation from the
+            reference model. For the IPO loss (`loss_type="ipo"`), β is the regularization parameter denoted by τ in
+            the [paper](https://huggingface.co/papers/2310.12036).
+        label_smoothing (`float`, *optional*, defaults to `0.0`):
+            Label smoothing factor. This argument is required if you want to use the default data collator.
+        loss_type (`str`, *optional*, defaults to `"sigmoid"`):
+            Type of loss to use. Possible values are:
+
+                - `"sigmoid"`: sigmoid loss from the original [DPO](https://huggingface.co/papers/2305.18290) paper.
+                - `"hinge"`: hinge loss on the normalized likelihood from the [SLiC](https://huggingface.co/papers/2305.10425) paper.
+                - `"ipo"`: IPO loss from the [IPO](https://huggingface.co/papers/2310.12036) paper.
+                - `"simpo"`: SimPO loss from the [SimPO](https://huggingface.co/papers/2405.14734) paper.
+
+        disable_dropout (`bool`, *optional*, defaults to `True`):
+            Whether to disable dropout in the model.
+        cpo_alpha (`float`, *optional*, defaults to `1.0`):
+            Weight of the BC regularizer in CPO training.
+        simpo_gamma (`float`, *optional*, defaults to `0.5`):
+            Target reward margin for the SimPO loss, used only when the `loss_type="simpo"`.
+        label_pad_token_id (`int`, *optional*, defaults to `-100`):
+            Label pad token id. This argument is required if you want to use the default data collator.
+        padding_value (`int` or `None`, *optional*, defaults to `None`):
+            Padding value to use. If `None`, the padding value of the tokenizer is used.
+        truncation_mode (`str`,*optional*,  defaults to `"keep_end"`):
+            Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`.
+            This argument is required if you want to use the default data collator.
+        generate_during_eval (`bool`, *optional*, defaults to `False`):
+            If `True`, generates and logs completions from the model to W&B or Comet during evaluation.
+        is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`):
+            When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument,
+            you need to specify if the model returned by the callable is an encoder-decoder model.
+        model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
+            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
+            string.
+        dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
+            Number of processes to use for processing the dataset.
+    
+    """
+    vllm_sampling_params: Optional[Any] = field(
+        default = None,
+        metadata = {'help': 'vLLM SamplingParams'},
+    )
+    unsloth_num_chunks : Optional[int] = field(
+        default = -1,
+        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
+    )
+    def __init__(
+        self,
+        output_dir = None,
+        overwrite_output_dir = None,
+        do_train = False,
+        do_eval = False,
+        do_predict = False,
+        eval_strategy = 'no',
+        prediction_loss_only = False,
+        per_device_train_batch_size = 4,
+        per_device_eval_batch_size = 4,
+        per_gpu_train_batch_size = None,
+        per_gpu_eval_batch_size = None,
+        gradient_accumulation_steps = 2,
+        eval_accumulation_steps = 2,
+        eval_delay = 0,
+        torch_empty_cache_steps = 250,
+        learning_rate = 5e-05,
+        weight_decay = 0.01,
+        adam_beta1 = 0.9,
+        adam_beta2 = 0.999,
+        adam_epsilon = 1e-08,
+        max_grad_norm = 1.0,
+        num_train_epochs = 3.0,
+        max_steps = -1,
+        lr_scheduler_type = 'linear',
+        warmup_ratio = 0.1,
+        warmup_steps = 0,
+        log_level = 'passive',
+        log_level_replica = 'warning',
+        log_on_each_node = True,
+        logging_dir = None,
+        logging_strategy = 'steps',
+        logging_first_step = False,
+        logging_steps = 1,
+        logging_nan_inf_filter = False,
+        save_strategy = 'steps',
+        save_steps = 500,
+        save_total_limit = None,
+        save_safetensors = True,
+        save_on_each_node = False,
+        save_only_model = False,
+        restore_callback_states_from_checkpoint = False,
+        no_cuda = False,
+        use_cpu = False,
+        use_mps_device = False,
+        seed = 3407,
+        data_seed = 3407,
+        jit_mode_eval = False,
+        use_ipex = False,
+        bf16 = False,
+        fp16 = False,
+        fp16_opt_level = 'O1',
+        half_precision_backend = 'auto',
+        bf16_full_eval = False,
+        fp16_full_eval = False,
+        tf32 = None,
+        local_rank = -1,
+        ddp_backend = None,
+        tpu_num_cores = None,
+        tpu_metrics_debug = False,
+        debug = '',
+        dataloader_drop_last = False,
+        eval_steps = None,
+        dataloader_num_workers = 0,
+        dataloader_prefetch_factor = None,
+        past_index = -1,
+        run_name = None,
+        disable_tqdm = None,
+        remove_unused_columns = True,
+        label_names = None,
+        load_best_model_at_end = False,
+        metric_for_best_model = None,
+        greater_is_better = None,
+        ignore_data_skip = False,
+        fsdp = '',
+        fsdp_min_num_params = 0,
+        fsdp_config = None,
+        tp_size = 0,
+        fsdp_transformer_layer_cls_to_wrap = None,
+        accelerator_config = None,
+        deepspeed = None,
+        label_smoothing_factor = 0.0,
+        optim = 'adamw_8bit',
+        optim_args = None,
+        adafactor = False,
+        group_by_length = False,
+        length_column_name = 'length',
+        report_to = None,
+        ddp_find_unused_parameters = None,
+        ddp_bucket_cap_mb = None,
+        ddp_broadcast_buffers = None,
+        dataloader_pin_memory = True,
+        dataloader_persistent_workers = False,
+        skip_memory_metrics = True,
+        use_legacy_prediction_loop = False,
+        push_to_hub = False,
+        resume_from_checkpoint = None,
+        hub_model_id = None,
+        hub_strategy = 'every_save',
+        hub_token = None,
+        hub_private_repo = None,
+        hub_always_push = False,
+        gradient_checkpointing = False,
+        gradient_checkpointing_kwargs = None,
+        include_inputs_for_metrics = False,
+        eval_do_concat_batches = True,
+        fp16_backend = 'auto',
+        push_to_hub_model_id = None,
+        push_to_hub_organization = None,
+        push_to_hub_token = None,
+        mp_parameters = '',
+        auto_find_batch_size = False,
+        full_determinism = False,
+        torchdynamo = None,
+        ray_scope = 'last',
+        ddp_timeout = 1800,
+        torch_compile = False,
+        torch_compile_backend = None,
+        torch_compile_mode = None,
+        include_tokens_per_second = False,
+        include_num_input_tokens_seen = False,
+        neftune_noise_alpha = None,
+        optim_target_modules = None,
+        batch_eval_metrics = False,
+        eval_on_start = False,
+        use_liger_kernel = False,
+        eval_use_gather_object = False,
+        average_tokens_across_devices = False,
+        max_length = 1024,
+        max_prompt_length = 512,
+        max_completion_length = None,
+        beta = 0.1,
+        label_smoothing = 0.0,
+        loss_type = 'sigmoid',
+        disable_dropout = True,
+        cpo_alpha = 1.0,
+        simpo_gamma = 0.5,
+        label_pad_token_id = -100,
+        padding_value = None,
+        truncation_mode = 'keep_end',
+        generate_during_eval = False,
+        is_encoder_decoder = None,
+        model_init_kwargs = None,
+        dataset_num_proc = None,
+        vllm_sampling_params = None,
+        unsloth_num_chunks = -1,
+        **kwargs,
+    ):
+        if learning_rate < 1e-7: raise FloatingPointError(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
+        if learning_rate > 1: raise OverflowError(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
+        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
+            output_dir = 'unsloth_training_checkpoints'
+            save_strategy = 'no'
+        if dataset_num_proc is None:
+            from multiprocessing import cpu_count
+            dataset_num_proc = cpu_count()
+        
+        super().__init__(
+            output_dir = output_dir,
+            overwrite_output_dir = overwrite_output_dir,
+            do_train = do_train,
+            do_eval = do_eval,
+            do_predict = do_predict,
+            eval_strategy = eval_strategy,
+            prediction_loss_only = prediction_loss_only,
+            per_device_train_batch_size = per_device_train_batch_size,
+            per_device_eval_batch_size = per_device_eval_batch_size,
+            per_gpu_train_batch_size = per_gpu_train_batch_size,
+            per_gpu_eval_batch_size = per_gpu_eval_batch_size,
+            gradient_accumulation_steps = gradient_accumulation_steps,
+            eval_accumulation_steps = eval_accumulation_steps,
+            eval_delay = eval_delay,
+            torch_empty_cache_steps = torch_empty_cache_steps,
+            learning_rate = learning_rate,
+            weight_decay = weight_decay,
+            adam_beta1 = adam_beta1,
+            adam_beta2 = adam_beta2,
+            adam_epsilon = adam_epsilon,
+            max_grad_norm = max_grad_norm,
+            num_train_epochs = num_train_epochs,
+            max_steps = max_steps,
+            lr_scheduler_type = lr_scheduler_type,
+            warmup_ratio = warmup_ratio,
+            warmup_steps = warmup_steps,
+            log_level = log_level,
+            log_level_replica = log_level_replica,
+            log_on_each_node = log_on_each_node,
+            logging_dir = logging_dir,
+            logging_strategy = logging_strategy,
+            logging_first_step = logging_first_step,
+            logging_steps = logging_steps,
+            logging_nan_inf_filter = logging_nan_inf_filter,
+            save_strategy = save_strategy,
+            save_steps = save_steps,
+            save_total_limit = save_total_limit,
+            save_safetensors = save_safetensors,
+            save_on_each_node = save_on_each_node,
+            save_only_model = save_only_model,
+            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
+            no_cuda = no_cuda,
+            use_cpu = use_cpu,
+            use_mps_device = use_mps_device,
+            seed = seed,
+            data_seed = data_seed,
+            jit_mode_eval = jit_mode_eval,
+            use_ipex = use_ipex,
+            bf16 = bf16,
+            fp16 = fp16,
+            fp16_opt_level = fp16_opt_level,
+            half_precision_backend = half_precision_backend,
+            bf16_full_eval = bf16_full_eval,
+            fp16_full_eval = fp16_full_eval,
+            tf32 = tf32,
+            local_rank = local_rank,
+            ddp_backend = ddp_backend,
+            tpu_num_cores = tpu_num_cores,
+            tpu_metrics_debug = tpu_metrics_debug,
+            debug = debug,
+            dataloader_drop_last = dataloader_drop_last,
+            eval_steps = eval_steps,
+            dataloader_num_workers = dataloader_num_workers,
+            dataloader_prefetch_factor = dataloader_prefetch_factor,
+            past_index = past_index,
+            run_name = run_name,
+            disable_tqdm = disable_tqdm,
+            remove_unused_columns = remove_unused_columns,
+            label_names = label_names,
+            load_best_model_at_end = load_best_model_at_end,
+            metric_for_best_model = metric_for_best_model,
+            greater_is_better = greater_is_better,
+            ignore_data_skip = ignore_data_skip,
+            fsdp = fsdp,
+            fsdp_min_num_params = fsdp_min_num_params,
+            fsdp_config = fsdp_config,
+            tp_size = tp_size,
+            fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
+            accelerator_config = accelerator_config,
+            deepspeed = deepspeed,
+            label_smoothing_factor = label_smoothing_factor,
+            optim = optim,
+            optim_args = optim_args,
+            adafactor = adafactor,
+            group_by_length = group_by_length,
+            length_column_name = length_column_name,
+            report_to = report_to,
+            ddp_find_unused_parameters = ddp_find_unused_parameters,
+            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
+            ddp_broadcast_buffers = ddp_broadcast_buffers,
+            dataloader_pin_memory = dataloader_pin_memory,
+            dataloader_persistent_workers = dataloader_persistent_workers,
+            skip_memory_metrics = skip_memory_metrics,
+            use_legacy_prediction_loop = use_legacy_prediction_loop,
+            push_to_hub = push_to_hub,
+            resume_from_checkpoint = resume_from_checkpoint,
+            hub_model_id = hub_model_id,
+            hub_strategy = hub_strategy,
+            hub_token = hub_token,
+            hub_private_repo = hub_private_repo,
+            hub_always_push = hub_always_push,
+            gradient_checkpointing = gradient_checkpointing,
+            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
+            include_inputs_for_metrics = include_inputs_for_metrics,
+            eval_do_concat_batches = eval_do_concat_batches,
+            fp16_backend = fp16_backend,
+            push_to_hub_model_id = push_to_hub_model_id,
+            push_to_hub_organization = push_to_hub_organization,
+            push_to_hub_token = push_to_hub_token,
+            mp_parameters = mp_parameters,
+            auto_find_batch_size = auto_find_batch_size,
+            full_determinism = full_determinism,
+            torchdynamo = torchdynamo,
+            ray_scope = ray_scope,
+            ddp_timeout = ddp_timeout,
+            torch_compile = torch_compile,
+            torch_compile_backend = torch_compile_backend,
+            torch_compile_mode = torch_compile_mode,
+            include_tokens_per_second = include_tokens_per_second,
+            include_num_input_tokens_seen = include_num_input_tokens_seen,
+            neftune_noise_alpha = neftune_noise_alpha,
+            optim_target_modules = optim_target_modules,
+            batch_eval_metrics = batch_eval_metrics,
+            eval_on_start = eval_on_start,
+            use_liger_kernel = use_liger_kernel,
+            eval_use_gather_object = eval_use_gather_object,
+            average_tokens_across_devices = average_tokens_across_devices,
+            max_length = max_length,
+            max_prompt_length = max_prompt_length,
+            max_completion_length = max_completion_length,
+            beta = beta,
+            label_smoothing = label_smoothing,
+            loss_type = loss_type,
+            disable_dropout = disable_dropout,
+            cpo_alpha = cpo_alpha,
+            simpo_gamma = simpo_gamma,
+            label_pad_token_id = label_pad_token_id,
+            padding_value = padding_value,
+            truncation_mode = truncation_mode,
+            generate_during_eval = generate_during_eval,
+            is_encoder_decoder = is_encoder_decoder,
+            model_init_kwargs = model_init_kwargs,
+            dataset_num_proc = dataset_num_proc,**kwargs)
+        self.vllm_sampling_params = vllm_sampling_params
+        self.unsloth_num_chunks = unsloth_num_chunks
+pass
+
+class _UnslothCPOTrainer(Trainer):
+    r""""""
+
+    _tag_names = ["trl", "cpo"]
+
+    def __init__(
+        self,
+        model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
+        args: Optional[CPOConfig] = None,
+        data_collator: Optional[DataCollator] = None,
+        train_dataset: Optional[Dataset] = None,
+        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
+        processing_class: Optional[
+            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
+        ] = None,
+        model_init: Optional[Callable[[], PreTrainedModel]] = None,
+        callbacks: Optional[list[TrainerCallback]] = None,
+        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
+        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+        peft_config: Optional[dict] = None,
+        compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None,
+    ):
+        if args.model_init_kwargs is None:
+            model_init_kwargs = {}
+        elif not isinstance(model, str):
+            raise ValueError("You passed model_kwargs to the CPOTrainer. But your model is already instantiated.")
+        else:
+            model_init_kwargs = args.model_init_kwargs
+            torch_dtype = model_init_kwargs.get("torch_dtype")
+            if torch_dtype is not None:
+                # Convert to `torch.dtype` if an str is passed
+                if isinstance(torch_dtype, str) and torch_dtype != "auto":
+                    torch_dtype = getattr(torch, torch_dtype)
+                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
+                    raise ValueError(
+                        f"Invalid `torch_dtype` passed to the CPOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
+                    )
+                model_init_kwargs["torch_dtype"] = torch_dtype
+
+        if isinstance(model, str):
+            model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)
+
+        # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16`
+        # has been called in order to properly call autocast if needed.
+        self._peft_has_been_casted_to_bf16 = False
+
+        if not is_peft_available() and peft_config is not None:
+            raise ValueError(
+                "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models"
+            )
+        elif is_peft_available() and peft_config is not None:
+            # if model is a peft model and we have a peft_config, we merge and unload it first
+            if isinstance(model, PeftModel):
+                model = model.merge_and_unload()
+
+            if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
+                _support_gc_kwargs = hasattr(
+                    args, "gradient_checkpointing_kwargs"
+                ) and "gradient_checkpointing_kwargs" in list(
+                    inspect.signature(prepare_model_for_kbit_training).parameters
+                )
+
+                prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
+
+                if _support_gc_kwargs:
+                    prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
+
+                model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)
+            elif getattr(args, "gradient_checkpointing", False):
+                # For backward compatibility with older versions of transformers
+                if hasattr(model, "enable_input_require_grads"):
+                    model.enable_input_require_grads()
+                else:
+
+                    def make_inputs_require_grad(module, input, output):
+                        output.requires_grad_(True)
+
+                    model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+            # get peft model with the given config
+            model = model
+            if args.bf16 and getattr(model, "is_loaded_in_4bit", False):
+                peft_module_casting_to_bf16(model)
+                # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager
+                self._peft_has_been_casted_to_bf16 = True
+
+        # For models that use gradient_checkpointing, we need to attach a hook that enables input
+        # to explicitly have `requires_grad=True`, otherwise training will either silently
+        # fail or completely fail.
+        elif getattr(args, "gradient_checkpointing", False):
+            # For backward compatibility with older versions of transformers
+            if hasattr(model, "enable_input_require_grads"):
+                model.enable_input_require_grads()
+            else:
+
+                def make_inputs_require_grad(module, input, output):
+                    output.requires_grad_(True)
+
+                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+        if args.generate_during_eval and not (is_wandb_available() or is_comet_available()):
+            raise ValueError(
+                "`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
+                " Please install `wandb` or `comet-ml` to resolve."
+            )
+
+        if model is not None:
+            self.is_encoder_decoder = model.config.is_encoder_decoder
+        elif args.is_encoder_decoder is None:
+            raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.")
+        else:
+            self.is_encoder_decoder = args.is_encoder_decoder
+
+        if self.is_encoder_decoder:
+            self.decoder_start_token_id = model.config.decoder_start_token_id
+            self.pad_token_id = model.config.pad_token_id
+
+        if processing_class is None:
+            raise ValueError("processing_class must be specified to tokenize a CPO dataset.")
+        if args.max_length is None:
+            warnings.warn(
+                "`max_length` is not set in the CPOConfig's init"
+                " it will default to `512` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_length = 512
+        else:
+            max_length = args.max_length
+        if args.max_prompt_length is None:
+            warnings.warn(
+                "`max_prompt_length` is not set in the CPOConfig's init"
+                " it will default to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_prompt_length = 128
+        else:
+            max_prompt_length = args.max_prompt_length
+
+        if args.max_completion_length is None and self.is_encoder_decoder:
+            warnings.warn(
+                "When using an encoder decoder architecture, you should set `max_completion_length` in the CPOConfig's init"
+                " it will default to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_completion_length = 128
+        else:
+            max_completion_length = args.max_completion_length
+
+        if data_collator is None:
+            data_collator = DPODataCollatorWithPadding(
+                pad_token_id=processing_class.pad_token_id,
+                label_pad_token_id=args.label_pad_token_id,
+                is_encoder_decoder=self.is_encoder_decoder,
+            )
+
+            if args.remove_unused_columns:
+                args.remove_unused_columns = False
+                # warn users
+                warnings.warn(
+                    "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments"
+                    " we have set it for you, but you should do it yourself in the future.",
+                    UserWarning,
+                )
+
+            self.use_dpo_data_collator = True
+        else:
+            self.use_dpo_data_collator = False
+
+        # Disable dropout in the model
+        if args.disable_dropout:
+            disable_dropout_in_model(model)
+
+        self.max_length = max_length
+        self.generate_during_eval = args.generate_during_eval
+        self.label_pad_token_id = args.label_pad_token_id
+        self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id
+        self.max_prompt_length = max_prompt_length
+        self.truncation_mode = args.truncation_mode
+        self.max_completion_length = max_completion_length
+        self.processing_class = processing_class
+
+        if args.loss_type in ["hinge", "ipo"] and args.label_smoothing > 0:
+            warnings.warn(
+                f"You are using the {args.loss_type} loss type that does not support label smoothing. The "
+                "`label_smoothing` parameter will be ignored. Set `label_smoothing` to `0.0` to remove this warning.",
+                UserWarning,
+            )
+        if args.loss_type == "kto_pair":
+            raise ValueError("Support for kto_pair has been removed in CPOTrainer. Please use KTOTrainer.")
+
+        self.beta = args.beta
+        self.label_smoothing = args.label_smoothing
+        self.loss_type = args.loss_type
+        self.cpo_alpha = args.cpo_alpha
+        self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)
+        self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0)
+        if self.aux_loss_enabled and self.aux_loss_coef == 0.0:
+            warnings.warn(
+                "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to "
+                "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value "
+                "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary "
+                "loss.",
+                UserWarning,
+            )
+
+        if args.loss_type == "simpo":
+            self.simpo_gamma = args.simpo_gamma
+
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+
+        # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
+        # input tensor associated with the key "input_ids". However, in CPO, the sampled data does not include the
+        # "input_ids" key. Instead, the available keys are "prompt_input_ids", "chosen_input_ids", and
+        # "rejected_input_ids". As a result, the trainer issues the warning: "Could not estimate the number of tokens
+        # of the input, floating-point operations will not be computed." To suppress this warning, we set the
+        # "estimate_tokens" key in the model's "warnings_issued" dictionary to True. This acts as a flag to indicate
+        # that the warning has already been issued.
+        model.warnings_issued["estimate_tokens"] = True
+
+        # Compute that only on the main process for faster data processing.
+        # see: https://github.com/huggingface/trl/pull/1255
+        with PartialState().local_main_process_first():
+            # Extract the prompt if needed, and apply the chat template if needed
+            train_dataset = train_dataset.map(maybe_extract_prompt, num_proc=args.dataset_num_proc)
+            train_dataset = train_dataset.map(
+                maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}, num_proc=args.dataset_num_proc
+            )
+            if eval_dataset is not None:
+                eval_dataset = eval_dataset.map(maybe_extract_prompt, num_proc=args.dataset_num_proc)
+                eval_dataset = eval_dataset.map(
+                    maybe_apply_chat_template,
+                    fn_kwargs={"tokenizer": processing_class},
+                    num_proc=args.dataset_num_proc,
+                )
+
+            # tokenize the dataset
+            train_dataset = train_dataset.map(self.tokenize_row, num_proc=args.dataset_num_proc)
+            if eval_dataset is not None:
+                eval_dataset = eval_dataset.map(self.tokenize_row, num_proc=args.dataset_num_proc)
+
+        super().__init__(
+            model=model,
+            args=args,
+            data_collator=data_collator,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            processing_class=processing_class,
+            model_init=model_init,
+            compute_metrics=compute_metrics,
+            callbacks=callbacks,
+            optimizers=optimizers,
+            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+        )
+
+        # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the
+        # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set
+        # self.model_accepts_loss_kwargs to False to enable scaling.
+        self.model_accepts_loss_kwargs = False
+
+        # Add tags for models that have been loaded with the correct transformers version
+        if hasattr(self.model, "add_model_tags"):
+            self.model.add_model_tags(self._tag_names)
+
+        if not hasattr(self, "accelerator"):
+            raise AttributeError(
+                "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`."
+            )
+
+    def build_tokenized_answer(self, prompt, answer):
+        """
+        Llama tokenizer does satisfy `enc(a + b) = enc(a) + enc(b)`.
+        It does ensure `enc(a + b) = enc(a) + enc(a + b)[len(enc(a)):]`.
+        Reference:
+            https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257
+        """
+
+        full_tokenized = self.processing_class(prompt + answer, add_special_tokens=False)
+        prompt_input_ids = self.processing_class(prompt, add_special_tokens=False)["input_ids"]
+
+        answer_input_ids = full_tokenized["input_ids"][len(prompt_input_ids) :]
+        answer_attention_mask = full_tokenized["attention_mask"][len(prompt_input_ids) :]
+
+        # Concat tokens to form `enc(a) + enc(a + b)[len(enc(a)):]`
+        full_concat_input_ids = np.concatenate([prompt_input_ids, answer_input_ids])
+
+        # Prepare input tokens for token by token comparison
+        full_input_ids = np.array(full_tokenized["input_ids"])
+
+        if len(full_input_ids) != len(full_concat_input_ids):
+            raise ValueError("Prompt input ids and answer input ids should have the same length.")
+
+        # On some tokenizers, like Llama-2 tokenizer, there are occasions where tokens
+        # can be merged together when tokenizing prompt+answer. This could result
+        # on the last token from the prompt being different when tokenized on its own
+        # vs when done as prompt+answer.
+        response_token_ids_start_idx = len(prompt_input_ids)
+
+        # If tokenized prompt is different than both prompt+answer, then it means the
+        # last token has changed due to merging.
+        if prompt_input_ids != full_tokenized["input_ids"][:response_token_ids_start_idx]:
+            response_token_ids_start_idx -= 1
+
+        prompt_input_ids = full_tokenized["input_ids"][:response_token_ids_start_idx]
+        prompt_attention_mask = full_tokenized["attention_mask"][:response_token_ids_start_idx]
+
+        if len(prompt_input_ids) != len(prompt_attention_mask):
+            raise ValueError("Prompt input ids and attention mask should have the same length.")
+
+        answer_input_ids = full_tokenized["input_ids"][response_token_ids_start_idx:]
+        answer_attention_mask = full_tokenized["attention_mask"][response_token_ids_start_idx:]
+
+        return dict(
+            prompt_input_ids=prompt_input_ids,
+            prompt_attention_mask=prompt_attention_mask,
+            input_ids=answer_input_ids,
+            attention_mask=answer_attention_mask,
+        )
+
+    def tokenize_row(self, feature, model: Optional[Union[PreTrainedModel, nn.Module]] = None) -> dict:
+        """Tokenize a single row from a CPO specific dataset.
+
+        At this stage, we don't convert to PyTorch tensors yet; we just handle the truncation
+        in case the prompt + chosen or prompt + rejected responses is/are too long. First
+        we truncate the prompt; if we're still too long, we truncate the chosen/rejected.
+
+        We also create the labels for the chosen/rejected responses, which are of length equal to
+        the sum of the length of the prompt and the chosen/rejected response, with
+        label_pad_token_id  for the prompt tokens.
+        """
+        batch = {}
+        prompt = feature["prompt"]
+        chosen = feature["chosen"]
+        rejected = feature["rejected"]
+
+        if not self.is_encoder_decoder:
+            # Check issues below for more details
+            #  1. https://github.com/huggingface/trl/issues/907
+            #  2. https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257
+            #  3. https://github.com/LianjiaTech/BELLE/issues/337
+
+            if not isinstance(prompt, str):
+                raise ValueError(f"prompt should be an str but got {type(prompt)}")
+            prompt_tokens = self.processing_class(prompt, add_special_tokens=False)
+            prompt_tokens = {f"prompt_{k}": v for k, v in prompt_tokens.items()}
+
+            if not isinstance(chosen, str):
+                raise ValueError(f"chosen should be an str but got {type(chosen)}")
+            chosen_tokens = self.build_tokenized_answer(prompt, chosen)
+
+            if not isinstance(rejected, str):
+                raise ValueError(f"rejected should be an str but got {type(rejected)}")
+            rejected_tokens = self.build_tokenized_answer(prompt, rejected)
+
+            # Last prompt token might get merged by tokenizer and
+            # it should not be included for generation if that happens
+            prompt_len_input_ids = len(prompt_tokens["prompt_input_ids"])
+
+            chosen_prompt_len_input_ids = len(chosen_tokens["prompt_input_ids"])
+            rejected_prompt_len_input_ids = len(rejected_tokens["prompt_input_ids"])
+            prompt_len_input_ids = min(chosen_prompt_len_input_ids, rejected_prompt_len_input_ids)
+
+            for k, v in prompt_tokens.items():
+                prompt_tokens[k] = v[:prompt_len_input_ids]
+
+            # Make sure prompts only have one different token at most an
+            # and length only differs by 1 at most
+            num_diff_tokens = sum(
+                [a != b for a, b in zip(chosen_tokens["prompt_input_ids"], rejected_tokens["prompt_input_ids"])]
+            )
+            num_diff_len = abs(chosen_prompt_len_input_ids - rejected_prompt_len_input_ids)
+            if num_diff_tokens > 1 or num_diff_len > 1:
+                raise ValueError(
+                    "Chosen and rejected prompt_input_ids might only differ on the "
+                    "last token due to tokenizer merge ops."
+                )
+
+            # add BOS token to head of prompt. Avoid adding if it's already there
+            prompt_tokens, chosen_tokens, rejected_tokens = add_bos_token_if_needed(
+                self.processing_class.bos_token_id,
+                prompt_len_input_ids,
+                prompt_tokens,
+                chosen_prompt_len_input_ids,
+                chosen_tokens,
+                rejected_prompt_len_input_ids,
+                rejected_tokens,
+            )
+
+            # add EOS token to end of answer. Avoid adding if it's already there
+            chosen_tokens, rejected_tokens = add_eos_token_if_needed(
+                self.processing_class.eos_token_id, chosen_tokens, rejected_tokens
+            )
+
+            longer_response_length = max(len(chosen_tokens["input_ids"]), len(rejected_tokens["input_ids"]))
+
+            # if combined sequence is too long, truncate the prompt
+            for answer_tokens in [chosen_tokens, rejected_tokens, prompt_tokens]:
+                if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length:
+                    if self.truncation_mode == "keep_start":
+                        for k in ["prompt_input_ids", "prompt_attention_mask"]:
+                            answer_tokens[k] = answer_tokens[k][: self.max_prompt_length]
+                    elif self.truncation_mode == "keep_end":
+                        for k in ["prompt_input_ids", "prompt_attention_mask"]:
+                            answer_tokens[k] = answer_tokens[k][-self.max_prompt_length :]
+                    else:
+                        raise ValueError(f"Unknown truncation mode: {self.truncation_mode}")
+
+            # if that's still too long, truncate the response
+            for answer_tokens in [chosen_tokens, rejected_tokens]:
+                if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length:
+                    for k in ["input_ids", "attention_mask"]:
+                        answer_tokens[k] = answer_tokens[k][: self.max_length - self.max_prompt_length]
+
+            # Create labels
+            chosen_sequence_tokens = {
+                k: chosen_tokens[f"prompt_{k}"] + chosen_tokens[k] for k in ["input_ids", "attention_mask"]
+            }
+            rejected_sequence_tokens = {
+                k: rejected_tokens[f"prompt_{k}"] + rejected_tokens[k] for k in ["input_ids", "attention_mask"]
+            }
+            chosen_sequence_tokens["labels"] = chosen_sequence_tokens["input_ids"][:]
+            chosen_sequence_tokens["labels"][: len(chosen_tokens["prompt_input_ids"])] = [
+                self.label_pad_token_id
+            ] * len(chosen_tokens["prompt_input_ids"])
+            rejected_sequence_tokens["labels"] = rejected_sequence_tokens["input_ids"][:]
+            rejected_sequence_tokens["labels"][: len(rejected_tokens["prompt_input_ids"])] = [
+                self.label_pad_token_id
+            ] * len(rejected_tokens["prompt_input_ids"])
+
+            for k, toks in {
+                "chosen_": chosen_sequence_tokens,
+                "rejected_": rejected_sequence_tokens,
+                "": prompt_tokens,
+            }.items():
+                for type_key, tokens in toks.items():
+                    if type_key == "token_type_ids":
+                        continue
+                    batch[f"{k}{type_key}"] = tokens
+
+        else:
+            chosen_tokens = self.processing_class(
+                chosen, truncation=True, max_length=self.max_completion_length, add_special_tokens=True
+            )
+            rejected_tokens = self.processing_class(
+                rejected, truncation=True, max_length=self.max_completion_length, add_special_tokens=True
+            )
+            prompt_tokens = self.processing_class(
+                prompt, truncation=True, max_length=self.max_prompt_length, add_special_tokens=True
+            )
+
+            batch["chosen_labels"] = chosen_tokens["input_ids"]
+            batch["rejected_labels"] = rejected_tokens["input_ids"]
+            batch["prompt_input_ids"] = prompt_tokens["input_ids"]
+            batch["prompt_attention_mask"] = prompt_tokens["attention_mask"]
+
+            if model is not None and hasattr(model, "prepare_decoder_input_ids_from_labels"):
+                batch["rejected_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels(
+                    labels=torch.tensor(batch["rejected_labels"])
+                )
+                batch["chosen_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels(
+                    labels=torch.tensor(batch["chosen_labels"])
+                )
+
+        return batch
+
+    @staticmethod
+    def concatenated_inputs(
+        batch: dict[str, Union[list, torch.LongTensor]],
+        is_encoder_decoder: bool = False,
+        label_pad_token_id: int = -100,
+        padding_value: int = 0,
+        device: Optional[torch.device] = None,
+    ) -> dict[str, torch.LongTensor]:
+        """Concatenate the chosen and rejected inputs into a single tensor.
+
+        Args:
+            batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length).
+            is_encoder_decoder: Whether the model is an encoder-decoder model.
+            label_pad_token_id: The label pad token id.
+            padding_value: The padding value to use for the concatenated inputs_ids.
+            device: The device for the concatenated inputs.
+
+        Returns:
+            A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'.
+        """
+        concatenated_batch = {}
+
+        if is_encoder_decoder:
+            max_length = max(batch["chosen_labels"].shape[1], batch["rejected_labels"].shape[1])
+        else:
+            max_length = max(batch["chosen_input_ids"].shape[1], batch["rejected_input_ids"].shape[1])
+
+        for k in batch:
+            if k.startswith("chosen") and isinstance(batch[k], torch.Tensor):
+                if "labels" in k or is_encoder_decoder:
+                    pad_value = label_pad_token_id
+                elif k.endswith("_input_ids"):
+                    pad_value = padding_value
+                elif k.endswith("_attention_mask"):
+                    pad_value = 0
+                concatenated_key = k.replace("chosen", "concatenated")
+                concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value)
+        for k in batch:
+            if k.startswith("rejected") and isinstance(batch[k], torch.Tensor):
+                if "labels" in k or is_encoder_decoder:
+                    pad_value = label_pad_token_id
+                elif k.endswith("_input_ids"):
+                    pad_value = padding_value
+                elif k.endswith("_attention_mask"):
+                    pad_value = 0
+                concatenated_key = k.replace("rejected", "concatenated")
+                concatenated_batch[concatenated_key] = torch.cat(
+                    (
+                        concatenated_batch[concatenated_key],
+                        pad_to_length(batch[k], max_length, pad_value=pad_value),
+                    ),
+                    dim=0,
+                ).to(device=device)
+
+        if is_encoder_decoder:
+            concatenated_batch["concatenated_input_ids"] = batch["prompt_input_ids"].repeat(2, 1).to(device=device)
+            concatenated_batch["concatenated_attention_mask"] = (
+                batch["prompt_attention_mask"].repeat(2, 1).to(device=device)
+            )
+
+        return concatenated_batch
+
+    def cpo_loss(
+        self,
+        policy_chosen_logps: torch.FloatTensor,
+        policy_rejected_logps: torch.FloatTensor,
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+        """Compute the CPO loss for a batch of policy and reference model log probabilities.
+
+        Args:
+            policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,)
+            policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_size,)
+
+        Returns:
+            A tuple of three tensors: (losses, chosen_rewards, rejected_rewards).
+            The losses tensor contains the CPO loss for each example in the batch.
+            The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.
+        """
+        logits = (policy_chosen_logps - policy_rejected_logps).to(self.accelerator.device)
+
+        # The beta is a temperature parameter for the CPO loss, typically something in the range of 0.1 to 0.5.
+        # We ignore the reference model as beta -> 0. The label_smoothing parameter encodes our uncertainty about the labels and
+        # calculates a conservative CPO loss.
+
+        if self.loss_type == "simpo":
+            gamma_logratios = self.simpo_gamma / self.beta
+            logits = logits - gamma_logratios
+            # This reduces to Equation 3 from the CPO paper when label_smoothing -> 0.
+            losses = (
+                -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing)
+                - F.logsigmoid(-self.beta * logits) * self.label_smoothing
+            )
+        elif self.loss_type == "sigmoid":
+            # This reduces to Equation 3 from the CPO paper when label_smoothing -> 0.
+            losses = (
+                -F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing)
+                - F.logsigmoid(-self.beta * logits) * self.label_smoothing
+            )
+        elif self.loss_type == "hinge":
+            losses = torch.relu(1 - self.beta * logits)
+        elif self.loss_type == "ipo":
+            # eqn (17) of the paper where beta is the regularization parameter for the IPO loss, denoted by tau in the paper.
+            losses = (logits - 1 / (2 * self.beta)) ** 2
+        else:
+            raise ValueError(
+                f"Unknown loss type: {self.loss_type}. Should be one of ['sigmoid', 'hinge', 'ipo', 'simpo']"
+            )
+
+        chosen_rewards = self.beta * (policy_chosen_logps.to(self.accelerator.device)).detach()
+        rejected_rewards = self.beta * (policy_rejected_logps.to(self.accelerator.device)).detach()
+
+        return losses, chosen_rewards, rejected_rewards
+
+    @staticmethod
+    def get_batch_logps(
+        logits: torch.FloatTensor,
+        labels: torch.LongTensor,
+        average_log_prob: bool = False,
+        label_pad_token_id: int = -100,
+        is_encoder_decoder: bool = False,
+    ) -> torch.FloatTensor:
+        """Compute the log probabilities of the given labels under the given logits.
+
+        Args:
+            logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)
+            labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length)
+            average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.
+            label_pad_token_id: The label pad token id.
+            is_encoder_decoder: Whether the model is an encoder-decoder model.
+
+        Returns:
+            A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.
+        """
+        if logits.shape[:-1] != labels.shape:
+            raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.")
+
+        if not is_encoder_decoder:
+            labels = labels[:, 1:].clone()
+            logits = logits[:, :-1, :]
+        loss_mask = labels != label_pad_token_id
+
+        # dummy token; we'll ignore the losses on these tokens later
+        labels[labels == label_pad_token_id] = 0
+
+        per_token_logps = selective_log_softmax(logits, labels)
+
+        if average_log_prob:
+            return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
+        else:
+            return (per_token_logps * loss_mask).sum(-1)
+
+    def concatenated_forward(
+        self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]]
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+        """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.
+
+        We do this to avoid doing two forward passes, because it's faster for FSDP.
+        """
+        concatenated_batch = self.concatenated_inputs(
+            batch,
+            is_encoder_decoder=self.is_encoder_decoder,
+            label_pad_token_id=self.label_pad_token_id,
+            padding_value=self.padding_value,
+            device=self.accelerator.device,
+        )
+        len_chosen = batch["chosen_labels"].shape[0]
+
+        model_kwargs = (
+            {
+                "decoder_input_ids": self._shift_right(concatenated_batch["concatenated_labels"]),
+            }
+            if self.is_encoder_decoder
+            else {}
+        )
+
+        if self.aux_loss_enabled:
+            model_kwargs["output_router_logits"] = True
+
+        outputs = model(
+            concatenated_batch["concatenated_input_ids"],
+            attention_mask=concatenated_batch["concatenated_attention_mask"],
+            use_cache=False,
+            **model_kwargs,
+        )
+        all_logits = outputs.logits
+
+        def cross_entropy_loss(logits, labels):
+            if not self.is_encoder_decoder:
+                # Shift so that tokens < n predict n
+                logits = logits[..., :-1, :].contiguous()
+                labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            logits = logits.view(-1, logits.shape[-1])
+            labels = labels.view(-1)
+            # Enable model parallelism
+            labels = labels.to(logits.device)
+            loss = loss_fct(logits, labels)
+            return loss
+
+        labels = concatenated_batch["concatenated_labels"].clone()
+
+        if self.cpo_alpha == 0:
+            nll_loss = torch.tensor(0.0).to(self.accelerator.device)
+        else:
+            nll_loss = cross_entropy_loss(all_logits[:len_chosen], labels[:len_chosen])
+
+        all_logps = self.get_batch_logps(
+            all_logits,
+            concatenated_batch["concatenated_labels"],
+            average_log_prob=self.loss_type in ["ipo", "simpo"],
+            is_encoder_decoder=self.is_encoder_decoder,
+            label_pad_token_id=self.label_pad_token_id,
+        )
+
+        chosen_logps = all_logps[:len_chosen]
+        rejected_logps = all_logps[len_chosen:]
+
+        chosen_logits = all_logits[:len_chosen]
+        rejected_logits = all_logits[len_chosen:]
+
+        if self.aux_loss_enabled:
+            return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, nll_loss, outputs.aux_loss)
+
+        return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, nll_loss)
+
+    def get_batch_loss_metrics(
+        self,
+        model,
+        batch: dict[str, Union[list, torch.LongTensor]],
+        train_eval: Literal["train", "eval"] = "train",
+    ):
+        """Compute the CPO loss and other metrics for the given batch of inputs for train or test."""
+        metrics = {}
+
+        forward_output = self.concatenated_forward(model, batch)
+        (
+            policy_chosen_logps,
+            policy_rejected_logps,
+            policy_chosen_logits,
+            policy_rejected_logits,
+            policy_nll_loss,
+        ) = forward_output[:5]
+        if self.aux_loss_enabled:
+            aux_loss = forward_output[5]
+
+        losses, chosen_rewards, rejected_rewards = self.cpo_loss(
+            policy_chosen_logps,
+            policy_rejected_logps,
+        )
+
+        loss = losses.mean() + self.cpo_alpha * policy_nll_loss
+        reward_accuracies = (chosen_rewards > rejected_rewards).float()
+
+        prefix = "eval_" if train_eval == "eval" else ""
+        metrics[f"{prefix}rewards/chosen"] = self.accelerator.gather_for_metrics(chosen_rewards).mean().item()
+        metrics[f"{prefix}rewards/rejected"] = self.accelerator.gather_for_metrics(rejected_rewards).mean().item()
+        metrics[f"{prefix}rewards/accuracies"] = self.accelerator.gather_for_metrics(reward_accuracies).mean().item()
+        metrics[f"{prefix}rewards/margins"] = (
+            self.accelerator.gather_for_metrics(chosen_rewards - rejected_rewards).mean().item()
+        )
+        metrics[f"{prefix}logps/rejected"] = (
+            self.accelerator.gather_for_metrics(policy_rejected_logps).detach().mean().item()
+        )
+        metrics[f"{prefix}logps/chosen"] = (
+            self.accelerator.gather_for_metrics(policy_chosen_logps).detach().mean().item()
+        )
+        metrics[f"{prefix}logits/rejected"] = (
+            self.accelerator.gather_for_metrics(policy_rejected_logits).detach().mean().item()
+        )
+        metrics[f"{prefix}logits/chosen"] = (
+            self.accelerator.gather_for_metrics(policy_chosen_logits).detach().mean().item()
+        )
+        metrics[f"{prefix}nll_loss"] = self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean().item()
+
+        if self.aux_loss_enabled:
+            loss += self.aux_loss_coef * aux_loss
+
+        return loss, metrics
+
+    def compute_loss(
+        self,
+        model: Union[PreTrainedModel, nn.Module],
+        inputs: dict[str, Union[torch.Tensor, Any]],
+        return_outputs=False,
+        num_items_in_batch=None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]:
+        compute_loss_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+
+        with compute_loss_context_manager:
+            loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="train")
+
+        # force log the metrics
+        self.store_metrics(metrics, train_eval="train")
+
+        if return_outputs:
+            return (loss, metrics)
+        return loss
+
+    def generate_from_model(self, model, batch: dict[str, torch.LongTensor]) -> str:
+        """Generate samples from the model and reference model for the given batch of inputs."""
+
+        # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with
+        # the torch cuda amp context manager as some hidden states are silently casted to full precision.
+        generate_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+
+        with generate_context_manager:
+            policy_output = model.generate(
+                input_ids=batch["prompt_input_ids"],
+                attention_mask=batch["prompt_attention_mask"],
+                max_length=self.max_length,
+                do_sample=True,
+                pad_token_id=self.processing_class.pad_token_id,
+            )
+
+        policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id)
+        policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True)
+
+        return policy_output_decoded
+
+    def prediction_step(
+        self,
+        model: Union[PreTrainedModel, nn.Module],
+        inputs: dict[str, Union[torch.Tensor, Any]],
+        prediction_loss_only: bool,
+        ignore_keys: Optional[list[str]] = None,
+    ):
+        if ignore_keys is None:
+            if hasattr(model, "config"):
+                ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", [])
+            else:
+                ignore_keys = []
+
+        prediction_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+
+        with torch.no_grad(), prediction_context_manager:
+            loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="eval")
+
+        # force log the metrics
+        self.store_metrics(metrics, train_eval="eval")
+
+        if prediction_loss_only:
+            return (loss.detach(), None, None)
+
+        # logits for the chosen and rejected samples from model
+        logits_dict = {
+            "eval_logits/chosen": metrics["eval_logits/chosen"],
+            "eval_logits/rejected": metrics["eval_logits/rejected"],
+        }
+        logits = tuple(v.unsqueeze(dim=0) for k, v in logits_dict.items() if k not in ignore_keys)
+        logits = torch.stack(logits).mean(axis=1).to(self.accelerator.device)
+        labels = torch.zeros(logits.shape[0], device=self.accelerator.device)
+
+        return (loss.detach(), logits, labels)
+
+    def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None:
+        for key, value in metrics.items():
+            self._stored_metrics[train_eval][key].append(value)
+
+    def evaluation_loop(
+        self,
+        dataloader: DataLoader,
+        description: str,
+        prediction_loss_only: Optional[bool] = None,
+        ignore_keys: Optional[list[str]] = None,
+        metric_key_prefix: str = "eval",
+    ) -> EvalLoopOutput:
+        """
+        Overriding built-in evaluation loop to store metrics for each batch.
+        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.
+
+        Works both with or without labels.
+        """
+
+        # Sample and save to game log if requested (for one batch to save time)
+        if self.generate_during_eval:
+            # Generate random indices within the range of the total number of samples
+            num_samples = len(dataloader.dataset)
+            random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size)
+
+            # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader
+            random_batch_dataset = dataloader.dataset.select(random_indices)
+            random_batch = self.data_collator(random_batch_dataset)
+            random_batch = self._prepare_inputs(random_batch)
+
+            policy_output_decoded = self.generate_from_model(self.model, random_batch)
+
+            table = pd.DataFrame(
+                columns=["Prompt", "Policy"],
+                data=[
+                    [prompt, pol[len(prompt) :]] for prompt, pol in zip(random_batch["prompt"], policy_output_decoded)
+                ],
+            )
+            if "wandb" in self.args.report_to:
+                wandb.log({"game_log": wandb.Table(data=table)})
+
+            if "comet_ml" in self.args.report_to:
+                log_table_to_comet_experiment(
+                    name="game_log.csv",
+                    table=table,
+                )
+
+        # Base evaluation
+        initial_output = super().evaluation_loop(
+            dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix
+        )
+
+        return initial_output
+
+    def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None:
+        """
+        Log `logs` on the various objects watching training, including stored metrics.
+
+        Args:
+            logs (`dict[str, float]`):
+                The values to log.
+            start_time (`float` or `None`, *optional*, defaults to `None`):
+                Start time of the training.
+        """
+        # logs either has 'loss' or 'eval_loss'
+        train_eval = "train" if "loss" in logs else "eval"
+        # Add averaged stored metrics to logs
+        for key, metrics in self._stored_metrics[train_eval].items():
+            logs[key] = torch.tensor(metrics).mean().item()
+        del self._stored_metrics[train_eval]
+
+        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
+            return super().log(logs, start_time)
+        else:  # transformers<=4.46
+            return super().log(logs)
+
+    def _shift_right(self, input_ids):
+        if self.decoder_start_token_id is None:
+            raise ValueError(
+                "model.config.decoder_start_token_id has to be defined. It is usually set to the pad_token_id."
+            )
+
+        # shift inputs to the right
+        if is_torch_fx_proxy(input_ids):
+            # Item assignment is not supported natively for proxies.
+            shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), self.decoder_start_token_id)
+            shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
+        else:
+            shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+            shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
+            shifted_input_ids[..., 0] = self.decoder_start_token_id
+
+        if self.pad_token_id is None:
+            raise ValueError("model.config.pad_token_id has to be defined.")
+        # replace possible -100 values in labels by `pad_token_id`
+        shifted_input_ids.masked_fill_(shifted_input_ids == -100, self.pad_token_id)
+
+        return shifted_input_ids
+
+    def create_model_card(
+        self,
+        model_name: Optional[str] = None,
+        dataset_name: Optional[str] = None,
+        tags: Union[str, list[str], None] = None,
+    ):
+        """
+        Creates a draft of a model card using the information available to the `Trainer`.
+
+        Args:
+            model_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the model.
+            dataset_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the dataset used for training.
+            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
+                Tags to be associated with the model card.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
+            base_model = self.model.config._name_or_path
+        else:
+            base_model = None
+
+        tags = tags or []
+        if isinstance(tags, str):
+            tags = [tags]
+
+        if hasattr(self.model.config, "unsloth_version"):
+            tags.append("unsloth")
+
+        citation = textwrap.dedent("""\
+        @inproceedings{xu2024contrastive,
+            title        = {{Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}},
+            author       = {Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
+            year         = 2024,
+            booktitle    = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
+            publisher    = {OpenReview.net},
+            url          = {https://openreview.net/forum?id=51iwkioZpn}
+        }""")
+
+        model_card = generate_model_card(
+            base_model=base_model,
+            model_name=model_name,
+            hub_model_id=self.hub_model_id,
+            dataset_name=dataset_name,
+            tags=tags,
+            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
+            comet_url=get_comet_experiment_url(),
+            trainer_name="CPO",
+            trainer_citation=citation,
+            paper_title="Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation",
+            paper_id="2401.08417",
+        )
+        model_card.save(os.path.join(self.args.output_dir, "README.md"))
+class UnslothCPOTrainer(_UnslothCPOTrainer):
+    """
+    
+    Initialize CPOTrainer.
+
+    Args:
+        model (`transformers.PreTrainedModel`):
+            The model to train, preferably an `AutoModelForSequenceClassification`.
+        args (`CPOConfig`):
+            The CPO config arguments to use for training.
+        data_collator (`transformers.DataCollator`):
+            The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used
+            which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
+        train_dataset (`datasets.Dataset`):
+            The dataset to use for training.
+        eval_dataset (`datasets.Dataset`):
+            The dataset to use for evaluation.
+        processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
+            Processing class used to process the data. If provided, will be used to automatically process the inputs
+            for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
+            reuse the fine-tuned model.
+        model_init (`Callable[[], transformers.PreTrainedModel]`):
+            The model initializer to use for training. If None is specified, the default model initializer will be used.
+        callbacks (`list[transformers.TrainerCallback]`):
+            The callbacks to use for training.
+        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
+            The optimizer and scheduler to use for training.
+        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
+            The function to use to preprocess the logits before computing the metrics.
+        peft_config (`dict`, defaults to `None`):
+            The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.
+        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
+            The function to use to compute the metrics. Must take a `EvalPrediction` and return
+            a dictionary string to metric values.
+    
+    """
+    def __init__(
+        self,
+        model = None,
+        args = None,
+        data_collator = None,
+        train_dataset = None,
+        eval_dataset = None,
+        processing_class = None,
+        model_init = None,
+        callbacks = None,
+        preprocess_logits_for_metrics = None,
+        peft_config = None,
+        compute_metrics = None,
+        **kwargs
+    ):
+        if args is None: args = UnslothCPOConfig()
+        use_bf16 = getattr(args, 'bf16', False)
+        use_fp16 = getattr(args, 'fp16', False)
+        force_float32 = False
+        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':
+            print('Unsloth: Switching to float32 training since model cannot work with float16')
+            force_float32 = True
+        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
+        dtype = getattr(model.config, 'torch_dtype', None)
+        if dtype is None: dtype = model.get_input_embeddings().dtype
+        from unsloth_zoo.utils import _get_dtype
+        dtype = _get_dtype(dtype)
+        float16 = dtype == torch.float16
+        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
+        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
+        if force_float32:
+            args.fp16 = False
+            args.bf16 = False
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
+        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
+            args.fp16 = float16
+            args.bf16 = not float16
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
+        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
+            args.eval_strategy = 'steps'
+            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
+        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
+        if ga_steps is not None and ga_steps > 1:
+            from transformers import __version__ as transformers_version
+            if Version(transformers_version) <= Version('4.45.2'):
+                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
+                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
+        if getattr(args, 'eval_strategy', 'no') != 'no':
+            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
+            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
+            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
+        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
+        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
+        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
+        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
+        if force_float32:
+            args.bf16_full_eval = False
+            args.fp16_full_eval = False
+        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
+            args.bf16_full_eval = True
+            args.fp16_full_eval = False
+        elif not bf16_full_eval and not fp16_full_eval:
+            args.bf16_full_eval = args.bf16
+            args.fp16_full_eval = args.fp16
+        _output_logits = False
+        if locals().get('compute_metrics', None) is not None: _output_logits = True
+        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
+        if _output_logits:
+            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
+        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
+            pass
+        else:
+            model_max_seq_length = getattr(model, 'max_seq_length', None)
+            args_max_seq_length  = getattr(args,  'max_seq_length', None)
+            if args_max_seq_length is None and model_max_seq_length is not None:
+                max_seq_length = model.max_seq_length
+                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
+        if model is not None and hasattr(model, 'for_training'):
+            model.for_training()
+        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
+        if 'processing_class' in locals():
+            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
+            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
+        __tokenizer = processing_class if 'processing_class' in locals() else tokenizer
+        from unsloth_zoo.vision_utils import UnslothVisionDataCollator
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
+                data_collator = DataCollatorForLanguageModeling(__tokenizer, mlm = False)
+            elif isinstance(data_collator, DataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
+                data_collator = DataCollatorForSeq2Seq(__tokenizer)
+        else:
+            if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
+            if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
+            if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
+                if isinstance(data_collator, DataCollatorForSeq2Seq):
+                    data_collator = DataCollatorForSeq2Seq(__tokenizer.tokenizer)
+                else:
+                    data_collator = DataCollatorForLanguageModeling(__tokenizer.tokenizer, mlm = False)
+        other_metrics = []
+        
+        from unsloth_zoo.logging_utils import PatchRLStatistics
+        PatchRLStatistics('cpo_trainer', other_metrics)
+        
+        super().__init__(
+            model = model,
+            args = args,
+            data_collator = data_collator,
+            train_dataset = train_dataset,
+            eval_dataset = eval_dataset,
+            processing_class = processing_class,
+            model_init = model_init,
+            callbacks = callbacks,
+            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
+            peft_config = peft_config,
+            compute_metrics = compute_metrics,**kwargs)
+        if hasattr(self, 'neftune_hook_handle'):
+            self.neftune_hook_handle.remove()
+            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
+        if getattr(args, 'neftune_noise_alpha', None) is not None:
+            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
+        pass
+        
+pass

unsloth_compiled_cache/UnslothGKDTrainer.py

@@ -0,0 +1,857 @@
+"""
+2025.4.1
+2025.4.1
+4.51.3
+0.15.2
+__UNSLOTH_VERSIONING__
+"""
+from torch import Tensor
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from trl.trainer.gkd_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DataCollator, DataCollatorForChatML, Dataset, EvalPrediction, F, FeatureExtractionMixin, GKDConfig, GKDTrainer, GenerationConfig, Optional, PeftConfig, PreTrainedModel, PreTrainedModelWrapper, PreTrainedTokenizerBase, ProcessorMixin, SFTTrainer, TrainerCallback, Union, deepcopy, disable_dropout_in_model, empty_cache, generate_model_card, get_comet_experiment_url, is_wandb_available, nn, os, random, textwrap, torch, unwrap_model_for_generation)
+
+
+import os
+from typing import *
+from dataclasses import dataclass, field
+from packaging.version import Version
+import torch
+import numpy as np
+from contextlib import nullcontext
+from torch.nn import functional as F
+from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
+
+torch_compile_options = {
+    "epilogue_fusion"   : True,
+    "max_autotune"      : False,
+    "shape_padding"     : True,
+    "trace.enabled"     : False,
+    "triton.cudagraphs" : False,
+}
+
+@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
+def selective_log_softmax(logits, index):
+    logits = logits.to(torch.float32)
+    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
+    # loop to reduce peak mem consumption
+    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
+    logsumexp_values = torch.logsumexp(logits, dim = -1)
+    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
+    return per_token_logps
+@dataclass
+class UnslothGKDConfig(GKDConfig):
+    """
+    
+    Configuration class for [`GKDTrainer`].
+
+    Args:
+        temperature (`float`, *optional*, defaults to `0.9`):
+            Temperature for sampling. The higher the temperature, the more random the completions.
+        lmbda (`float`, *optional*, defaults to `0.5`):
+            Lambda parameter that controls the student data fraction (i.e., the proportion of on-policy
+            student-generated outputs).
+        beta (`float`, *optional*, defaults to `0.5`):
+            Interpolation coefficient between `0.0` and `1.0` of the Generalized Jensen-Shannon Divergence loss. When
+            beta is `0.0`, the loss is the KL divergence. When beta is `1.0`, the loss is the Inverse KL Divergence.
+        max_new_tokens (`int`, *optional*, defaults to `128`):
+            Maximum number of tokens to generate per completion.
+        teacher_model_name_or_path (`str` or `None`, *optional*, defaults to `None`):
+            Model name or path of the teacher model. If `None`, the teacher model will be the same as the model
+            being trained.
+        teacher_model_init_kwargs (`dict[str, Any]]` or `None`, *optional*, defaults to `None`):
+            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the teacher model
+            from a string.
+        disable_dropout (`bool`, *optional*, defaults to `True`):
+            Whether to disable dropout in the model.
+        seq_kd (`bool`, *optional*, defaults to `False`):
+            Seq_kd parameter that controls whether to perform Sequence-Level KD (can be viewed as supervised FT
+            on teacher-generated output).
+    
+    """
+    vllm_sampling_params: Optional[Any] = field(
+        default = None,
+        metadata = {'help': 'vLLM SamplingParams'},
+    )
+    unsloth_num_chunks : Optional[int] = field(
+        default = -1,
+        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
+    )
+    def __init__(
+        self,
+        output_dir = None,
+        overwrite_output_dir = None,
+        do_train = False,
+        do_eval = False,
+        do_predict = False,
+        eval_strategy = 'no',
+        prediction_loss_only = False,
+        per_device_train_batch_size = 4,
+        per_device_eval_batch_size = 4,
+        per_gpu_train_batch_size = None,
+        per_gpu_eval_batch_size = None,
+        gradient_accumulation_steps = 2,
+        eval_accumulation_steps = 2,
+        eval_delay = 0,
+        torch_empty_cache_steps = 250,
+        learning_rate = 5e-05,
+        weight_decay = 0.01,
+        adam_beta1 = 0.9,
+        adam_beta2 = 0.999,
+        adam_epsilon = 1e-08,
+        max_grad_norm = 1.0,
+        num_train_epochs = 3.0,
+        max_steps = -1,
+        lr_scheduler_type = 'linear',
+        warmup_ratio = 0.1,
+        warmup_steps = 0,
+        log_level = 'passive',
+        log_level_replica = 'warning',
+        log_on_each_node = True,
+        logging_dir = None,
+        logging_strategy = 'steps',
+        logging_first_step = False,
+        logging_steps = 1,
+        logging_nan_inf_filter = False,
+        save_strategy = 'steps',
+        save_steps = 500,
+        save_total_limit = None,
+        save_safetensors = True,
+        save_on_each_node = False,
+        save_only_model = False,
+        restore_callback_states_from_checkpoint = False,
+        no_cuda = False,
+        use_cpu = False,
+        use_mps_device = False,
+        seed = 3407,
+        data_seed = 3407,
+        jit_mode_eval = False,
+        use_ipex = False,
+        bf16 = False,
+        fp16 = False,
+        fp16_opt_level = 'O1',
+        half_precision_backend = 'auto',
+        bf16_full_eval = False,
+        fp16_full_eval = False,
+        tf32 = None,
+        local_rank = -1,
+        ddp_backend = None,
+        tpu_num_cores = None,
+        tpu_metrics_debug = False,
+        debug = '',
+        dataloader_drop_last = False,
+        eval_steps = None,
+        dataloader_num_workers = 0,
+        dataloader_prefetch_factor = None,
+        past_index = -1,
+        run_name = None,
+        disable_tqdm = None,
+        remove_unused_columns = True,
+        label_names = None,
+        load_best_model_at_end = False,
+        metric_for_best_model = None,
+        greater_is_better = None,
+        ignore_data_skip = False,
+        fsdp = '',
+        fsdp_min_num_params = 0,
+        fsdp_config = None,
+        tp_size = 0,
+        fsdp_transformer_layer_cls_to_wrap = None,
+        accelerator_config = None,
+        deepspeed = None,
+        label_smoothing_factor = 0.0,
+        optim = 'adamw_8bit',
+        optim_args = None,
+        adafactor = False,
+        group_by_length = False,
+        length_column_name = 'length',
+        report_to = None,
+        ddp_find_unused_parameters = None,
+        ddp_bucket_cap_mb = None,
+        ddp_broadcast_buffers = None,
+        dataloader_pin_memory = True,
+        dataloader_persistent_workers = False,
+        skip_memory_metrics = True,
+        use_legacy_prediction_loop = False,
+        push_to_hub = False,
+        resume_from_checkpoint = None,
+        hub_model_id = None,
+        hub_strategy = 'every_save',
+        hub_token = None,
+        hub_private_repo = None,
+        hub_always_push = False,
+        gradient_checkpointing = False,
+        gradient_checkpointing_kwargs = None,
+        include_inputs_for_metrics = False,
+        eval_do_concat_batches = True,
+        fp16_backend = 'auto',
+        push_to_hub_model_id = None,
+        push_to_hub_organization = None,
+        push_to_hub_token = None,
+        mp_parameters = '',
+        auto_find_batch_size = False,
+        full_determinism = False,
+        torchdynamo = None,
+        ray_scope = 'last',
+        ddp_timeout = 1800,
+        torch_compile = False,
+        torch_compile_backend = None,
+        torch_compile_mode = None,
+        include_tokens_per_second = False,
+        include_num_input_tokens_seen = False,
+        neftune_noise_alpha = None,
+        optim_target_modules = None,
+        batch_eval_metrics = False,
+        eval_on_start = False,
+        use_liger_kernel = False,
+        eval_use_gather_object = False,
+        average_tokens_across_devices = False,
+        model_init_kwargs = None,
+        use_liger = False,
+        dataset_text_field = 'text',
+        dataset_kwargs = None,
+        dataset_num_proc = None,
+        max_seq_length = None,
+        packing = False,
+        eval_packing = None,
+        dataset_batch_size = None,
+        num_of_sequences = None,
+        chars_per_token = None,
+        temperature = 0.9,
+        lmbda = 0.5,
+        beta = 0.5,
+        max_new_tokens = 128,
+        teacher_model_name_or_path = None,
+        teacher_model_init_kwargs = None,
+        disable_dropout = True,
+        seq_kd = False,
+        vllm_sampling_params = None,
+        unsloth_num_chunks = -1,
+        **kwargs,
+    ):
+        if learning_rate < 1e-7: raise FloatingPointError(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
+        if learning_rate > 1: raise OverflowError(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
+        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
+            output_dir = 'unsloth_training_checkpoints'
+            save_strategy = 'no'
+        if dataset_num_proc is None:
+            from multiprocessing import cpu_count
+            dataset_num_proc = cpu_count()
+        
+        super().__init__(
+            output_dir = output_dir,
+            overwrite_output_dir = overwrite_output_dir,
+            do_train = do_train,
+            do_eval = do_eval,
+            do_predict = do_predict,
+            eval_strategy = eval_strategy,
+            prediction_loss_only = prediction_loss_only,
+            per_device_train_batch_size = per_device_train_batch_size,
+            per_device_eval_batch_size = per_device_eval_batch_size,
+            per_gpu_train_batch_size = per_gpu_train_batch_size,
+            per_gpu_eval_batch_size = per_gpu_eval_batch_size,
+            gradient_accumulation_steps = gradient_accumulation_steps,
+            eval_accumulation_steps = eval_accumulation_steps,
+            eval_delay = eval_delay,
+            torch_empty_cache_steps = torch_empty_cache_steps,
+            learning_rate = learning_rate,
+            weight_decay = weight_decay,
+            adam_beta1 = adam_beta1,
+            adam_beta2 = adam_beta2,
+            adam_epsilon = adam_epsilon,
+            max_grad_norm = max_grad_norm,
+            num_train_epochs = num_train_epochs,
+            max_steps = max_steps,
+            lr_scheduler_type = lr_scheduler_type,
+            warmup_ratio = warmup_ratio,
+            warmup_steps = warmup_steps,
+            log_level = log_level,
+            log_level_replica = log_level_replica,
+            log_on_each_node = log_on_each_node,
+            logging_dir = logging_dir,
+            logging_strategy = logging_strategy,
+            logging_first_step = logging_first_step,
+            logging_steps = logging_steps,
+            logging_nan_inf_filter = logging_nan_inf_filter,
+            save_strategy = save_strategy,
+            save_steps = save_steps,
+            save_total_limit = save_total_limit,
+            save_safetensors = save_safetensors,
+            save_on_each_node = save_on_each_node,
+            save_only_model = save_only_model,
+            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
+            no_cuda = no_cuda,
+            use_cpu = use_cpu,
+            use_mps_device = use_mps_device,
+            seed = seed,
+            data_seed = data_seed,
+            jit_mode_eval = jit_mode_eval,
+            use_ipex = use_ipex,
+            bf16 = bf16,
+            fp16 = fp16,
+            fp16_opt_level = fp16_opt_level,
+            half_precision_backend = half_precision_backend,
+            bf16_full_eval = bf16_full_eval,
+            fp16_full_eval = fp16_full_eval,
+            tf32 = tf32,
+            local_rank = local_rank,
+            ddp_backend = ddp_backend,
+            tpu_num_cores = tpu_num_cores,
+            tpu_metrics_debug = tpu_metrics_debug,
+            debug = debug,
+            dataloader_drop_last = dataloader_drop_last,
+            eval_steps = eval_steps,
+            dataloader_num_workers = dataloader_num_workers,
+            dataloader_prefetch_factor = dataloader_prefetch_factor,
+            past_index = past_index,
+            run_name = run_name,
+            disable_tqdm = disable_tqdm,
+            remove_unused_columns = remove_unused_columns,
+            label_names = label_names,
+            load_best_model_at_end = load_best_model_at_end,
+            metric_for_best_model = metric_for_best_model,
+            greater_is_better = greater_is_better,
+            ignore_data_skip = ignore_data_skip,
+            fsdp = fsdp,
+            fsdp_min_num_params = fsdp_min_num_params,
+            fsdp_config = fsdp_config,
+            tp_size = tp_size,
+            fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
+            accelerator_config = accelerator_config,
+            deepspeed = deepspeed,
+            label_smoothing_factor = label_smoothing_factor,
+            optim = optim,
+            optim_args = optim_args,
+            adafactor = adafactor,
+            group_by_length = group_by_length,
+            length_column_name = length_column_name,
+            report_to = report_to,
+            ddp_find_unused_parameters = ddp_find_unused_parameters,
+            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
+            ddp_broadcast_buffers = ddp_broadcast_buffers,
+            dataloader_pin_memory = dataloader_pin_memory,
+            dataloader_persistent_workers = dataloader_persistent_workers,
+            skip_memory_metrics = skip_memory_metrics,
+            use_legacy_prediction_loop = use_legacy_prediction_loop,
+            push_to_hub = push_to_hub,
+            resume_from_checkpoint = resume_from_checkpoint,
+            hub_model_id = hub_model_id,
+            hub_strategy = hub_strategy,
+            hub_token = hub_token,
+            hub_private_repo = hub_private_repo,
+            hub_always_push = hub_always_push,
+            gradient_checkpointing = gradient_checkpointing,
+            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
+            include_inputs_for_metrics = include_inputs_for_metrics,
+            eval_do_concat_batches = eval_do_concat_batches,
+            fp16_backend = fp16_backend,
+            push_to_hub_model_id = push_to_hub_model_id,
+            push_to_hub_organization = push_to_hub_organization,
+            push_to_hub_token = push_to_hub_token,
+            mp_parameters = mp_parameters,
+            auto_find_batch_size = auto_find_batch_size,
+            full_determinism = full_determinism,
+            torchdynamo = torchdynamo,
+            ray_scope = ray_scope,
+            ddp_timeout = ddp_timeout,
+            torch_compile = torch_compile,
+            torch_compile_backend = torch_compile_backend,
+            torch_compile_mode = torch_compile_mode,
+            include_tokens_per_second = include_tokens_per_second,
+            include_num_input_tokens_seen = include_num_input_tokens_seen,
+            neftune_noise_alpha = neftune_noise_alpha,
+            optim_target_modules = optim_target_modules,
+            batch_eval_metrics = batch_eval_metrics,
+            eval_on_start = eval_on_start,
+            use_liger_kernel = use_liger_kernel,
+            eval_use_gather_object = eval_use_gather_object,
+            average_tokens_across_devices = average_tokens_across_devices,
+            model_init_kwargs = model_init_kwargs,
+            use_liger = use_liger,
+            dataset_text_field = dataset_text_field,
+            dataset_kwargs = dataset_kwargs,
+            dataset_num_proc = dataset_num_proc,
+            max_seq_length = max_seq_length,
+            packing = packing,
+            eval_packing = eval_packing,
+            dataset_batch_size = dataset_batch_size,
+            num_of_sequences = num_of_sequences,
+            chars_per_token = chars_per_token,
+            temperature = temperature,
+            lmbda = lmbda,
+            beta = beta,
+            max_new_tokens = max_new_tokens,
+            teacher_model_name_or_path = teacher_model_name_or_path,
+            teacher_model_init_kwargs = teacher_model_init_kwargs,
+            disable_dropout = disable_dropout,
+            seq_kd = seq_kd,**kwargs)
+        self.vllm_sampling_params = vllm_sampling_params
+        self.unsloth_num_chunks = unsloth_num_chunks
+pass
+
+class _UnslothGKDTrainer(SFTTrainer):
+    _tag_names = ["trl", "gkd"]
+
+    def __init__(
+        self,
+        model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
+        teacher_model: Union[PreTrainedModel, nn.Module, str] = None,
+        args: Optional[GKDConfig] = None,
+        data_collator: Optional[DataCollator] = None,  # type: ignore
+        train_dataset: Optional[Dataset] = None,
+        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
+        processing_class: Optional[
+            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
+        ] = None,
+        compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
+        callbacks: Optional[list[TrainerCallback]] = None,
+        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
+        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+        peft_config: Optional["PeftConfig"] = None,
+        formatting_func: Optional[Callable] = None,
+    ):
+        # add remove_unused_columns=False to the dataclass args
+        args.remove_unused_columns = False
+        data_collator = DataCollatorForChatML(tokenizer=processing_class, max_length=args.max_seq_length)
+
+        super().__init__(
+            model,
+            args=args,
+            data_collator=data_collator,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            processing_class=processing_class,
+            compute_metrics=compute_metrics,
+            callbacks=callbacks,
+            optimizers=optimizers,
+            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+            peft_config=peft_config,
+            formatting_func=formatting_func,
+        )
+
+        if args.teacher_model_init_kwargs is None:
+            teacher_model_init_kwargs = {}
+        elif not isinstance(teacher_model, str):
+            raise ValueError(
+                "You passed teacher_model_init_kwargs to the GKDConfig, but your teacher_model is already instantiated."
+            )
+        else:
+            teacher_model_init_kwargs = args.teacher_model_init_kwargs
+            teacher_model_init_kwargs["torch_dtype"] = (
+                teacher_model_init_kwargs["torch_dtype"]
+                if teacher_model_init_kwargs["torch_dtype"] in ["auto", None]
+                else getattr(torch, teacher_model_init_kwargs["torch_dtype"])
+            )
+
+        if isinstance(teacher_model, str):
+            if args.use_liger:
+                teacher_model = AutoLigerKernelForCausalLM.from_pretrained(teacher_model, **teacher_model_init_kwargs)
+            else:
+                teacher_model = AutoModelForCausalLM.from_pretrained(teacher_model, **teacher_model_init_kwargs)
+
+        # Disable dropout in the model
+        if args.disable_dropout:
+            disable_dropout_in_model(self.model)
+
+        if self.is_deepspeed_enabled:
+            self.teacher_model = self._prepare_deepspeed(teacher_model)
+        else:
+            self.teacher_model = self.accelerator.prepare_model(teacher_model, evaluation_mode=True)
+
+        self.lmbda = args.lmbda
+        self.beta = args.beta
+        self.temperature = args.temperature
+        self.seq_kd = args.seq_kd
+
+        self.generation_config = GenerationConfig(
+            max_new_tokens=args.max_new_tokens,
+            temperature=args.temperature,
+            do_sample=True,
+            top_k=0,
+            use_cache=False if args.gradient_checkpointing else True,
+            pad_token_id=self.processing_class.pad_token_id,
+        )
+        # Set custom EOS tokens if they are specified by the model's generation
+        # config. This is important for models with the Llama 3 chat template,
+        # which use special tokens <|eot_id|> and <|eom_id|> to mark the end of
+        # turns or messages.
+        if (
+            hasattr(self.model.generation_config, "eos_token_id")
+            and self.model.generation_config.eos_token_id is not None
+        ):
+            self.generation_config.eos_token_id = self.model.generation_config.eos_token_id
+
+    def _prepare_dataset(self, dataset, *args):
+        # SFTTrainer._prepare_dataset() applies the chat template and rename the messages column to text. However, we
+        # need to keep the messages column as it is. We use the following workaround to keep the messages column.
+        dataset = dataset.add_column("_messages", dataset["messages"])
+        dataset = super()._prepare_dataset(dataset, *args)
+        dataset = dataset.rename_column("_messages", "messages")
+        return dataset
+
+    @staticmethod
+    def generalized_jsd_loss(
+        student_logits, teacher_logits, labels=None, beta=0.5, temperature=1.0, reduction="batchmean"
+    ):
+        """
+        Compute the generalized Jensen-Shannon Divergence loss for knowledge distillation using F.kl_div. See Eq. (1)
+        of https://huggingface.co/papers/2306.13649 for the definition.
+
+        Args:
+            student_logits: Tensor of shape (batch_size, sequence_length, vocab_size)
+            teacher_logits: Tensor of shape (batch_size, sequence_length, vocab_size)
+            labels: Tensor of shape (batch_size, sequence_length) with -100 for padding tokens to ignore when computing loss
+            beta: Interpolation coefficient between 0 and 1 (default: 0.5)
+            temperature: Softmax temperature (default: 1.0)
+            reduction: Specifies the reduction to apply to the output (default: 'batchmean')
+
+        Returns:
+            loss: Scalar tensor with the generalized JSD loss
+        """
+
+        # Apply temperature scaling
+        student_logits = student_logits / temperature
+        teacher_logits = teacher_logits / temperature
+
+        # Compute log probabilities for student and probabilities for teacher
+        student_log_probs = F.log_softmax(student_logits, dim=-1)
+        teacher_log_probs = F.log_softmax(teacher_logits, dim=-1)
+
+        # Compute the log of the mixture distribution
+        # log(a + b) = log(exp(log(a)) + exp(log(b))) -> for mixture
+        beta = torch.tensor(beta, dtype=student_log_probs.dtype)
+        mixture_log_probs = torch.logsumexp(
+            torch.stack([student_log_probs + torch.log(beta), teacher_log_probs + torch.log(1 - beta)]),
+            dim=0,
+        )
+
+        # Compute KL divergences using F.kl_div
+        # PyTorch differs from the standard mathematical definition, so the order of the probability distributions is swapped compared to that defined in the paper.
+        kl_teacher = F.kl_div(mixture_log_probs, teacher_log_probs, reduction="none", log_target=True)
+        kl_student = F.kl_div(mixture_log_probs, student_log_probs, reduction="none", log_target=True)
+
+        # Compute the Generalized Jensen-Shannon Divergence
+        jsd = beta * kl_teacher + (1 - beta) * kl_student
+
+        # Masking
+        if labels is not None:
+            mask = labels != -100
+            jsd = jsd[mask]
+
+        # Apply reduction
+        if reduction == "batchmean":
+            return jsd.sum() / mask.sum() if labels is not None else jsd.sum() / (jsd.size(0) * jsd.size(1))
+        elif reduction == "sum":
+            return jsd.sum()
+        elif reduction == "mean":
+            return jsd.mean()
+        else:
+            return jsd
+
+    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
+        # compute student output
+        outputs_student = model(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+        )
+
+        # compute teacher output in eval mode
+        self.teacher_model.eval()
+        with torch.no_grad():
+            outputs_teacher = self.teacher_model(
+                input_ids=inputs["input_ids"],
+                attention_mask=inputs["attention_mask"],
+            )
+
+        # slice the logits for the generated tokens using the inputs["prompts"] lengths
+        prompt_lengths = inputs["prompts"].shape[1]
+        shifted_student_logits = outputs_student.logits[:, prompt_lengths - 1 : -1, :]
+        shifted_teacher_logits = outputs_teacher.logits[:, prompt_lengths - 1 : -1, :]
+        shifted_labels = inputs["labels"][:, prompt_lengths:]
+
+        # compute loss
+        loss = self.generalized_jsd_loss(
+            student_logits=shifted_student_logits,
+            teacher_logits=shifted_teacher_logits,
+            labels=shifted_labels,
+            beta=self.beta,
+        )
+
+        # empty cache
+        empty_cache()
+
+        # Return loss
+        return (loss, outputs_student) if return_outputs else loss
+
+    @staticmethod
+    def generate_on_policy_outputs(model, inputs, generation_config, pad_token_id=None):
+        # Generate output with respect to the prompt only
+        generated_outputs = model.generate(
+            input_ids=inputs["prompts"],
+            attention_mask=inputs.get("prompt_attention_mask", None),
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+        )
+
+        # Get the generated token IDs
+        generated_tokens = generated_outputs.sequences
+        # Calculate new attention mask
+        new_attention_mask = torch.ones_like(generated_tokens)
+        new_labels = generated_tokens.clone()
+
+        # If there's pad_token_id, set attention mask to 0 for padding tokens
+        if pad_token_id is not None:
+            new_labels[new_labels == pad_token_id] = -100
+            new_attention_mask[generated_tokens == pad_token_id] = 0
+
+        return generated_tokens, new_attention_mask, new_labels
+
+    def training_step(
+        self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None
+    ) -> torch.Tensor:
+        """
+        Perform a training step for the Generalized Knowledge Distillation (GKD) model.
+
+        This method implements the on-policy learning approach described in the GKD paper.
+        With probability `self.lmbda`, it generates new responses using the student model,
+        which are then used for training instead of the original inputs.
+        """
+        if self.seq_kd:
+            with unwrap_model_for_generation(self.teacher_model, self.accelerator) as unwrapped_model:
+                new_input_ids, new_attention_mask, new_labels = self.generate_on_policy_outputs(
+                    unwrapped_model, inputs, self.generation_config, self.processing_class.pad_token_id
+                )
+            inputs["input_ids"] = new_input_ids
+            inputs["attention_mask"] = new_attention_mask
+            inputs["labels"] = new_labels
+        if random.random() <= self.lmbda:
+            with unwrap_model_for_generation(model, self.accelerator) as unwrapped_model:
+                new_input_ids, new_attention_mask, new_labels = self.generate_on_policy_outputs(
+                    unwrapped_model, inputs, self.generation_config, self.processing_class.pad_token_id
+                )
+            inputs["input_ids"] = new_input_ids
+            inputs["attention_mask"] = new_attention_mask
+            inputs["labels"] = new_labels
+
+        loss = super().training_step(model, inputs, num_items_in_batch)
+        return loss
+
+    def _prepare_deepspeed(self, model: PreTrainedModelWrapper):
+        # Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
+        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
+        config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config)
+
+        if model is not None:
+            if hasattr(model, "config"):
+                hidden_size = (
+                    max(model.config.hidden_sizes)
+                    if getattr(model.config, "hidden_sizes", None)
+                    else getattr(model.config, "hidden_size", None)
+                )
+                if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3:
+                    # Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0`
+                    # This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081
+                    config_kwargs.update(
+                        {
+                            "zero_optimization.reduce_bucket_size": hidden_size * hidden_size,
+                            "zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size,
+                            "zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size,
+                        }
+                    )
+
+        # If ZeRO-3 is used, we shard both the active and reference model.
+        # Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0)
+        if config_kwargs["zero_optimization"]["stage"] != 3:
+            config_kwargs["zero_optimization"]["stage"] = 0
+        model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
+        model.eval()
+        return model
+
+    def create_model_card(
+        self,
+        model_name: Optional[str] = None,
+        dataset_name: Optional[str] = None,
+        tags: Union[str, list[str], None] = None,
+    ):
+        """
+        Creates a draft of a model card using the information available to the `Trainer`.
+
+        Args:
+            model_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the model.
+            dataset_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the dataset used for training.
+            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
+                Tags to be associated with the model card.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
+            base_model = self.model.config._name_or_path
+        else:
+            base_model = None
+
+        tags = tags or []
+        if isinstance(tags, str):
+            tags = [tags]
+
+        if hasattr(self.model.config, "unsloth_version"):
+            tags.append("unsloth")
+
+        citation = textwrap.dedent("""\
+        @inproceedings{agarwal2024on-policy,
+            title        = {{On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes}},
+            author       = {Rishabh Agarwal and Nino Vieillard and Yongchao Zhou and Piotr Stanczyk and Sabela Ramos Garea and Matthieu Geist and Olivier Bachem},
+            year         = 2024,
+            booktitle    = {The Twelfth International Conference on Learning Representations, {ICLR} 2024, Vienna, Austria, May 7-11, 2024},
+            publisher    = {OpenReview.net},
+            url          = {https://openreview.net/forum?id=3zKtaqxLhW},
+        }""")
+
+        model_card = generate_model_card(
+            base_model=base_model,
+            model_name=model_name,
+            hub_model_id=self.hub_model_id,
+            dataset_name=dataset_name,
+            tags=tags,
+            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
+            comet_url=get_comet_experiment_url(),
+            trainer_name="GKD",
+            trainer_citation=citation,
+            paper_title="On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes",
+            paper_id="2306.13649",
+        )
+
+        model_card.save(os.path.join(self.args.output_dir, "README.md"))
+class UnslothGKDTrainer(_UnslothGKDTrainer):
+    """
+    
+    """
+    def __init__(
+        self,
+        model = None,
+        teacher_model = None,
+        args = None,
+        data_collator = None,
+        train_dataset = None,
+        eval_dataset = None,
+        processing_class = None,
+        compute_metrics = None,
+        callbacks = None,
+        preprocess_logits_for_metrics = None,
+        peft_config = None,
+        formatting_func = None,
+        **kwargs
+    ):
+        if args is None: args = UnslothGKDConfig()
+        use_bf16 = getattr(args, 'bf16', False)
+        use_fp16 = getattr(args, 'fp16', False)
+        force_float32 = False
+        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':
+            print('Unsloth: Switching to float32 training since model cannot work with float16')
+            force_float32 = True
+        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
+        dtype = getattr(model.config, 'torch_dtype', None)
+        if dtype is None: dtype = model.get_input_embeddings().dtype
+        from unsloth_zoo.utils import _get_dtype
+        dtype = _get_dtype(dtype)
+        float16 = dtype == torch.float16
+        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
+        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
+        if force_float32:
+            args.fp16 = False
+            args.bf16 = False
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
+        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
+            args.fp16 = float16
+            args.bf16 = not float16
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
+        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
+            args.eval_strategy = 'steps'
+            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
+        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
+        if ga_steps is not None and ga_steps > 1:
+            from transformers import __version__ as transformers_version
+            if Version(transformers_version) <= Version('4.45.2'):
+                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
+                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
+        if getattr(args, 'eval_strategy', 'no') != 'no':
+            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
+            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
+            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
+        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
+        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
+        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
+        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
+        if force_float32:
+            args.bf16_full_eval = False
+            args.fp16_full_eval = False
+        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
+            args.bf16_full_eval = True
+            args.fp16_full_eval = False
+        elif not bf16_full_eval and not fp16_full_eval:
+            args.bf16_full_eval = args.bf16
+            args.fp16_full_eval = args.fp16
+        _output_logits = False
+        if locals().get('compute_metrics', None) is not None: _output_logits = True
+        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
+        if _output_logits:
+            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
+        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
+            pass
+        else:
+            model_max_seq_length = getattr(model, 'max_seq_length', None)
+            args_max_seq_length  = getattr(args,  'max_seq_length', None)
+            if args_max_seq_length is None and model_max_seq_length is not None:
+                max_seq_length = model.max_seq_length
+                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
+        if model is not None and hasattr(model, 'for_training'):
+            model.for_training()
+        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
+        if 'processing_class' in locals():
+            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
+            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
+        __tokenizer = processing_class if 'processing_class' in locals() else tokenizer
+        from unsloth_zoo.vision_utils import UnslothVisionDataCollator
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
+                data_collator = DataCollatorForLanguageModeling(__tokenizer, mlm = False)
+            elif isinstance(data_collator, DataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
+                data_collator = DataCollatorForSeq2Seq(__tokenizer)
+        else:
+            if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
+            if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
+            if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
+                if isinstance(data_collator, DataCollatorForSeq2Seq):
+                    data_collator = DataCollatorForSeq2Seq(__tokenizer.tokenizer)
+                else:
+                    data_collator = DataCollatorForLanguageModeling(__tokenizer.tokenizer, mlm = False)
+        other_metrics = []
+        
+        from unsloth_zoo.logging_utils import PatchRLStatistics
+        PatchRLStatistics('gkd_trainer', other_metrics)
+        
+        super().__init__(
+            model = model,
+            teacher_model = teacher_model,
+            args = args,
+            data_collator = data_collator,
+            train_dataset = train_dataset,
+            eval_dataset = eval_dataset,
+            processing_class = processing_class,
+            compute_metrics = compute_metrics,
+            callbacks = callbacks,
+            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
+            peft_config = peft_config,
+            formatting_func = formatting_func,**kwargs)
+        if hasattr(self, 'neftune_hook_handle'):
+            self.neftune_hook_handle.remove()
+            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
+        if getattr(args, 'neftune_noise_alpha', None) is not None:
+            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
+        pass
+        
+pass

unsloth_compiled_cache/UnslothGRPOTrainer.py

@@ -0,0 +1,1432 @@
+"""
+2025.4.1
+2025.4.1
+4.51.3
+0.15.2
+__UNSLOTH_VERSIONING__
+"""
+from torch import Tensor
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from trl.trainer.grpo_trainer import (Any, AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, Dataset, GRPOConfig, GRPOTrainer, GenerationConfig, IterableDataset, Optional, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, RepeatRandomSampler, RewardFunc, Sampler, SyncRefModelCallback, Trainer, TrainerCallback, Union, apply_chat_template, broadcast_object_list, create_reference_model, defaultdict, gather, gather_object, generate_model_card, get_comet_experiment_url, is_conversational, is_deepspeed_zero3_enabled, is_peft_model, is_wandb_available, maybe_apply_chat_template, nn, os, pad, prepare_deepspeed, set_seed, textwrap, torch, transformers, unwrap_model_for_generation, version, warnings, os, torch, transformers, Any, Union, apply_chat_template, broadcast_object_list, gather, gather_object, is_conversational, maybe_apply_chat_template, nn, os, pad, torch, unwrap_model_for_generation, GRPOTrainer, Trainer, gather, os, torch)
+
+
+import os
+from typing import *
+from dataclasses import dataclass, field
+from packaging.version import Version
+import torch
+import numpy as np
+from contextlib import nullcontext
+from torch.nn import functional as F
+from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
+
+torch_compile_options = {
+    "epilogue_fusion"   : True,
+    "max_autotune"      : False,
+    "shape_padding"     : True,
+    "trace.enabled"     : False,
+    "triton.cudagraphs" : False,
+}
+
+@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
+def selective_log_softmax(logits, index):
+    logits = logits.to(torch.float32)
+    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
+    # loop to reduce peak mem consumption
+    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
+    logsumexp_values = torch.logsumexp(logits, dim = -1)
+    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
+    return per_token_logps
+
+def grpo_compute_loss(old_logits, new_logits, input_ids, mask, beta, advantages):
+    # All Unsloth Zoo code licensed under LGPLv3
+    old_logits = old_logits.to(torch.float32)
+    new_logits = new_logits.to(torch.float32)
+    input_ids  = input_ids.unsqueeze(-1)
+
+    # x_i - logsumexp(x_i)
+    old_x = torch.gather(old_logits, dim = -1, index = input_ids).squeeze(-1)
+    new_x = torch.gather(new_logits, dim = -1, index = input_ids).squeeze(-1)
+    old = old_x - torch.logsumexp(old_logits, dim = -1)
+    new = new_x - torch.logsumexp(new_logits, dim = -1)
+
+    # Reverse KL
+    kl_i = torch.exp(old - new) - (old - new) - 1.0
+    # Full correct reverse KL divergence?? Missing term maybe?
+    # kl_i = torch.exp(new) * kl_i
+
+    # Below is forward KL (normal KL)
+    # kl_i = torch.exp(old) * (old - new)
+
+    # Must detach - otherwise gradients are not propagated correctly!
+    # exp(x - x) == 1
+    loss_i = torch.exp(new - new.detach()) * advantages.unsqueeze(1)
+    loss_i = -(loss_i - beta * kl_i)
+
+    mask = mask.to(torch.float32)
+    n_mask_per_reward = mask.sum(1)
+
+    # See https://github.com/huggingface/trl/pull/2881
+    loss_per_reward = (loss_i * mask).sum(1) / n_mask_per_reward
+    loss = loss_per_reward.mean()
+    # loss = (loss_i * mask).sum() / mask.sum()
+    
+    # Get metrics as well which are folded
+    with torch.inference_mode():
+        completion_length = n_mask_per_reward.mean()
+        mean_kl_per_reward = (kl_i * mask).sum(1) / n_mask_per_reward
+        mean_kl = mean_kl_per_reward.mean()
+    pass
+    return loss, completion_length, mean_kl
+
+class UnslothEfficientGRPO(torch.autograd.Function):
+    # All Unsloth Zoo code licensed under LGPLv3
+    @staticmethod
+    def forward(ctx, _new_hidden_states, _old_hidden_states, lm_head, _input_ids, _mask, _advantages, beta, scaler = None, n_chunks = 1):
+        def compute_loss(new_hidden_states, old_hidden_states, input_ids, mask, advantages, scaling):
+            new_logits = torch.matmul(new_hidden_states, lm_head.t())
+            new_logits = new_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred
+            old_logits = torch.matmul(old_hidden_states, lm_head.t())
+            old_logits = old_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred
+            loss, completion_length, mean_kl = grpo_compute_loss(
+                old_logits, new_logits, input_ids, mask, beta, advantages,
+            )
+            # Scale loss if needed for mixed precision training
+            scaled_loss = loss * scaling
+            # Must add .loss.detach otherwise autograd uses 2x VRAM
+            return scaled_loss, (loss.detach(), completion_length, mean_kl,)
+        pass
+
+        device =_new_hidden_states.device
+        grad_inputs = torch.empty_like(_new_hidden_states)
+        accumulated_loss              = torch.zeros(1, device = device)
+        accumulated_completion_length = torch.zeros(1, device = device)
+        accumulated_mean_kl           = torch.zeros(1, device = device)
+
+        def accumulate_chunk(new_hidden_states_j, old_hidden_states_j, input_ids_j, mask_j, advantages_j, scaling):
+            (chunk_grad_input,), (chunk_loss, (unscaled_loss, chunk_completion_length, chunk_mean_kl,)) = torch.func.grad_and_value(
+                compute_loss,
+                argnums = (0,),
+                has_aux = True,
+            )(new_hidden_states_j, old_hidden_states_j, input_ids_j, mask_j, advantages_j, scaling)
+            accumulated_loss             .add_(unscaled_loss)
+            accumulated_completion_length.add_(chunk_completion_length)
+            accumulated_mean_kl          .add_(chunk_mean_kl)
+            return chunk_grad_input
+        pass
+
+        accumulate_chunk = torch.compile(
+            accumulate_chunk,
+            fullgraph = True,
+            options = torch_compile_options,
+        )
+
+        grad_inputs_chunks = torch.chunk(grad_inputs,        chunks = n_chunks, dim = 0)
+        new_hidden_states  = torch.chunk(_new_hidden_states, chunks = n_chunks, dim = 0)
+        old_hidden_states  = torch.chunk(_old_hidden_states, chunks = n_chunks, dim = 0)
+        input_ids          = torch.chunk(_input_ids,         chunks = n_chunks, dim = 0)
+        mask               = torch.chunk(_mask,              chunks = n_chunks, dim = 0)
+        advantages         = torch.chunk(_advantages,        chunks = n_chunks, dim = 0)
+
+        # Get mixed precision scaling if seen
+        scaling = scaler.get_scale() if scaler is not None else 1.0
+
+        # Force torch.compile to use dynamic shapes for seqlen dim
+        mark_dynamic = lambda x: torch._dynamo.mark_dynamic(x, 1)
+
+        for (grad_inputs_j, new_hidden_states_j, old_hidden_states_j, input_ids_j, mask_j, advantages_j,) in \
+            zip(grad_inputs_chunks, new_hidden_states, old_hidden_states, input_ids, mask, advantages):
+
+            mark_dynamic(new_hidden_states_j)
+            mark_dynamic(old_hidden_states_j)
+            mark_dynamic(input_ids_j)
+            mark_dynamic(mask_j)
+
+            grad_inputs_j.copy_(
+                accumulate_chunk(new_hidden_states_j, old_hidden_states_j, input_ids_j, mask_j, advantages_j, scaling)
+            )
+        pass
+
+        grad_inputs                  .div_(n_chunks)
+        accumulated_loss             .div_(n_chunks)
+        accumulated_completion_length.div_(n_chunks)
+        accumulated_mean_kl          .div_(n_chunks)
+        ctx.save_for_backward(grad_inputs)
+
+        return (
+            accumulated_loss,
+            accumulated_completion_length,
+            accumulated_mean_kl,
+        )
+    pass
+
+    @staticmethod
+    def backward(ctx, grad_output, dcompletion_length, dmean_kl):
+        (grad_input,) = ctx.saved_tensors
+        return (grad_input, None, None, None, None, None, None, None, None,)
+    pass
+
+def grpo_accumulated_loss(
+    trainer,
+    input_ids,
+    logits_to_keep,
+    completion_mask,
+    advantages,
+    n_chunks = -1,
+):
+    # All Unsloth Zoo code licensed under LGPLv3
+    bsz, qlen = input_ids.shape
+    # Find closest multiple
+    factors = [i for i in range(1, bsz + 1) if bsz % i == 0]
+    if n_chunks == -1: n_chunks = bsz
+    n_chunks = factors[min(np.searchsorted(factors, n_chunks), len(factors)-1)]
+
+    mixed_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16
+    os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1"
+
+    completion_input_ids = input_ids[:, -logits_to_keep:]
+    lm_head = trainer.model.get_output_embeddings().weight
+
+    with torch.amp.autocast(device_type = "cuda", dtype = mixed_dtype):
+        with torch.inference_mode(), trainer.accelerator.unwrap_model(trainer.model, keep_fp32_wrapper = False).disable_adapter():
+            old_hidden_states = trainer.model(input_ids = input_ids, logits_to_keep = logits_to_keep + 1).logits
+        pass
+
+        new_hidden_states = trainer.model(input_ids = input_ids, logits_to_keep = logits_to_keep + 1).logits
+        
+        loss, completion_length, mean_kl = UnslothEfficientGRPO.apply(
+            new_hidden_states, old_hidden_states, lm_head,
+            completion_input_ids, completion_mask, advantages, trainer.beta,
+            trainer.accelerator.scaler,
+            n_chunks, 
+        )
+        return loss, completion_length, mean_kl
+
+        # Old non efficient code path
+        new_logits = torch.matmul(new_hidden_states, lm_head.t())
+        new_logits = new_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred
+        old_logits = torch.matmul(old_hidden_states, lm_head.t())
+        old_logits = old_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred
+        loss, completion_length, mean_kl = grpo_compute_loss(
+            old_logits, new_logits, completion_input_ids, completion_mask, trainer.beta, advantages,
+        )
+        return loss, completion_length, mean_kl
+    pass
+
+@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options)
+def grpo_compute_loss_slow(old_logits, new_logits, input_ids, mask, beta, advantages):
+    # All Unsloth Zoo code licensed under LGPLv3
+    old_logits = old_logits.to(torch.float32)
+    new_logits = new_logits.to(torch.float32)
+    input_ids  = input_ids.unsqueeze(-1)
+
+    # x_i - logsumexp(x_i)
+    old_x = torch.gather(old_logits, dim = -1, index = input_ids).squeeze(-1)
+    new_x = torch.gather(new_logits, dim = -1, index = input_ids).squeeze(-1)
+    old = old_x - torch.logsumexp(old_logits, dim = -1)
+    new = new_x - torch.logsumexp(new_logits, dim = -1)
+
+    # Reverse KL
+    kl_i = torch.exp(old - new) - (old - new) - 1.0
+    # Full correct reverse KL divergence?? Missing term maybe?
+    # kl_i = torch.exp(new) * kl_i
+
+    # Below is forward KL (normal KL)
+    # kl_i = torch.exp(old) * (old - new)
+
+    # Must detach - otherwise gradients are not propagated correctly!
+    # exp(x - x) == 1
+    loss_i = torch.exp(new - new.detach()) * advantages.unsqueeze(1)
+    loss_i = -(loss_i - beta * kl_i)
+
+    mask = mask.to(torch.float32)
+    n_mask_per_reward = mask.sum(1)
+
+    # See https://github.com/huggingface/trl/pull/2881
+    loss_per_reward = (loss_i * mask).sum(1) / n_mask_per_reward
+    loss = loss_per_reward.mean()
+    # loss = (loss_i * mask).sum() / mask.sum()
+    
+    # Get metrics as well which are folded
+    with torch.inference_mode():
+        completion_length = n_mask_per_reward.mean()
+        mean_kl_per_reward = (kl_i * mask).sum(1) / n_mask_per_reward
+        mean_kl = mean_kl_per_reward.mean()
+    pass
+    return loss, completion_length, mean_kl
+
+def vLLMSamplingParams(**kwargs):
+    from vllm import SamplingParams
+    sampling_params = SamplingParams(**kwargs)
+    sampling_params._set_kwargs = kwargs
+    return sampling_params
+@dataclass
+class UnslothGRPOConfig(GRPOConfig):
+    """
+    
+    Configuration class for the [`GRPOTrainer`].
+
+    Only the parameters specific to GRPO training are listed here. For details on other parameters, refer to the
+    [`~transformers.TrainingArguments`] documentation.
+
+    Using [`~transformers.HfArgumentParser`] we can turn this class into
+    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
+    command line.
+
+    Parameters:
+        > Parameters that control the model and reference model
+
+        model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
+            Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
+            argument of the [`GRPOTrainer`] is provided as a string.
+
+        > Parameters that control the data preprocessing
+
+        remove_unused_columns (`bool`, *optional*, defaults to `False`):
+            Whether to only keep the column `"prompt"` in the dataset. If you use a custom reward function that
+            requires any column other than `"prompts"` and `"completions"`, you should keep this to `False`.
+        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
+            Maximum length of the prompt. If the prompt is longer than this value, it will be truncated left.
+        num_generations (`int` or `None`, *optional*, defaults to `8`):
+            Number of generations per prompt to sample. The global batch size (num_processes * per_device_batch_size)
+            must be divisible by this value.
+        temperature (`float`, *optional*, defaults to `0.9`):
+            Temperature for sampling. The higher the temperature, the more random the completions.
+        max_completion_length (`int` or `None`, *optional*, defaults to `256`):
+            Maximum length of the generated completion.
+        ds3_gather_for_generation (`bool`, *optional*, defaults to `True`):
+            This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation,
+            improving generation speed. However, disabling this option allows training models that exceed the VRAM
+            capacity of a single GPU, albeit at the cost of slower generation. Disabling this option is not compatible
+            with vLLM generation.
+
+        > Parameters that control generation acceleration powered by vLLM
+
+        use_vllm (`bool`, *optional*, defaults to `False`):
+            Whether to use vLLM for generating completions. If set to `True`, ensure that a GPU is kept unused for
+            training, as vLLM will require one for generation. vLLM must be installed (`pip install vllm`).
+        vllm_device (`str`, *optional*, defaults to `"auto"`):
+            Device where vLLM generation will run, e.g. `"cuda:1"`. If set to `"auto"` (default), the system will
+            automatically select the next available GPU after the last one used for training. This assumes that
+            training has not already occupied all available GPUs. If only one device is available, the device will be
+            shared between both training and vLLM.
+        vllm_gpu_memory_utilization (`float`, *optional*, defaults to `0.9`):
+            Ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV cache on the
+            device dedicated to generation powered by vLLM. Higher values will increase the KV cache size and thus
+            improve the model's throughput. However, if the value is too high, it may cause out-of-memory (OOM) errors
+            during initialization.
+        vllm_dtype (`str`, *optional*, defaults to `"auto"`):
+            Data type to use for vLLM generation. If set to `"auto"`, the data type will be automatically determined
+            based on the model configuration. Find the supported values in the vLLM documentation.
+        vllm_max_model_len (`int` or `None`, *optional*, defaults to `None`):
+            If set, the `max_model_len` to use for vLLM. This could be useful when running with reduced
+            `vllm_gpu_memory_utilization`, leading to a reduced KV cache size. If not set, vLLM will use the model
+            context size, which might be much larger than the KV cache, leading to inefficiencies.
+
+        > Parameters that control the training
+
+        learning_rate (`float`, *optional*, defaults to `1e-6`):
+            Initial learning rate for [`AdamW`] optimizer. The default value replaces that of
+            [`~transformers.TrainingArguments`].
+        beta (`float`, *optional*, defaults to `0.04`):
+            KL coefficient.
+        reward_weights (`list[float]` or `None`, *optional*, defaults to `None`):
+            Weights for each reward function. Must match the number of reward functions. If `None`, all rewards are
+            weighted equally with weight `1.0`.
+        sync_ref_model (`bool`, *optional*, defaults to `False`):
+            Whether to synchronize the reference model with the active model every `ref_model_sync_steps` steps, using
+            the `ref_model_mixup_alpha` parameter. This synchronization originites from the
+            [TR-DPO](https://huggingface.co/papers/2404.09656) paper.
+        ref_model_mixup_alpha (`float`, *optional*, defaults to `0.9`):
+            α parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which controls the mix
+            between the current policy and the previous reference policy during updates. The reference policy is
+            updated according to the equation: `π_ref = α * π_θ + (1 - α) * π_ref_prev`. To use this parameter, you
+            must set `sync_ref_model=True`.
+        ref_model_sync_steps (`int`, *optional*, defaults to `64`):
+            τ parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which determines how
+            frequently the current policy is synchronized with the reference policy. To use this parameter, you must
+            set `sync_ref_model=True`.
+
+        > Parameters that control the logging
+
+        log_completions (`bool`, *optional*, defaults to `False`):
+            Whether to log the completions during training.
+    
+    """
+    vllm_sampling_params: Optional[Any] = field(
+        default = None,
+        metadata = {'help': 'vLLM SamplingParams'},
+    )
+    unsloth_num_chunks : Optional[int] = field(
+        default = -1,
+        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
+    )
+    def __init__(
+        self,
+        output_dir = None,
+        overwrite_output_dir = None,
+        do_train = False,
+        do_eval = False,
+        do_predict = False,
+        eval_strategy = 'no',
+        prediction_loss_only = False,
+        per_device_train_batch_size = 4,
+        per_device_eval_batch_size = 4,
+        per_gpu_train_batch_size = None,
+        per_gpu_eval_batch_size = None,
+        gradient_accumulation_steps = 2,
+        eval_accumulation_steps = 2,
+        eval_delay = 0,
+        torch_empty_cache_steps = 250,
+        learning_rate = 5e-05,
+        weight_decay = 0.01,
+        adam_beta1 = 0.9,
+        adam_beta2 = 0.999,
+        adam_epsilon = 1e-08,
+        max_grad_norm = 1.0,
+        num_train_epochs = 3.0,
+        max_steps = -1,
+        lr_scheduler_type = 'linear',
+        warmup_ratio = 0.1,
+        warmup_steps = 0,
+        log_level = 'passive',
+        log_level_replica = 'warning',
+        log_on_each_node = True,
+        logging_dir = None,
+        logging_strategy = 'steps',
+        logging_first_step = False,
+        logging_steps = 1,
+        logging_nan_inf_filter = False,
+        save_strategy = 'steps',
+        save_steps = 500,
+        save_total_limit = None,
+        save_safetensors = True,
+        save_on_each_node = False,
+        save_only_model = False,
+        restore_callback_states_from_checkpoint = False,
+        no_cuda = False,
+        use_cpu = False,
+        use_mps_device = False,
+        seed = 3407,
+        data_seed = 3407,
+        jit_mode_eval = False,
+        use_ipex = False,
+        bf16 = False,
+        fp16 = False,
+        fp16_opt_level = 'O1',
+        half_precision_backend = 'auto',
+        bf16_full_eval = False,
+        fp16_full_eval = False,
+        tf32 = None,
+        local_rank = -1,
+        ddp_backend = None,
+        tpu_num_cores = None,
+        tpu_metrics_debug = False,
+        debug = '',
+        dataloader_drop_last = False,
+        eval_steps = None,
+        dataloader_num_workers = 0,
+        dataloader_prefetch_factor = None,
+        past_index = -1,
+        run_name = None,
+        disable_tqdm = None,
+        remove_unused_columns = False,
+        label_names = None,
+        load_best_model_at_end = False,
+        metric_for_best_model = None,
+        greater_is_better = None,
+        ignore_data_skip = False,
+        fsdp = '',
+        fsdp_min_num_params = 0,
+        fsdp_config = None,
+        tp_size = 0,
+        fsdp_transformer_layer_cls_to_wrap = None,
+        accelerator_config = None,
+        deepspeed = None,
+        label_smoothing_factor = 0.0,
+        optim = 'adamw_8bit',
+        optim_args = None,
+        adafactor = False,
+        group_by_length = False,
+        length_column_name = 'length',
+        report_to = None,
+        ddp_find_unused_parameters = None,
+        ddp_bucket_cap_mb = None,
+        ddp_broadcast_buffers = None,
+        dataloader_pin_memory = True,
+        dataloader_persistent_workers = False,
+        skip_memory_metrics = True,
+        use_legacy_prediction_loop = False,
+        push_to_hub = False,
+        resume_from_checkpoint = None,
+        hub_model_id = None,
+        hub_strategy = 'every_save',
+        hub_token = None,
+        hub_private_repo = None,
+        hub_always_push = False,
+        gradient_checkpointing = False,
+        gradient_checkpointing_kwargs = None,
+        include_inputs_for_metrics = False,
+        eval_do_concat_batches = True,
+        fp16_backend = 'auto',
+        push_to_hub_model_id = None,
+        push_to_hub_organization = None,
+        push_to_hub_token = None,
+        mp_parameters = '',
+        auto_find_batch_size = False,
+        full_determinism = False,
+        torchdynamo = None,
+        ray_scope = 'last',
+        ddp_timeout = 1800,
+        torch_compile = False,
+        torch_compile_backend = None,
+        torch_compile_mode = None,
+        include_tokens_per_second = False,
+        include_num_input_tokens_seen = False,
+        neftune_noise_alpha = None,
+        optim_target_modules = None,
+        batch_eval_metrics = False,
+        eval_on_start = False,
+        use_liger_kernel = False,
+        eval_use_gather_object = False,
+        average_tokens_across_devices = False,
+        model_init_kwargs = None,
+        max_prompt_length = 512,
+        num_generations = 8,
+        temperature = 0.9,
+        max_completion_length = 256,
+        ds3_gather_for_generation = True,
+        use_vllm = False,
+        vllm_device = 'auto',
+        vllm_gpu_memory_utilization = 0.9,
+        vllm_dtype = 'auto',
+        vllm_max_model_len = None,
+        beta = 0.04,
+        reward_weights = None,
+        sync_ref_model = False,
+        ref_model_mixup_alpha = 0.9,
+        ref_model_sync_steps = 64,
+        log_completions = False,
+        vllm_sampling_params = None,
+        unsloth_num_chunks = -1,
+        **kwargs,
+    ):
+        if learning_rate < 1e-7: raise FloatingPointError(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
+        if learning_rate > 1: raise OverflowError(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
+        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
+            output_dir = 'unsloth_training_checkpoints'
+            save_strategy = 'no'
+        div = per_device_train_batch_size // num_generations
+        if div * num_generations != per_device_train_batch_size:
+            print('Unsloth: We now expect `per_device_train_batch_size` to be a multiple of `num_generations`.\nWe will change the batch size of ' + str(per_device_train_batch_size) + ' to the `num_generations` of ' + str(num_generations))
+            per_device_train_batch_size = num_generations
+        
+        super().__init__(
+            output_dir = output_dir,
+            overwrite_output_dir = overwrite_output_dir,
+            do_train = do_train,
+            do_eval = do_eval,
+            do_predict = do_predict,
+            eval_strategy = eval_strategy,
+            prediction_loss_only = prediction_loss_only,
+            per_device_train_batch_size = per_device_train_batch_size,
+            per_device_eval_batch_size = per_device_eval_batch_size,
+            per_gpu_train_batch_size = per_gpu_train_batch_size,
+            per_gpu_eval_batch_size = per_gpu_eval_batch_size,
+            gradient_accumulation_steps = gradient_accumulation_steps,
+            eval_accumulation_steps = eval_accumulation_steps,
+            eval_delay = eval_delay,
+            torch_empty_cache_steps = torch_empty_cache_steps,
+            learning_rate = learning_rate,
+            weight_decay = weight_decay,
+            adam_beta1 = adam_beta1,
+            adam_beta2 = adam_beta2,
+            adam_epsilon = adam_epsilon,
+            max_grad_norm = max_grad_norm,
+            num_train_epochs = num_train_epochs,
+            max_steps = max_steps,
+            lr_scheduler_type = lr_scheduler_type,
+            warmup_ratio = warmup_ratio,
+            warmup_steps = warmup_steps,
+            log_level = log_level,
+            log_level_replica = log_level_replica,
+            log_on_each_node = log_on_each_node,
+            logging_dir = logging_dir,
+            logging_strategy = logging_strategy,
+            logging_first_step = logging_first_step,
+            logging_steps = logging_steps,
+            logging_nan_inf_filter = logging_nan_inf_filter,
+            save_strategy = save_strategy,
+            save_steps = save_steps,
+            save_total_limit = save_total_limit,
+            save_safetensors = save_safetensors,
+            save_on_each_node = save_on_each_node,
+            save_only_model = save_only_model,
+            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
+            no_cuda = no_cuda,
+            use_cpu = use_cpu,
+            use_mps_device = use_mps_device,
+            seed = seed,
+            data_seed = data_seed,
+            jit_mode_eval = jit_mode_eval,
+            use_ipex = use_ipex,
+            bf16 = bf16,
+            fp16 = fp16,
+            fp16_opt_level = fp16_opt_level,
+            half_precision_backend = half_precision_backend,
+            bf16_full_eval = bf16_full_eval,
+            fp16_full_eval = fp16_full_eval,
+            tf32 = tf32,
+            local_rank = local_rank,
+            ddp_backend = ddp_backend,
+            tpu_num_cores = tpu_num_cores,
+            tpu_metrics_debug = tpu_metrics_debug,
+            debug = debug,
+            dataloader_drop_last = dataloader_drop_last,
+            eval_steps = eval_steps,
+            dataloader_num_workers = dataloader_num_workers,
+            dataloader_prefetch_factor = dataloader_prefetch_factor,
+            past_index = past_index,
+            run_name = run_name,
+            disable_tqdm = disable_tqdm,
+            remove_unused_columns = remove_unused_columns,
+            label_names = label_names,
+            load_best_model_at_end = load_best_model_at_end,
+            metric_for_best_model = metric_for_best_model,
+            greater_is_better = greater_is_better,
+            ignore_data_skip = ignore_data_skip,
+            fsdp = fsdp,
+            fsdp_min_num_params = fsdp_min_num_params,
+            fsdp_config = fsdp_config,
+            tp_size = tp_size,
+            fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
+            accelerator_config = accelerator_config,
+            deepspeed = deepspeed,
+            label_smoothing_factor = label_smoothing_factor,
+            optim = optim,
+            optim_args = optim_args,
+            adafactor = adafactor,
+            group_by_length = group_by_length,
+            length_column_name = length_column_name,
+            report_to = report_to,
+            ddp_find_unused_parameters = ddp_find_unused_parameters,
+            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
+            ddp_broadcast_buffers = ddp_broadcast_buffers,
+            dataloader_pin_memory = dataloader_pin_memory,
+            dataloader_persistent_workers = dataloader_persistent_workers,
+            skip_memory_metrics = skip_memory_metrics,
+            use_legacy_prediction_loop = use_legacy_prediction_loop,
+            push_to_hub = push_to_hub,
+            resume_from_checkpoint = resume_from_checkpoint,
+            hub_model_id = hub_model_id,
+            hub_strategy = hub_strategy,
+            hub_token = hub_token,
+            hub_private_repo = hub_private_repo,
+            hub_always_push = hub_always_push,
+            gradient_checkpointing = gradient_checkpointing,
+            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
+            include_inputs_for_metrics = include_inputs_for_metrics,
+            eval_do_concat_batches = eval_do_concat_batches,
+            fp16_backend = fp16_backend,
+            push_to_hub_model_id = push_to_hub_model_id,
+            push_to_hub_organization = push_to_hub_organization,
+            push_to_hub_token = push_to_hub_token,
+            mp_parameters = mp_parameters,
+            auto_find_batch_size = auto_find_batch_size,
+            full_determinism = full_determinism,
+            torchdynamo = torchdynamo,
+            ray_scope = ray_scope,
+            ddp_timeout = ddp_timeout,
+            torch_compile = torch_compile,
+            torch_compile_backend = torch_compile_backend,
+            torch_compile_mode = torch_compile_mode,
+            include_tokens_per_second = include_tokens_per_second,
+            include_num_input_tokens_seen = include_num_input_tokens_seen,
+            neftune_noise_alpha = neftune_noise_alpha,
+            optim_target_modules = optim_target_modules,
+            batch_eval_metrics = batch_eval_metrics,
+            eval_on_start = eval_on_start,
+            use_liger_kernel = use_liger_kernel,
+            eval_use_gather_object = eval_use_gather_object,
+            average_tokens_across_devices = average_tokens_across_devices,
+            model_init_kwargs = model_init_kwargs,
+            max_prompt_length = max_prompt_length,
+            num_generations = num_generations,
+            temperature = temperature,
+            max_completion_length = max_completion_length,
+            ds3_gather_for_generation = ds3_gather_for_generation,
+            use_vllm = use_vllm,
+            vllm_device = vllm_device,
+            vllm_gpu_memory_utilization = vllm_gpu_memory_utilization,
+            vllm_dtype = vllm_dtype,
+            vllm_max_model_len = vllm_max_model_len,
+            beta = beta,
+            reward_weights = reward_weights,
+            sync_ref_model = sync_ref_model,
+            ref_model_mixup_alpha = ref_model_mixup_alpha,
+            ref_model_sync_steps = ref_model_sync_steps,
+            log_completions = log_completions,**kwargs)
+        self.vllm_sampling_params = vllm_sampling_params
+        self.unsloth_num_chunks = unsloth_num_chunks
+pass
+
+class _UnslothGRPOTrainer(Trainer):
+    """"""
+
+    _tag_names = ["trl", "grpo"]
+
+    def __init__(
+        self,
+        model: Union[str, PreTrainedModel],
+        reward_funcs: Union[RewardFunc, list[RewardFunc]],
+        args: GRPOConfig = None,
+        train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
+        eval_dataset: Optional[Union[Dataset, IterableDataset, dict[str, Union[Dataset, IterableDataset]]]] = None,
+        processing_class: Optional[PreTrainedTokenizerBase] = None,
+        reward_processing_classes: Optional[Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]] = None,
+        callbacks: Optional[list[TrainerCallback]] = None,
+        optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None),
+        peft_config: Optional["PeftConfig"] = None,
+    ):
+
+        if hasattr(model, 'vllm_engine') and hasattr(args, 'use_vllm') and (getattr(args, 'use_vllm', False) == False): args.use_vllm = True
+        # Args
+        if args is None:
+            model_name = model if isinstance(model, str) else model.config._name_or_path
+            model_name = model_name.split("/")[-1]
+            args = GRPOConfig(f"{model_name}-GRPO")
+
+        # Models
+        # Trained model
+        model_init_kwargs = args.model_init_kwargs or {}
+        if isinstance(model, str):
+            model_id = model
+            torch_dtype = model_init_kwargs.get("torch_dtype")
+            if isinstance(torch_dtype, torch.dtype) or torch_dtype == "auto" or torch_dtype is None:
+                pass  # torch_dtype is already a torch.dtype or "auto" or None
+            elif isinstance(torch_dtype, str):  # it's a str, but not "auto"
+                torch_dtype = getattr(torch, torch_dtype)
+                model_init_kwargs["torch_dtype"] = torch_dtype
+            else:
+                raise ValueError(
+                    "Invalid `torch_dtype` passed to `GRPOConfig`. Expected either 'auto' or a string representing "
+                    f"a `torch.dtype` (e.g., 'float32'), but got {torch_dtype}."
+                )
+            # Disable caching if gradient checkpointing is enabled (not supported)
+            model_init_kwargs["use_cache"] = (
+                False if args.gradient_checkpointing else model_init_kwargs.get("use_cache")
+            )
+            model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)
+        else:
+            model_id = model.config._name_or_path
+            if args.model_init_kwargs is not None:
+                raise ValueError(
+                    "You passed `model_init_kwargs` to the `GRPOConfig`, but your model is already instantiated. "
+                    "This argument can only be used when the `model` argument is a string."
+                )
+
+        if False:
+            model = model
+
+        # Reference model
+        if is_deepspeed_zero3_enabled():
+            self.ref_model = AutoModelForCausalLM.from_pretrained(model_id, **model_init_kwargs)
+        elif not is_peft_model(model):
+            # If PEFT configuration is not provided, create a reference model based on the initial model.
+            self.ref_model = create_reference_model(model)
+        else:
+            # If PEFT is used, the reference model is not needed since the adapter can be disabled
+            # to revert to the initial model.
+            self.ref_model = None
+
+        # Processing class
+        if processing_class is None:
+            processing_class = AutoTokenizer.from_pretrained(model.config._name_or_path, padding_side="left")
+
+        # Reward functions
+        if not isinstance(reward_funcs, list):
+            reward_funcs = [reward_funcs]
+        for i, reward_func in enumerate(reward_funcs):
+            if isinstance(reward_func, str):
+                reward_funcs[i] = AutoModelForSequenceClassification.from_pretrained(
+                    reward_func, num_labels=1, **model_init_kwargs
+                )
+        self.reward_funcs = reward_funcs
+
+        # Reward weights
+        if args.reward_weights is not None:
+            if len(args.reward_weights) != len(reward_funcs):
+                raise ValueError(
+                    f"Number of reward weights ({len(args.reward_weights)}) must match number of reward "
+                    f"functions ({len(reward_funcs)})"
+                )
+            self.reward_weights = torch.tensor(args.reward_weights, dtype=torch.float32)
+        else:
+            self.reward_weights = torch.ones(len(reward_funcs), dtype=torch.float32)
+
+        # Reward processing class
+        if reward_processing_classes is None:
+            reward_processing_classes = [None] * len(reward_funcs)
+        elif not isinstance(reward_processing_classes, list):
+            reward_processing_classes = [reward_processing_classes]
+        else:
+            if len(reward_processing_classes) != len(reward_funcs):
+                raise ValueError("The number of reward processing classes must match the number of reward functions.")
+
+        for i, (reward_processing_class, reward_func) in enumerate(zip(reward_processing_classes, reward_funcs)):
+            if isinstance(reward_func, PreTrainedModel):
+                if reward_processing_class is None:
+                    reward_processing_class = AutoTokenizer.from_pretrained(reward_func.config._name_or_path)
+                if reward_processing_class.pad_token_id is None:
+                    reward_processing_class.pad_token = reward_processing_class.eos_token
+                # The reward model computes the reward for the latest non-padded token in the input sequence.
+                # So it's important to set the pad token ID to the padding token ID of the processing class.
+                reward_func.config.pad_token_id = reward_processing_class.pad_token_id
+                reward_processing_classes[i] = reward_processing_class
+        self.reward_processing_classes = reward_processing_classes
+
+        # Data collator
+        def data_collator(features):  # No data collation is needed in GRPO
+            return features
+
+        # Training arguments
+        self.max_prompt_length = args.max_prompt_length
+        self.max_completion_length = args.max_completion_length  # = |o_i| in the GRPO paper
+        self.num_generations = args.num_generations  # = G in the GRPO paper
+        self.use_vllm = args.use_vllm
+
+        self.beta = args.beta
+
+        # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
+        # input tensor associated with the key "input_ids". However, in GRPO, the sampled data does not include the
+        # "input_ids" key. Instead, the available keys is "prompt". As a result, the trainer issues the warning:
+        # "Could not estimate the number of tokens of the input, floating-point operations will not be computed." To
+        # suppress this warning, we set the "estimate_tokens" key in the model's "warnings_issued" dictionary to True.
+        # This acts as a flag to indicate that the warning has already been issued.
+        model.warnings_issued["estimate_tokens"] = True
+
+        # Initialize the metrics
+        self._metrics = defaultdict(list)
+        self.log_completions = args.log_completions
+
+        super().__init__(
+            model=model,
+            args=args,
+            data_collator=data_collator,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            processing_class=processing_class,
+            callbacks=callbacks,
+            optimizers=optimizers,
+        )
+
+        # Check if the per_device_train/eval_batch_size * num processes can be divided by the number of generations
+        num_processes = self.accelerator.num_processes
+        global_batch_size = args.per_device_train_batch_size * num_processes
+        possible_values = [n_gen for n_gen in range(2, global_batch_size + 1) if (global_batch_size) % n_gen == 0]
+        if self.num_generations not in possible_values:
+            raise ValueError(
+                f"The global train batch size ({num_processes} x {args.per_device_train_batch_size}) must be evenly "
+                f"divisible by the number of generations per prompt ({self.num_generations}). Given the current train "
+                f"batch size, the valid values for the number of generations are: {possible_values}."
+            )
+        if self.args.eval_strategy != "no":
+            global_batch_size = args.per_device_eval_batch_size * num_processes
+            possible_values = [n_gen for n_gen in range(2, global_batch_size + 1) if (global_batch_size) % n_gen == 0]
+            if self.num_generations not in possible_values:
+                raise ValueError(
+                    f"The global eval batch size ({num_processes} x {args.per_device_eval_batch_size}) must be evenly "
+                    f"divisible by the number of generations per prompt ({self.num_generations}). Given the current "
+                    f"eval batch size, the valid values for the number of generations are: {possible_values}."
+                )
+
+        # Ensure each process receives a unique seed to prevent duplicate completions when generating with
+        # transformers if num_generations exceeds per_device_train_batch_size. We could skip it if we use vLLM, but
+        # it's safer to set it in all cases.
+        set_seed(args.seed, device_specific=True)
+
+        if self.use_vllm:
+            self.llm = model.vllm_engine; self._last_loaded_step = 0; self.sampling_params = SamplingParams(
+                    temperature=args.temperature,
+                    max_tokens=self.max_completion_length,**getattr(getattr(args, 'vllm_sampling_params', vLLMSamplingParams()), '_set_kwargs', {}),)
+        else:
+            self.generation_config = GenerationConfig(
+                max_new_tokens=self.max_completion_length,
+                do_sample=True,
+                temperature=args.temperature,
+                pad_token_id=processing_class.pad_token_id,
+            )
+
+        # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the
+        # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set
+        # self.model_accepts_loss_kwargs to False to enable scaling.
+        self.model_accepts_loss_kwargs = False
+
+        # Add tags to the model
+        self.model.add_model_tags(self._tag_names)
+
+        if self.ref_model is not None:
+            if self.is_deepspeed_enabled:
+                self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator)
+            else:
+                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
+
+        if args.sync_ref_model:
+            self.add_callback(SyncRefModelCallback(ref_model=self.ref_model, accelerator=self.accelerator))
+
+        for i, reward_func in enumerate(self.reward_funcs):
+            if isinstance(reward_func, PreTrainedModel):
+                self.reward_funcs[i] = self.accelerator.prepare_model(reward_func, evaluation_mode=True)
+
+    def _set_signature_columns_if_needed(self):
+        # If `self.args.remove_unused_columns` is True, non-signature columns are removed.
+        # By default, this method sets `self._signature_columns` to the model's expected inputs.
+        # In GRPOTrainer, we preprocess data, so using the model's signature columns doesn't work.
+        # Instead, we set them to the columns expected by the `training_step` method, hence the override.
+        if self._signature_columns is None:
+            self._signature_columns = ["prompt"]
+
+    def _get_train_sampler(self) -> Sampler:
+        # Returns a sampler that ensures each prompt is repeated across multiple processes. This guarantees that
+        # identical prompts are distributed to different GPUs, allowing rewards to be computed and normalized correctly
+        # within each prompt group. Using the same seed across processes ensures consistent prompt assignment,
+        # preventing discrepancies in group formation.
+        return RepeatRandomSampler(self.train_dataset, self.num_generations, seed=self.args.seed)
+
+    def _get_eval_sampler(self, eval_dataset) -> Sampler:
+        # Returns a sampler that ensures each prompt is repeated across multiple processes. This guarantees that
+        # identical prompts are distributed to different GPUs, allowing rewards to be computed and normalized correctly
+        # within each prompt group. Using the same seed across processes ensures consistent prompt assignment,
+        # preventing discrepancies in group formation.
+        return RepeatRandomSampler(eval_dataset, self.num_generations, seed=self.args.seed)
+
+    # Get the per-token log probabilities for the completions for the model and the reference model
+    def _get_per_token_logps(self, model, input_ids, attention_mask, logits_to_keep):
+        if os.environ.get('UNSLOTH_USE_NEW_MODEL', '0') == '0':
+            return None # Unsloth efficient GRPO
+        # Otherwise, calculate normally:
+        if not hasattr(self, '_autocast_dtype'):
+            self._autocast_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16
+            if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1': self._autocast_dtype = torch.float16
+        with torch.amp.autocast(device_type = 'cuda', dtype = self._autocast_dtype):
+            # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded
+            logits = model(input_ids=input_ids, attention_mask=attention_mask, logits_to_keep=logits_to_keep + 1).logits
+            logits = logits[:, :-1, :]  # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
+
+            input_ids = input_ids[:, -logits_to_keep:]
+            # For transformers<=4.48, logits_to_keep argument isn't supported, so here we drop logits ourselves.
+            # See https://github.com/huggingface/trl/issues/2770
+            logits = logits[:, -logits_to_keep:]
+            return logits
+            # return selective_log_softmax(logits, input_ids)  #  compute logprobs for the input tokens
+        pass
+
+    def _move_model_to_vllm(self, *args, **kwargs): return None
+
+    def _prepare_inputs(self, inputs: dict[str, Union[torch.Tensor, Any]]) -> dict[str, Union[torch.Tensor, Any]]:
+        device = self.accelerator.device
+        prompts = [x["prompt"] for x in inputs]
+        prompts_text = [maybe_apply_chat_template(example, self.processing_class)["prompt"] for example in inputs]
+        prompt_inputs = self.processing_class(
+            prompts_text, return_tensors="pt", padding=True, padding_side="left", add_special_tokens=False
+        )
+        prompt_inputs = super()._prepare_inputs(prompt_inputs)
+        prompt_ids, prompt_mask = prompt_inputs["input_ids"], prompt_inputs["attention_mask"]
+
+        if self.max_prompt_length is not None:
+            prompt_ids = prompt_ids[:, -self.max_prompt_length :]
+            prompt_mask = prompt_mask[:, -self.max_prompt_length :]
+
+        # Generate completions using either vLLM or regular generation
+        if self.args.use_vllm:
+            # First, have main process load weights if needed
+            if self.state.global_step != self._last_loaded_step:
+                self._move_model_to_vllm()
+                self._last_loaded_step = self.state.global_step
+
+            # Generate completions using vLLM: gather all prompts and use them in a single call in the main process
+            all_prompts_text = gather_object(prompts_text)
+            if self.accelerator.is_main_process:
+                outputs = self.llm.generate(all_prompts_text, sampling_params=self.sampling_params, use_tqdm=False, lora_request = self.model.load_lora('grpo_trainer_lora_model', load_tensors = True))
+                completion_ids = [out.token_ids for completions in outputs for out in completions.outputs]
+            else:
+                completion_ids = [None] * len(all_prompts_text)
+            # Broadcast the completions from the main process to all processes, ensuring each process receives its
+            # corresponding slice.
+            completion_ids = broadcast_object_list(completion_ids, from_process=0)
+            process_slice = slice(
+                self.accelerator.process_index * len(prompts),
+                (self.accelerator.process_index + 1) * len(prompts),
+            )
+            completion_ids = completion_ids[process_slice]
+
+            # Pad the completions, and concatenate them with the prompts
+            completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids]
+            completion_ids = pad(completion_ids, padding_value=self.processing_class.pad_token_id)
+            prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1)
+        else:
+            # Regular generation path
+            with unwrap_model_for_generation(self.model, self.accelerator) as unwrapped_model:
+                prompt_completion_ids = unwrapped_model.generate(
+                    prompt_ids, attention_mask=prompt_mask, generation_config=self.generation_config
+                )
+
+            # Compute prompt length and extract completion ids
+            prompt_length = prompt_ids.size(1)
+            prompt_ids = prompt_completion_ids[:, :prompt_length]
+            completion_ids = prompt_completion_ids[:, prompt_length:]
+
+        # Mask everything after the first EOS token
+        is_eos = completion_ids == self.processing_class.eos_token_id
+        eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=device)
+        eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)]
+        sequence_indices = torch.arange(is_eos.size(1), device=device).expand(is_eos.size(0), -1)
+        completion_mask = (sequence_indices <= eos_idx.unsqueeze(1)).int()
+
+        # Concatenate prompt_mask with completion_mask for logit computation
+        attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)  # (B*G, P+C)
+
+        logits_to_keep = completion_ids.size(1)  # we only need to compute the logits for the completion tokens
+
+        with torch.inference_mode(), torch.amp.autocast(device_type = 'cuda', dtype = ((torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16) if not torch.is_autocast_enabled('cuda') else nullcontext())if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '0' else torch.float16):
+            if self.ref_model is not None:
+                ref_per_token_logps = self._get_per_token_logps(
+                    self.ref_model, prompt_completion_ids, attention_mask, logits_to_keep
+                )
+            else:
+                with self.accelerator.unwrap_model(self.model, keep_fp32_wrapper = False).disable_adapter():
+                    ref_per_token_logps = self._get_per_token_logps(
+                        self.model, prompt_completion_ids, attention_mask, logits_to_keep
+                    )
+
+        # Decode the generated completions
+        completions_text = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True)
+        if is_conversational(inputs[0]):
+            completions = []
+            for prompt, completion in zip(prompts, completions_text):
+                bootstrap = prompt.pop()["content"] if prompt[-1]["role"] == "assistant" else ""
+                completions.append([{"role": "assistant", "content": bootstrap + completion}])
+        else:
+            completions = completions_text
+
+        rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device)
+        for i, (reward_func, reward_processing_class) in enumerate(
+            zip(self.reward_funcs, self.reward_processing_classes)
+        ):
+            if isinstance(reward_func, nn.Module):  # Module instead of PretrainedModel for compat with compiled models
+                if is_conversational(inputs[0]):
+                    messages = [{"messages": p + c} for p, c in zip(prompts, completions)]
+                    texts = [apply_chat_template(x, reward_processing_class)["text"] for x in messages]
+                else:
+                    texts = [p + c for p, c in zip(prompts, completions)]
+                reward_inputs = reward_processing_class(
+                    texts, return_tensors="pt", padding=True, padding_side="right", add_special_tokens=False
+                )
+                reward_inputs = super()._prepare_inputs(reward_inputs)
+                with torch.inference_mode(), torch.amp.autocast(device_type = 'cuda', dtype = ((torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16) if not torch.is_autocast_enabled('cuda') else nullcontext())if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '0' else torch.float16):
+                    rewards_per_func[:, i] = reward_func(**reward_inputs).logits[:, 0]  # Shape (B*G,)
+            else:
+                # Repeat all input columns (but "prompt" and "completion") to match the number of generations
+                keys = [key for key in inputs[0] if key not in ["prompt", "completion"]]
+                reward_kwargs = {key: [example[key] for example in inputs] for key in keys}
+                output_reward_func = reward_func(prompts=prompts, completions=completions, **reward_kwargs)
+                rewards_per_func[:, i] = torch.tensor(output_reward_func, dtype=torch.float32, device=device)
+
+        # Gather the reward per function: this part is crucial, because the rewards are normalized per group and the
+        # completions may be distributed across processes
+        rewards_per_func = gather(rewards_per_func)
+
+        # Apply weights to each reward function's output and sum
+        rewards = (rewards_per_func * self.reward_weights.to(device).unsqueeze(0)).sum(dim=1)
+
+        # Compute grouped-wise rewards
+        mean_grouped_rewards = rewards.view(-1, self.num_generations).mean(dim=1)
+        std_grouped_rewards = rewards.view(-1, self.num_generations).std(dim=1)
+
+        # Normalize the rewards to compute the advantages
+        mean_grouped_rewards = mean_grouped_rewards.repeat_interleave(self.num_generations, dim=0)
+        std_grouped_rewards = std_grouped_rewards.repeat_interleave(self.num_generations, dim=0)
+        advantages = (rewards - mean_grouped_rewards) / (std_grouped_rewards + 1e-4)
+
+        # Slice to keep only the local part of the data
+        process_slice = slice(
+            self.accelerator.process_index * len(prompts),
+            (self.accelerator.process_index + 1) * len(prompts),
+        )
+        advantages = advantages[process_slice]
+
+        # Log the metrics
+        reward_per_func = rewards_per_func.mean(0)
+        for i, reward_func in enumerate(self.reward_funcs):
+            if isinstance(reward_func, nn.Module):  # Module instead of PretrainedModel for compat with compiled models
+                reward_func_name = reward_func.config._name_or_path.split("/")[-1]
+            else:
+                reward_func_name = reward_func.__name__
+            self._metrics[f"rewards/{reward_func_name}"].append(reward_per_func[i].item())
+
+        self._metrics["reward"].append(rewards.mean().item())
+        self._metrics["reward_std"].append(std_grouped_rewards.mean().item())
+
+        if (
+            self.log_completions
+            and self.state.global_step % self.args.logging_steps == 0
+            and "wandb" in self.args.report_to
+        ):
+            import pandas as pd
+
+            # For logging
+            table = {
+                "step": [str(self.state.global_step)] * len(rewards),
+                "prompt": gather_object(prompts_text),
+                "completion": gather_object(completions_text),
+                "reward": rewards.tolist(),
+            }
+            df = pd.DataFrame(table)
+
+            if wandb.run is not None and self.accelerator.is_main_process:
+                wandb.log({"completions": wandb.Table(dataframe=df)})
+
+        return {
+            "prompt_ids": prompt_ids,
+            "prompt_mask": prompt_mask,
+            "completion_ids": completion_ids,
+            "completion_mask": completion_mask,
+            "ref_per_token_logps": ref_per_token_logps,
+            "advantages": advantages,
+        }
+
+    def compute_loss(self, model, inputs, return_outputs = False, num_items_in_batch = None):
+        if return_outputs:
+            raise ValueError("The GRPOTrainer does not support returning outputs")
+        # Compute the per-token log probabilities for the model
+
+        prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"]
+        completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"]
+        input_ids = torch.cat([prompt_ids, completion_ids], dim=1)
+        bsz, qlen = input_ids.shape
+        attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)
+        # attention_mask = None
+        logits_to_keep = completion_ids.size(1)  # we only need to compute the logits for the completion tokens
+        _input_ids = input_ids
+        _logits_to_keep = logits_to_keep
+        
+        per_token_logps = self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep)
+
+        # Compute the KL divergence between the model and the reference model
+        ref_per_token_logps = inputs["ref_per_token_logps"]
+        # per_token_kl = torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1
+
+        # x - x.detach() allows for preserving gradients from x
+        advantages = inputs["advantages"]
+        # per_token_loss = torch.exp(per_token_logps - per_token_logps.detach()) * advantages.unsqueeze(1)
+        # per_token_loss = -(per_token_loss - self.beta * per_token_kl)
+        # loss = ((per_token_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean()
+        input_ids = input_ids[:, -logits_to_keep:]
+        if per_token_logps is not None:
+            loss, completion_length, mean_kl = grpo_compute_loss_slow(
+                ref_per_token_logps, per_token_logps, input_ids, completion_mask, self.beta, advantages,
+            )
+        else:
+            loss, completion_length, mean_kl = grpo_accumulated_loss(
+                self, _input_ids, logits_to_keep, completion_mask, advantages,
+                n_chunks = self.args.unsloth_num_chunks,
+            )
+
+        # Log the metrics
+        # completion_length = self.accelerator.gather_for_metrics(completion_mask.sum(1)).float().mean().item()
+
+        # mean_kl = ((per_token_kl * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean()
+        # self._metrics["kl"].append(self.accelerator.gather_for_metrics(mean_kl).mean().item())
+
+        if "train" in self._metrics:
+            mode = "eval" if self.control.should_evaluate else "train"
+            self._metrics[mode]["completion_length"].append(completion_length.item())
+            self._metrics[mode]["kl"].append(mean_kl.item())
+        else:
+            self._metrics["completion_length"].append(completion_length.item())
+            self._metrics["kl"].append(mean_kl.item())
+        return loss
+
+    def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys: Optional[list[str]] = None):
+        inputs = self._prepare_inputs(inputs)
+        with torch.no_grad():
+            with self.compute_loss_context_manager():
+                loss = self.compute_loss(model, inputs)
+            loss = loss.mean().detach()
+        return loss, None, None
+
+    def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None:
+        metrics = {key: sum(val) / len(val) for key, val in self._metrics.items()}  # average the metrics
+
+        # This method can be called both in training and evaluation. When called in evaluation, the keys in `logs`
+        # start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format.
+        if next(iter(logs.keys())).startswith("eval_"):
+            metrics = {f"eval_{key}": val for key, val in metrics.items()}
+
+        logs = {**logs, **metrics}
+        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
+            super().log(logs, start_time)
+        else:  # transformers<=4.46
+            super().log(logs)
+        self._metrics.clear()
+
+    def create_model_card(
+        self,
+        model_name: Optional[str] = None,
+        dataset_name: Optional[str] = None,
+        tags: Union[str, list[str], None] = None,
+    ):
+        """
+        Creates a draft of a model card using the information available to the `Trainer`.
+
+        Args:
+            model_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the model.
+            dataset_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the dataset used for training.
+            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
+                Tags to be associated with the model card.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
+            base_model = self.model.config._name_or_path
+        else:
+            base_model = None
+
+        tags = tags or []
+        if isinstance(tags, str):
+            tags = [tags]
+
+        if hasattr(self.model.config, "unsloth_version"):
+            tags.append("unsloth")
+
+        citation = textwrap.dedent(
+            """\
+            @article{zhihong2024deepseekmath,
+                title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
+                author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
+                year         = 2024,
+                eprint       = {arXiv:2402.03300},
+            }
+            """
+        )
+
+        model_card = generate_model_card(
+            base_model=base_model,
+            model_name=model_name,
+            hub_model_id=self.hub_model_id,
+            dataset_name=dataset_name,
+            tags=tags,
+            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
+            comet_url=get_comet_experiment_url(),
+            trainer_name="GRPO",
+            trainer_citation=citation,
+            paper_title="DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models",
+            paper_id="2402.03300",
+        )
+
+        model_card.save(os.path.join(self.args.output_dir, "README.md"))
+class UnslothGRPOTrainer(_UnslothGRPOTrainer):
+    """
+    
+    Trainer for the Group Relative Policy Optimization (GRPO) method. This algorithm was initially proposed in the
+    paper [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
+
+    Example:
+
+    ```python
+    from datasets import load_dataset
+    from trl import GRPOTrainer
+
+    dataset = load_dataset("trl-lib/tldr", split="train")
+
+    def reward_func(completions, **kwargs):
+        # Dummy reward function that rewards completions with more unique letters.
+        return [float(len(set(completion))) for completion in completions]
+
+    trainer = GRPOTrainer(
+        model="Qwen/Qwen2-0.5B-Instruct",
+        reward_funcs=reward_func,
+        train_dataset=dataset,
+    )
+
+    trainer.train()
+    ```
+
+    Args:
+        model (`Union[str, PreTrainedModel]`):
+            Model to be trained. Can be either:
+
+            - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or
+              a path to a *directory* containing model weights saved using
+              [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is
+              loaded using [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keywork arguments
+              in `args.model_init_kwargs`.
+            - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported.
+        reward_funcs (`Union[RewardFunc, list[RewardFunc]]`):
+            Reward functions to be used for computing the rewards. To compute the rewards, we call all the reward
+            functions with the prompts and completions and sum the rewards. Can be either:
+
+            - A single reward function, such as:
+                - A string: The *model ID* of a pretrained model hosted inside a model repo on huggingface.co, or a
+                path to a *directory* containing model weights saved using
+                [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded
+                using [`~transformers.AutoModelForSequenceClassification.from_pretrained`] with `num_labels=1` and the
+                keyword arguments in `args.model_init_kwargs`.
+                - A [`~transformers.PreTrainedModel`] object: Only sequence classification models are supported.
+                - A custom reward function: The function is provided with the prompts and the generated completions,
+                  plus any additional columns in the dataset. It should return a list of rewards. For more details, see
+                  [Using a custom reward function](#using-a-custom-reward-function).
+            - A list of reward functions, where each item can independently be any of the above types. Mixing different
+            types within the list (e.g., a string model ID and a custom reward function) is allowed.
+        args ([`GRPOConfig`], *optional*, defaults to `None`):
+            Configuration for this trainer. If `None`, a default configuration is used.
+        train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]):
+            Dataset to use for training. It must include a column `"prompt"`. Any additional columns in the dataset is
+            ignored. The format of the samples can be either:
+
+            - [Standard](dataset_formats#standard): Each sample contains plain text.
+            - [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role
+              and content).
+        eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, IterableDataset]]`):
+            Dataset to use for evaluation. It must meet the same requirements as `train_dataset`.
+        processing_class ([`~transformers.PreTrainedTokenizerBase`], *optional*, defaults to `None`):
+            Processing class used to process the data. The padding side must be set to "left". If `None`, the
+            processing class is loaded from the model's name with [`~transformers.AutoTokenizer.from_pretrained`].
+        reward_processing_classes (`Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]`, *optional*, defaults to `None`):
+            Processing classes corresponding to the reward functions specified in `reward_funcs`. Can be either:
+
+            - A single processing class: Used when `reward_funcs` contains only one reward function.
+            - A list of processing classes: Must match the order and length of the reward functions in `reward_funcs`.
+            If set to `None`, or if an element of the list corresponding to a [`~transformers.PreTrainedModel`] is
+            `None`, the tokenizer for the model is automatically loaded using [`~transformers.AutoTokenizer.from_pretrained`].
+            For elements in `reward_funcs` that are custom reward functions (not [`~transformers.PreTrainedModel`]),
+            the corresponding entries in `reward_processing_classes` are ignored.
+        callbacks (list of [`~transformers.TrainerCallback`], *optional*, defaults to `None`):
+            List of callbacks to customize the training loop. Will add those to the list of default callbacks
+            detailed in [here](https://huggingface.co/docs/transformers/main_classes/callback).
+
+            If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`]
+            method.
+        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*, defaults to `(None, None)`):
+            A tuple containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your
+            model and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`.
+        peft_config ([`~peft.PeftConfig`], *optional*, defaults to `None`):
+            PEFT configuration used to wrap the model. If `None`, the model is not wrapped.
+    
+    """
+    def __init__(
+        self,
+        model,
+        reward_funcs,
+        args = None,
+        train_dataset = None,
+        eval_dataset = None,
+        processing_class = None,
+        reward_processing_classes = None,
+        callbacks = None,
+        peft_config = None,
+        **kwargs
+    ):
+        if args is None: args = UnslothGRPOConfig()
+        use_bf16 = getattr(args, 'bf16', False)
+        use_fp16 = getattr(args, 'fp16', False)
+        force_float32 = False
+        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':
+            print('Unsloth: Switching to float32 training since model cannot work with float16')
+            force_float32 = True
+        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
+        dtype = getattr(model.config, 'torch_dtype', None)
+        if dtype is None: dtype = model.get_input_embeddings().dtype
+        from unsloth_zoo.utils import _get_dtype
+        dtype = _get_dtype(dtype)
+        float16 = dtype == torch.float16
+        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
+        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
+        if force_float32:
+            args.fp16 = False
+            args.bf16 = False
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
+        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
+            args.fp16 = float16
+            args.bf16 = not float16
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
+        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
+            args.eval_strategy = 'steps'
+            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
+        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
+        if ga_steps is not None and ga_steps > 1:
+            from transformers import __version__ as transformers_version
+            if Version(transformers_version) <= Version('4.45.2'):
+                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
+                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
+        if getattr(args, 'eval_strategy', 'no') != 'no':
+            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
+            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
+            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
+        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
+        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
+        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
+        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
+        if force_float32:
+            args.bf16_full_eval = False
+            args.fp16_full_eval = False
+        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
+            args.bf16_full_eval = True
+            args.fp16_full_eval = False
+        elif not bf16_full_eval and not fp16_full_eval:
+            args.bf16_full_eval = args.bf16
+            args.fp16_full_eval = args.fp16
+        _output_logits = False
+        if locals().get('compute_metrics', None) is not None: _output_logits = True
+        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
+        if _output_logits:
+            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
+        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
+            pass
+        else:
+            model_max_seq_length = getattr(model, 'max_seq_length', None)
+            args_max_seq_length  = getattr(args,  'max_seq_length', None)
+            if args_max_seq_length is None and model_max_seq_length is not None:
+                max_seq_length = model.max_seq_length
+                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
+        if model is not None and hasattr(model, 'for_training'):
+            model.for_training()
+        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
+        if 'processing_class' in locals():
+            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
+            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
+        other_metrics = []
+        if not isinstance(reward_funcs, list): _reward_funcs = [reward_funcs]
+        else: _reward_funcs = reward_funcs
+        for reward_func in _reward_funcs:
+            try:
+                reward_func_name = reward_func.__name__
+                other_metrics.append(f'rewards/{reward_func_name}')
+            except: pass
+        
+        from unsloth_zoo.logging_utils import PatchRLStatistics
+        PatchRLStatistics('grpo_trainer', other_metrics)
+        
+        super().__init__(
+            model = model,
+            reward_funcs = reward_funcs,
+            args = args,
+            train_dataset = train_dataset,
+            eval_dataset = eval_dataset,
+            processing_class = processing_class,
+            reward_processing_classes = reward_processing_classes,
+            callbacks = callbacks,
+            peft_config = peft_config,**kwargs)
+        if hasattr(self, 'neftune_hook_handle'):
+            self.neftune_hook_handle.remove()
+            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
+        if getattr(args, 'neftune_noise_alpha', None) is not None:
+            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
+        pass
+        
+pass

unsloth_compiled_cache/UnslothKTOTrainer.py

@@ -0,0 +1,1834 @@
+"""
+2025.4.1
+2025.4.1
+4.51.3
+0.15.2
+__UNSLOTH_VERSIONING__
+"""
+from torch import Tensor
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from trl.trainer.kto_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, KTOConfig, KTOTrainer, Literal, Optional, PartialState, PeftModel, PreTrainedModel, PreTrainedModelWrapper, PreTrainedTokenizerBase, ProcessorMixin, SequentialSampler, Trainer, TrainerCallback, TrainingArguments, Union, _get_kl_dataset, _process_tokens, _tokenize, amp, concatenate_datasets, contextmanager, create_reference_model, deepcopy, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, has_length, inspect, is_comet_available, is_peft_available, is_wandb_available, itemgetter, log_table_to_comet_experiment, maybe_apply_chat_template, maybe_extract_prompt, maybe_unpair_preference_dataset, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_model_for_kbit_training, random, textwrap, torch, tqdm, transformers, version, warnings)
+
+
+import os
+from typing import *
+from dataclasses import dataclass, field
+from packaging.version import Version
+import torch
+import numpy as np
+from contextlib import nullcontext
+from torch.nn import functional as F
+from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
+
+torch_compile_options = {
+    "epilogue_fusion"   : True,
+    "max_autotune"      : False,
+    "shape_padding"     : True,
+    "trace.enabled"     : False,
+    "triton.cudagraphs" : False,
+}
+
+@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
+def selective_log_softmax(logits, index):
+    logits = logits.to(torch.float32)
+    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
+    # loop to reduce peak mem consumption
+    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
+    logsumexp_values = torch.logsumexp(logits, dim = -1)
+    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
+    return per_token_logps
+@dataclass
+class UnslothKTOConfig(KTOConfig):
+    """
+    
+    Configuration class for the [`KTOTrainer`].
+
+    Using [`~transformers.HfArgumentParser`] we can turn this class into
+    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
+    command line.
+
+    Parameters:
+        learning_rate (`float`, *optional*, defaults to `5e-7`):
+            Initial learning rate for [`AdamW`] optimizer. The default value replaces that of
+            [`~transformers.TrainingArguments`].
+        max_length (`int` or `None`, *optional*, defaults to `1024`):
+            Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want
+            to use the default data collator.
+        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
+            Maximum length of the prompt. This argument is required if you want to use the default data collator.
+        max_completion_length (`int` or `None`, *optional*, defaults to `None`):
+            Maximum length of the completion. This argument is required if you want to use the default data collator
+            and your model is an encoder-decoder.
+        beta (`float`, *optional*, defaults to `0.1`):
+            Parameter controlling the deviation from the reference model. Higher β means less deviation from the
+            reference model.
+        loss_type (`str`, *optional*, defaults to `"kto"`):
+            Type of loss to use. Possible values are:
+
+                - `"kto"`: KTO loss from the [KTO](https://huggingface.co/papers/2402.01306) paper.
+                - `"apo_zero_unpaired"`: Unpaired variant of APO-zero loss from the [APO](https://huggingface.co/papers/2408.06266) paper.
+
+        desirable_weight (`float`, *optional*, defaults to `1.0`):
+            Desirable losses are weighed by this factor to counter unequal number of desirable and undesirable paris.
+        undesirable_weight (`float`, *optional*, defaults to `1.0`):
+            Undesirable losses are weighed by this factor to counter unequal number of desirable and undesirable pairs.
+        label_pad_token_id (`int`, *optional*, defaults to `-100`):
+            Label pad token id. This argument is required if you want to use the default data collator.
+        padding_value (`int` or `None`, *optional*, defaults to `None`):
+            Padding value to use. If `None`, the padding value of the tokenizer is used.
+        truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
+            Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`.
+            This argument is required if you want to use the default data collator.
+        generate_during_eval (`bool`, *optional*, defaults to `False`):
+            If `True`, generates and logs completions from both the model and the reference model to W&B or Comet during
+            evaluation.
+        is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`):
+            When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument,
+            you need to specify if the model returned by the callable is an encoder-decoder model.
+        precompute_ref_log_probs (`bool`, *optional*, defaults to `False`):
+            Whether to precompute reference model log probabilities for training and evaluation datasets. This is
+            useful when training without the reference model to reduce the total GPU memory needed.
+        model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
+            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
+            string.
+        ref_model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
+            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the reference model
+            from a string.
+        dataset_num_proc: (`int` or `None`, *optional*, defaults to `None`):
+            Number of processes to use for processing the dataset.
+        disable_dropout (`bool`, *optional*, defaults to `True`):
+            Whether to disable dropout in the model and reference model.
+    
+    """
+    vllm_sampling_params: Optional[Any] = field(
+        default = None,
+        metadata = {'help': 'vLLM SamplingParams'},
+    )
+    unsloth_num_chunks : Optional[int] = field(
+        default = -1,
+        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
+    )
+    def __init__(
+        self,
+        output_dir = None,
+        overwrite_output_dir = None,
+        do_train = False,
+        do_eval = False,
+        do_predict = False,
+        eval_strategy = 'no',
+        prediction_loss_only = False,
+        per_device_train_batch_size = 4,
+        per_device_eval_batch_size = 4,
+        per_gpu_train_batch_size = None,
+        per_gpu_eval_batch_size = None,
+        gradient_accumulation_steps = 2,
+        eval_accumulation_steps = 2,
+        eval_delay = 0,
+        torch_empty_cache_steps = 250,
+        learning_rate = 5e-05,
+        weight_decay = 0.01,
+        adam_beta1 = 0.9,
+        adam_beta2 = 0.999,
+        adam_epsilon = 1e-08,
+        max_grad_norm = 1.0,
+        num_train_epochs = 3.0,
+        max_steps = -1,
+        lr_scheduler_type = 'linear',
+        warmup_ratio = 0.1,
+        warmup_steps = 0,
+        log_level = 'passive',
+        log_level_replica = 'warning',
+        log_on_each_node = True,
+        logging_dir = None,
+        logging_strategy = 'steps',
+        logging_first_step = False,
+        logging_steps = 1,
+        logging_nan_inf_filter = False,
+        save_strategy = 'steps',
+        save_steps = 500,
+        save_total_limit = None,
+        save_safetensors = True,
+        save_on_each_node = False,
+        save_only_model = False,
+        restore_callback_states_from_checkpoint = False,
+        no_cuda = False,
+        use_cpu = False,
+        use_mps_device = False,
+        seed = 3407,
+        data_seed = 3407,
+        jit_mode_eval = False,
+        use_ipex = False,
+        bf16 = False,
+        fp16 = False,
+        fp16_opt_level = 'O1',
+        half_precision_backend = 'auto',
+        bf16_full_eval = False,
+        fp16_full_eval = False,
+        tf32 = None,
+        local_rank = -1,
+        ddp_backend = None,
+        tpu_num_cores = None,
+        tpu_metrics_debug = False,
+        debug = '',
+        dataloader_drop_last = False,
+        eval_steps = None,
+        dataloader_num_workers = 0,
+        dataloader_prefetch_factor = None,
+        past_index = -1,
+        run_name = None,
+        disable_tqdm = None,
+        remove_unused_columns = True,
+        label_names = None,
+        load_best_model_at_end = False,
+        metric_for_best_model = None,
+        greater_is_better = None,
+        ignore_data_skip = False,
+        fsdp = '',
+        fsdp_min_num_params = 0,
+        fsdp_config = None,
+        tp_size = 0,
+        fsdp_transformer_layer_cls_to_wrap = None,
+        accelerator_config = None,
+        deepspeed = None,
+        label_smoothing_factor = 0.0,
+        optim = 'adamw_8bit',
+        optim_args = None,
+        adafactor = False,
+        group_by_length = False,
+        length_column_name = 'length',
+        report_to = None,
+        ddp_find_unused_parameters = None,
+        ddp_bucket_cap_mb = None,
+        ddp_broadcast_buffers = None,
+        dataloader_pin_memory = True,
+        dataloader_persistent_workers = False,
+        skip_memory_metrics = True,
+        use_legacy_prediction_loop = False,
+        push_to_hub = False,
+        resume_from_checkpoint = None,
+        hub_model_id = None,
+        hub_strategy = 'every_save',
+        hub_token = None,
+        hub_private_repo = None,
+        hub_always_push = False,
+        gradient_checkpointing = False,
+        gradient_checkpointing_kwargs = None,
+        include_inputs_for_metrics = False,
+        eval_do_concat_batches = True,
+        fp16_backend = 'auto',
+        push_to_hub_model_id = None,
+        push_to_hub_organization = None,
+        push_to_hub_token = None,
+        mp_parameters = '',
+        auto_find_batch_size = False,
+        full_determinism = False,
+        torchdynamo = None,
+        ray_scope = 'last',
+        ddp_timeout = 1800,
+        torch_compile = False,
+        torch_compile_backend = None,
+        torch_compile_mode = None,
+        include_tokens_per_second = False,
+        include_num_input_tokens_seen = False,
+        neftune_noise_alpha = None,
+        optim_target_modules = None,
+        batch_eval_metrics = False,
+        eval_on_start = False,
+        use_liger_kernel = False,
+        eval_use_gather_object = False,
+        average_tokens_across_devices = False,
+        max_length = 1024,
+        max_prompt_length = 512,
+        max_completion_length = None,
+        beta = 0.1,
+        loss_type = 'kto',
+        desirable_weight = 1.0,
+        undesirable_weight = 1.0,
+        label_pad_token_id = -100,
+        padding_value = None,
+        truncation_mode = 'keep_end',
+        generate_during_eval = False,
+        is_encoder_decoder = None,
+        disable_dropout = True,
+        precompute_ref_log_probs = False,
+        model_init_kwargs = None,
+        ref_model_init_kwargs = None,
+        dataset_num_proc = None,
+        vllm_sampling_params = None,
+        unsloth_num_chunks = -1,
+        **kwargs,
+    ):
+        if learning_rate < 1e-7: raise FloatingPointError(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
+        if learning_rate > 1: raise OverflowError(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
+        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
+            output_dir = 'unsloth_training_checkpoints'
+            save_strategy = 'no'
+        if dataset_num_proc is None:
+            from multiprocessing import cpu_count
+            dataset_num_proc = cpu_count()
+        
+        super().__init__(
+            output_dir = output_dir,
+            overwrite_output_dir = overwrite_output_dir,
+            do_train = do_train,
+            do_eval = do_eval,
+            do_predict = do_predict,
+            eval_strategy = eval_strategy,
+            prediction_loss_only = prediction_loss_only,
+            per_device_train_batch_size = per_device_train_batch_size,
+            per_device_eval_batch_size = per_device_eval_batch_size,
+            per_gpu_train_batch_size = per_gpu_train_batch_size,
+            per_gpu_eval_batch_size = per_gpu_eval_batch_size,
+            gradient_accumulation_steps = gradient_accumulation_steps,
+            eval_accumulation_steps = eval_accumulation_steps,
+            eval_delay = eval_delay,
+            torch_empty_cache_steps = torch_empty_cache_steps,
+            learning_rate = learning_rate,
+            weight_decay = weight_decay,
+            adam_beta1 = adam_beta1,
+            adam_beta2 = adam_beta2,
+            adam_epsilon = adam_epsilon,
+            max_grad_norm = max_grad_norm,
+            num_train_epochs = num_train_epochs,
+            max_steps = max_steps,
+            lr_scheduler_type = lr_scheduler_type,
+            warmup_ratio = warmup_ratio,
+            warmup_steps = warmup_steps,
+            log_level = log_level,
+            log_level_replica = log_level_replica,
+            log_on_each_node = log_on_each_node,
+            logging_dir = logging_dir,
+            logging_strategy = logging_strategy,
+            logging_first_step = logging_first_step,
+            logging_steps = logging_steps,
+            logging_nan_inf_filter = logging_nan_inf_filter,
+            save_strategy = save_strategy,
+            save_steps = save_steps,
+            save_total_limit = save_total_limit,
+            save_safetensors = save_safetensors,
+            save_on_each_node = save_on_each_node,
+            save_only_model = save_only_model,
+            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
+            no_cuda = no_cuda,
+            use_cpu = use_cpu,
+            use_mps_device = use_mps_device,
+            seed = seed,
+            data_seed = data_seed,
+            jit_mode_eval = jit_mode_eval,
+            use_ipex = use_ipex,
+            bf16 = bf16,
+            fp16 = fp16,
+            fp16_opt_level = fp16_opt_level,
+            half_precision_backend = half_precision_backend,
+            bf16_full_eval = bf16_full_eval,
+            fp16_full_eval = fp16_full_eval,
+            tf32 = tf32,
+            local_rank = local_rank,
+            ddp_backend = ddp_backend,
+            tpu_num_cores = tpu_num_cores,
+            tpu_metrics_debug = tpu_metrics_debug,
+            debug = debug,
+            dataloader_drop_last = dataloader_drop_last,
+            eval_steps = eval_steps,
+            dataloader_num_workers = dataloader_num_workers,
+            dataloader_prefetch_factor = dataloader_prefetch_factor,
+            past_index = past_index,
+            run_name = run_name,
+            disable_tqdm = disable_tqdm,
+            remove_unused_columns = remove_unused_columns,
+            label_names = label_names,
+            load_best_model_at_end = load_best_model_at_end,
+            metric_for_best_model = metric_for_best_model,
+            greater_is_better = greater_is_better,
+            ignore_data_skip = ignore_data_skip,
+            fsdp = fsdp,
+            fsdp_min_num_params = fsdp_min_num_params,
+            fsdp_config = fsdp_config,
+            tp_size = tp_size,
+            fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
+            accelerator_config = accelerator_config,
+            deepspeed = deepspeed,
+            label_smoothing_factor = label_smoothing_factor,
+            optim = optim,
+            optim_args = optim_args,
+            adafactor = adafactor,
+            group_by_length = group_by_length,
+            length_column_name = length_column_name,
+            report_to = report_to,
+            ddp_find_unused_parameters = ddp_find_unused_parameters,
+            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
+            ddp_broadcast_buffers = ddp_broadcast_buffers,
+            dataloader_pin_memory = dataloader_pin_memory,
+            dataloader_persistent_workers = dataloader_persistent_workers,
+            skip_memory_metrics = skip_memory_metrics,
+            use_legacy_prediction_loop = use_legacy_prediction_loop,
+            push_to_hub = push_to_hub,
+            resume_from_checkpoint = resume_from_checkpoint,
+            hub_model_id = hub_model_id,
+            hub_strategy = hub_strategy,
+            hub_token = hub_token,
+            hub_private_repo = hub_private_repo,
+            hub_always_push = hub_always_push,
+            gradient_checkpointing = gradient_checkpointing,
+            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
+            include_inputs_for_metrics = include_inputs_for_metrics,
+            eval_do_concat_batches = eval_do_concat_batches,
+            fp16_backend = fp16_backend,
+            push_to_hub_model_id = push_to_hub_model_id,
+            push_to_hub_organization = push_to_hub_organization,
+            push_to_hub_token = push_to_hub_token,
+            mp_parameters = mp_parameters,
+            auto_find_batch_size = auto_find_batch_size,
+            full_determinism = full_determinism,
+            torchdynamo = torchdynamo,
+            ray_scope = ray_scope,
+            ddp_timeout = ddp_timeout,
+            torch_compile = torch_compile,
+            torch_compile_backend = torch_compile_backend,
+            torch_compile_mode = torch_compile_mode,
+            include_tokens_per_second = include_tokens_per_second,
+            include_num_input_tokens_seen = include_num_input_tokens_seen,
+            neftune_noise_alpha = neftune_noise_alpha,
+            optim_target_modules = optim_target_modules,
+            batch_eval_metrics = batch_eval_metrics,
+            eval_on_start = eval_on_start,
+            use_liger_kernel = use_liger_kernel,
+            eval_use_gather_object = eval_use_gather_object,
+            average_tokens_across_devices = average_tokens_across_devices,
+            max_length = max_length,
+            max_prompt_length = max_prompt_length,
+            max_completion_length = max_completion_length,
+            beta = beta,
+            loss_type = loss_type,
+            desirable_weight = desirable_weight,
+            undesirable_weight = undesirable_weight,
+            label_pad_token_id = label_pad_token_id,
+            padding_value = padding_value,
+            truncation_mode = truncation_mode,
+            generate_during_eval = generate_during_eval,
+            is_encoder_decoder = is_encoder_decoder,
+            disable_dropout = disable_dropout,
+            precompute_ref_log_probs = precompute_ref_log_probs,
+            model_init_kwargs = model_init_kwargs,
+            ref_model_init_kwargs = ref_model_init_kwargs,
+            dataset_num_proc = dataset_num_proc,**kwargs)
+        self.vllm_sampling_params = vllm_sampling_params
+        self.unsloth_num_chunks = unsloth_num_chunks
+pass
+
+class _UnslothKTOTrainer(Trainer):
+    r""""""
+
+    _tag_names = ["trl", "kto"]
+
+    def __init__(
+        self,
+        model: Union[PreTrainedModel, nn.Module, str] = None,
+        ref_model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
+        args: KTOConfig = None,
+        train_dataset: Optional[Dataset] = None,
+        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
+        processing_class: Optional[
+            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
+        ] = None,
+        data_collator: Optional[DataCollator] = None,
+        model_init: Optional[Callable[[], PreTrainedModel]] = None,
+        callbacks: Optional[list[TrainerCallback]] = None,
+        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
+        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+        peft_config: Optional[dict] = None,
+        compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None,
+        model_adapter_name: Optional[str] = None,
+        ref_adapter_name: Optional[str] = None,
+    ):
+        if type(args) is TrainingArguments:
+            raise ValueError("Please use `KTOConfig` instead TrainingArguments.")
+
+        if not isinstance(model, str) and ref_model is model:
+            raise ValueError(
+                "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the "
+                "same as `model`, you must mass a copy of it, or `None` if you use peft."
+            )
+
+        if args.model_init_kwargs is None:
+            model_init_kwargs = {}
+        elif not isinstance(model, str):
+            raise ValueError("You passed model_kwargs to the KTOTrainer. But your model is already instantiated.")
+        else:
+            model_init_kwargs = args.model_init_kwargs
+            torch_dtype = model_init_kwargs.get("torch_dtype")
+            if torch_dtype is not None:
+                # Convert to `torch.dtype` if an str is passed
+                if isinstance(torch_dtype, str) and torch_dtype != "auto":
+                    torch_dtype = getattr(torch, torch_dtype)
+                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
+                    raise ValueError(
+                        f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
+                    )
+                model_init_kwargs["torch_dtype"] = torch_dtype
+
+        if args.ref_model_init_kwargs is None:
+            ref_model_init_kwargs = {}
+        elif not isinstance(ref_model, str):
+            raise ValueError(
+                "You passed ref_model_kwargs to the KTOTrainer. But your ref_model is already instantiated."
+            )
+        else:
+            ref_model_init_kwargs = args.ref_model_init_kwargs
+            torch_dtype = ref_model_init_kwargs.get("torch_dtype")
+            if torch_dtype is not None:
+                # Convert to `torch.dtype` if an str is passed
+                if isinstance(torch_dtype, str) and torch_dtype != "auto":
+                    torch_dtype = getattr(torch, torch_dtype)
+                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
+                    raise ValueError(
+                        f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
+                    )
+                ref_model_init_kwargs["torch_dtype"] = torch_dtype
+
+        if isinstance(model, str):
+            model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)
+
+        if isinstance(ref_model, str):
+            ref_model = AutoModelForCausalLM.from_pretrained(ref_model, **ref_model_init_kwargs)
+
+        # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16`
+        # has been called in order to properly call autocast if needed.
+        self._peft_has_been_casted_to_bf16 = False
+
+        if not is_peft_available() and peft_config is not None:
+            raise ValueError(
+                "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it with `pip install peft` to use the PEFT models"
+            )
+        elif is_peft_available() and peft_config is not None:
+            # if model is a peft model and we have a peft_config, we merge and unload it first
+            if isinstance(model, PeftModel):
+                model = model.merge_and_unload()
+
+            if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
+                _support_gc_kwargs = hasattr(
+                    args, "gradient_checkpointing_kwargs"
+                ) and "gradient_checkpointing_kwargs" in list(
+                    inspect.signature(prepare_model_for_kbit_training).parameters
+                )
+
+                prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
+
+                if _support_gc_kwargs:
+                    prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
+
+                model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)
+            elif getattr(args, "gradient_checkpointing", False):
+                # For backward compatibility with older versions of transformers
+                if hasattr(model, "enable_input_require_grads"):
+                    model.enable_input_require_grads()
+                else:
+
+                    def make_inputs_require_grad(module, input, output):
+                        output.requires_grad_(True)
+
+                    model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+            # get peft model with the given config
+            model = model
+            if args.bf16 and getattr(model, "is_loaded_in_4bit", False):
+                peft_module_casting_to_bf16(model)
+                # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager
+                self._peft_has_been_casted_to_bf16 = True
+
+        # For models that use gradient_checkpointing, we need to attach a hook that enables input
+        # to explicitly have `requires_grad=True`, otherwise training will either silently
+        # fail or completely fail.
+        elif getattr(args, "gradient_checkpointing", False):
+            # For backward compatibility with older versions of transformers
+            if hasattr(model, "enable_input_require_grads"):
+                model.enable_input_require_grads()
+            else:
+
+                def make_inputs_require_grad(module, input, output):
+                    output.requires_grad_(True)
+
+                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+        if args.generate_during_eval and not (is_wandb_available() or is_comet_available()):
+            raise ValueError(
+                "`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
+                " Please install `wandb` or `comet-ml` to resolve."
+            )
+
+        if model is not None:
+            self.is_encoder_decoder = model.config.is_encoder_decoder
+        elif args.is_encoder_decoder is None:
+            raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.")
+        else:
+            self.is_encoder_decoder = args.is_encoder_decoder
+
+        self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)
+        self.model_adapter_name = model_adapter_name
+        self.ref_adapter_name = ref_adapter_name
+
+        if ref_model:
+            self.ref_model = ref_model
+        elif self.is_peft_model or args.precompute_ref_log_probs:
+            # The `model` with adapters turned off will be used as the reference model
+            self.ref_model = None
+        else:
+            self.ref_model = create_reference_model(model)
+
+        if processing_class is None:
+            raise ValueError(
+                "max_length or a processing_class must be specified when using the default DPODataCollatorWithPadding"
+            )
+        if args.max_length is None:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding, you should set `max_length` in the KTOTrainer's init"
+                " it will be set to `512` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_length = 512
+        if args.max_length is not None:
+            max_length = args.max_length
+
+        if args.max_prompt_length is None:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the KTOTrainer's init"
+                " it will be set to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_prompt_length = 128
+        if args.max_prompt_length is not None:
+            max_prompt_length = args.max_prompt_length
+
+        max_completion_length = None
+        if args.max_completion_length is None and self.is_encoder_decoder:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding with an encoder decoder architecture, you should set `max_completion_length` in the KTOTrainer's init"
+                " it will be set to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_completion_length = 128
+        if args.max_completion_length is not None and self.is_encoder_decoder:
+            max_completion_length = args.max_completion_length
+
+        if data_collator is None:
+            data_collator = DPODataCollatorWithPadding(
+                pad_token_id=processing_class.pad_token_id,
+                label_pad_token_id=args.label_pad_token_id,
+                is_encoder_decoder=self.is_encoder_decoder,
+            )
+
+            if args.remove_unused_columns:
+                args.remove_unused_columns = False
+                # warn users
+                warnings.warn(
+                    "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your KTOConfig"
+                    " we have set it for you, but you should do it yourself in the future.",
+                    UserWarning,
+                )
+
+            self.use_dpo_data_collator = True
+        else:
+            self.use_dpo_data_collator = False
+
+        # Disable dropout in the model and reference model
+        if args.disable_dropout:
+            disable_dropout_in_model(model)
+            if self.ref_model is not None:
+                disable_dropout_in_model(self.ref_model)
+
+        self.loss_type = args.loss_type
+        self.max_length = max_length
+        self.generate_during_eval = args.generate_during_eval
+        self.label_pad_token_id = args.label_pad_token_id
+        self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id
+        self.max_prompt_length = max_prompt_length
+        self.truncation_mode = args.truncation_mode
+        self.max_completion_length = max_completion_length
+        self.processing_class = processing_class
+        self.precompute_ref_log_probs = args.precompute_ref_log_probs
+
+        # Not all losses require a KL calculation
+        self.calculate_KL = True
+        if self.loss_type in ["apo_zero_unpaired"]:
+            self.calculate_KL = False
+
+        # Since ref_logs are precomputed on the first call to get_train/eval_dataloader
+        # keep track of first called to avoid computation of future calls
+        self._precomputed_train_ref_log_probs = False
+        self._precomputed_eval_ref_log_probs = False
+
+        # metric
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+
+        # KTO parameter
+        self.beta = args.beta
+        self.desirable_weight = args.desirable_weight
+        self.undesirable_weight = args.undesirable_weight
+        self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)
+        self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0)
+        if self.aux_loss_enabled and self.aux_loss_coef == 0.0:
+            warnings.warn(
+                "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to "
+                "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value "
+                "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary "
+                "loss.",
+                UserWarning,
+            )
+
+        # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
+        # input tensor associated with the key "input_ids". However, in KTO, the sampled data does not include the
+        # "input_ids" key. Instead, the available keys are "prompt_input_ids" and "completion_input_ids". As a result,
+        # the trainer issues the warning: "Could not estimate the number of tokens of the input, floating-point
+        # operations will not be computed." To suppress this warning, we set the "estimate_tokens" key in the model's
+        # "warnings_issued" dictionary to True. This acts as a flag to indicate that the warning has already been
+        # issued.
+        model.warnings_issued["estimate_tokens"] = True
+
+        # Compute that only on the main process for faster data processing.
+        # see: https://github.com/huggingface/trl/pull/1255
+        with PartialState().local_main_process_first():
+            # Extract the prompt if needed
+            train_dataset = train_dataset.map(
+                maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from train dataset"
+            )
+            # Unpair the dataset if needed
+            train_dataset = maybe_unpair_preference_dataset(
+                train_dataset, args.dataset_num_proc, desc="Unpairing train dataset"
+            )
+            # Apply the chat template if needed
+            train_dataset = train_dataset.map(
+                maybe_apply_chat_template,
+                fn_kwargs={"tokenizer": processing_class},
+                num_proc=args.dataset_num_proc,
+                desc="Applying chat template to train dataset",
+            )
+            if eval_dataset is not None:
+                eval_dataset = eval_dataset.map(
+                    maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from eval dataset"
+                )
+                eval_dataset = maybe_unpair_preference_dataset(
+                    eval_dataset, args.dataset_num_proc, desc="Unpairing eval dataset"
+                )
+                eval_dataset = eval_dataset.map(
+                    maybe_apply_chat_template,
+                    fn_kwargs={"tokenizer": processing_class},
+                    num_proc=args.dataset_num_proc,
+                    desc="Applying chat template to eval dataset",
+                )
+
+            # Tokenize and prepare the training datasets
+            train_dataset = train_dataset.map(
+                _tokenize,
+                batched=True,
+                fn_kwargs={"tokenizer": self.processing_class},
+                num_proc=args.dataset_num_proc,
+                desc="Tokenizing train dataset",
+            )
+
+            fn_kwargs = {
+                "prefix": "",
+                "is_encoder_decoder": self.is_encoder_decoder,
+                "tokenizer": self.processing_class,
+                "max_length": self.max_length,
+                "truncation_mode": self.truncation_mode,
+                "label_pad_token_id": self.label_pad_token_id,
+                "max_prompt_length": self.max_prompt_length,
+                "max_completion_length": self.max_completion_length,
+            }
+
+            train_dataset = train_dataset.map(
+                _process_tokens,
+                fn_kwargs=fn_kwargs,
+                num_proc=args.dataset_num_proc,
+                desc="Processing tokenized train dataset",
+            )
+
+            # Tokenize and prepare the eval datasets
+            if eval_dataset is not None:
+                eval_dataset = eval_dataset.map(
+                    _tokenize,
+                    fn_kwargs={"tokenizer": self.processing_class},
+                    batched=True,
+                    num_proc=args.dataset_num_proc,
+                    desc="Tokenizing eval dataset",
+                )
+
+                eval_dataset = eval_dataset.map(
+                    _process_tokens,
+                    fn_kwargs=fn_kwargs,
+                    num_proc=args.dataset_num_proc,
+                    desc="Processing tokenized eval dataset",
+                )
+
+            # Get KL datasets if needed
+            if self.calculate_KL:
+                if args.per_device_train_batch_size <= 1:
+                    raise ValueError(
+                        "Actual (not effective) batch size must be > 1. KTO will not work properly because the KL term will be equivalent to the implied reward."
+                    )
+
+                # create pairs for estimating the KL term by flipping the matched pairs in each batch of size total_batch_size
+                # i.e., (x_1, y_1), ..., (x_n, y_n) --> (x_1, y_n), ..., (x_n, y_1) = (x'_1, y'_1), ..., (x'_n, y'_n)
+                train_kl_dataset = train_dataset.map(
+                    _get_kl_dataset,
+                    batched=True,
+                    batch_size=args.per_device_train_batch_size,
+                    num_proc=args.dataset_num_proc,
+                    desc="Extracting KL train dataset",
+                )
+
+                fn_kwargs["prefix"] = "KL_"
+                train_kl_dataset = train_kl_dataset.map(
+                    _process_tokens,
+                    fn_kwargs=fn_kwargs,
+                    num_proc=args.dataset_num_proc,
+                    remove_columns=[c for c in train_kl_dataset.column_names if c in train_dataset.column_names],
+                    desc="Processing tokenized train KL dataset",
+                )
+
+                # merge the datasets
+                train_dataset = concatenate_datasets([train_dataset, train_kl_dataset], axis=1)
+
+                if eval_dataset is not None:
+                    # Get KL dataset
+                    eval_kl_dataset = eval_dataset.map(
+                        _get_kl_dataset,
+                        batched=True,
+                        batch_size=args.per_device_train_batch_size,
+                        num_proc=args.dataset_num_proc,
+                        desc="Extracting eval KL dataset",
+                    )
+
+                    eval_kl_dataset = eval_kl_dataset.map(
+                        _process_tokens,
+                        fn_kwargs=fn_kwargs,
+                        num_proc=args.dataset_num_proc,
+                        remove_columns=[c for c in eval_kl_dataset.column_names if c in eval_dataset.column_names],
+                        desc="Processing tokenized eval KL dataset",
+                    )
+
+                    # merge the datasets
+                    eval_dataset = concatenate_datasets([eval_dataset, eval_kl_dataset], axis=1)
+
+            # calculate dataset desirability balance
+            num_desirable = max(sum(train_dataset["label"]), 1)
+            num_undesirable = max(len(train_dataset["label"]) - num_desirable, 1)  # "label" is binary
+
+            if num_desirable != num_undesirable:
+                # The lower and upper bounds come from Eq. (8) of https://huggingface.co/papers/2402.01306
+                des_weight_lower_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1, 2)
+                des_weight_upper_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1.33, 2)
+                und_weight_lower_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1.33, 2)
+                und_weight_upper_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1, 2)
+
+                des_weight_in_range = des_weight_lower_bound <= self.desirable_weight <= des_weight_upper_bound
+                und_weight_in_range = und_weight_lower_bound <= self.undesirable_weight <= und_weight_upper_bound
+
+                if not (des_weight_in_range or und_weight_in_range):
+                    warnings.warn(
+                        "You have different amounts of desirable/positive and undesirable/negative examples but the "
+                        "weights on the desirable and undesirable losses don't seem to be in an ideal range. Based "
+                        f"on your data, we recommend EITHER "
+                        f"desirable_weight in [{des_weight_lower_bound}, {des_weight_upper_bound}] or "
+                        f"undesirable_weight in [{und_weight_lower_bound}, {und_weight_upper_bound}] (but NOT BOTH). "
+                        "See the documentation on how to optimally set these weights.",
+                        UserWarning,
+                    )
+
+        super().__init__(
+            model=model,
+            args=args,
+            data_collator=data_collator,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            processing_class=processing_class,
+            model_init=model_init,
+            compute_metrics=compute_metrics,
+            callbacks=callbacks,
+            optimizers=optimizers,
+            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+        )
+
+        # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the
+        # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set
+        # self.model_accepts_loss_kwargs to False to enable scaling.
+        self.model_accepts_loss_kwargs = False
+
+        # Add tags for models that have been loaded with the correct transformers version
+        if hasattr(self.model, "add_model_tags"):
+            self.model.add_model_tags(self._tag_names)
+
+        if not hasattr(self, "accelerator"):
+            raise AttributeError(
+                "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`."
+            )
+
+        # Deepspeed Zero-3 does not support precompute_ref_log_probs
+        if self.is_deepspeed_enabled:
+            if self.accelerator.state.deepspeed_plugin.zero_stage == 3 and self.precompute_ref_log_probs:
+                raise ValueError(
+                    "You cannot use `precompute_ref_log_probs=True` with Deepspeed ZeRO-3. Please set `precompute_ref_log_probs=False`."
+                )
+
+        if self.ref_model is None:
+            if not (self.is_peft_model or self.precompute_ref_log_probs):
+                raise ValueError(
+                    "No reference model and model is not a Peft model. Try setting `precompute_ref_log_probs=True`"
+                )
+        else:
+            if self.is_deepspeed_enabled:
+                self.ref_model = self._prepare_deepspeed(self.ref_model)
+            else:
+                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
+
+    def _prepare_deepspeed(self, model: PreTrainedModelWrapper):
+        # Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
+        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
+        config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config)
+
+        if model is not None:
+            if hasattr(model, "config"):
+                hidden_size = (
+                    max(model.config.hidden_sizes)
+                    if getattr(model.config, "hidden_sizes", None)
+                    else getattr(model.config, "hidden_size", None)
+                )
+                if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3:
+                    # Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0`
+                    # This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081
+                    config_kwargs.update(
+                        {
+                            "zero_optimization.reduce_bucket_size": hidden_size * hidden_size,
+                            "zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size,
+                            "zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size,
+                        }
+                    )
+
+        # If ZeRO-3 is used, we shard both the active and reference model.
+        # Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0)
+        if config_kwargs["zero_optimization"]["stage"] != 3:
+            config_kwargs["zero_optimization"]["stage"] = 0
+        model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
+        model.eval()
+        return model
+
+    @contextmanager
+    def null_ref_context(self):
+        """Context manager for handling null reference model (that is, peft adapter manipulation)."""
+        with (
+            self.accelerator.unwrap_model(self.model).disable_adapter()
+            if self.is_peft_model and not self.ref_adapter_name
+            else nullcontext()
+        ):
+            if self.ref_adapter_name:
+                self.model.set_adapter(self.ref_adapter_name)
+            yield
+            if self.ref_adapter_name:
+                self.model.set_adapter(self.model_adapter_name or "default")
+
+    def get_train_dataloader(self) -> DataLoader:
+        """
+        Returns the training [`~torch.utils.data.DataLoader`].
+
+        Subclass of transformers.src.transformers.trainer.get_train_dataloader to precompute `ref_log_probs`.
+        """
+
+        if self.precompute_ref_log_probs and not self._precomputed_train_ref_log_probs:
+            dataloader_params = {
+                "batch_size": self.args.per_device_train_batch_size,
+                "collate_fn": self.data_collator,
+                "num_workers": self.args.dataloader_num_workers,
+                "pin_memory": self.args.dataloader_pin_memory,
+                "shuffle": False,
+            }
+
+            # prepare dataloader
+            data_loader = self.accelerator.prepare(DataLoader(self.train_dataset, **dataloader_params))
+            reference_completion_logps = []
+            reference_KL_logps = []
+
+            for padded_batch in tqdm(iterable=data_loader, desc="Train dataset reference log probs"):
+                reference_completion_logp, reference_KL_logp = self.compute_reference_log_probs(padded_batch)
+
+                reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp)
+                reference_completion_logps.append(reference_completion_logp.cpu())
+
+                if self.calculate_KL:
+                    reference_KL_logp = self.accelerator.gather_for_metrics(reference_KL_logp)
+                    reference_KL_logps.append(reference_KL_logp.cpu())
+
+            self.train_dataset = self.train_dataset.add_column(
+                name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy()
+            )
+
+            if self.calculate_KL:
+                self.train_dataset = self.train_dataset.add_column(
+                    name="reference_KL_logps", column=torch.cat(reference_KL_logps).float().numpy()
+                )
+
+            self._precomputed_train_ref_log_probs = True
+
+        return super().get_train_dataloader()
+
+    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
+        """
+        Returns the evaluation [`~torch.utils.data.DataLoader`].
+
+        Subclass of transformers.src.transformers.trainer.get_eval_dataloader to precompute `ref_log_probs`.
+
+        Args:
+            eval_dataset (`torch.utils.data.Dataset`, *optional*):
+                If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted
+                by the `model.forward()` method are automatically removed. It must implement `__len__`.
+        """
+        if eval_dataset is None and self.eval_dataset is None:
+            raise ValueError("Trainer: evaluation requires an eval_dataset.")
+        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
+
+        if self.precompute_ref_log_probs and not self._precomputed_eval_ref_log_probs:
+            dataloader_params = {
+                "batch_size": self.args.per_device_eval_batch_size,
+                "collate_fn": self.data_collator,
+                "num_workers": self.args.dataloader_num_workers,
+                "pin_memory": self.args.dataloader_pin_memory,
+                "shuffle": False,
+            }
+
+            # prepare dataloader
+            data_loader = self.accelerator.prepare(DataLoader(eval_dataset, **dataloader_params))
+
+            reference_completion_logps = []
+            reference_KL_logps = []
+
+            for padded_batch in tqdm(iterable=data_loader, desc="Eval dataset reference log probs"):
+                reference_completion_logp, reference_KL_logp = self.compute_reference_log_probs(padded_batch)
+
+                reference_completion_logp = self.accelerator.gather_for_metrics(reference_completion_logp)
+                reference_completion_logps.append(reference_completion_logp.cpu())
+
+                if self.calculate_KL:
+                    reference_KL_logp = self.accelerator.gather_for_metrics(reference_KL_logp)
+                    reference_KL_logps.append(reference_KL_logp.cpu())
+
+            eval_dataset = eval_dataset.add_column(
+                name="reference_logps", column=torch.cat(reference_completion_logps).float().numpy()
+            )
+            if self.calculate_KL:
+                eval_dataset = eval_dataset.add_column(
+                    name="reference_KL_logps", column=torch.cat(reference_KL_logps).float().numpy()
+                )
+
+            # Save calculated reference_chosen_logps and reference_rejected_logps to the eval_dataset for subsequent runs
+            if self.eval_dataset is not None:
+                self.eval_dataset = eval_dataset
+            self._precomputed_eval_ref_log_probs = True
+
+        return super().get_eval_dataloader(eval_dataset=eval_dataset)
+
+    def compute_reference_log_probs(self, padded_batch: dict) -> dict:
+        """Computes log probabilities of the reference model for a single padded batch of a KTO specific dataset."""
+        with torch.no_grad():
+            if self.ref_model is None:
+                with self.null_ref_context():
+                    if self.is_encoder_decoder:
+                        completion_logits = self.model(
+                            padded_batch["prompt_input_ids"],
+                            attention_mask=padded_batch["prompt_attention_mask"],
+                            decoder_input_ids=padded_batch.get("completion_decoder_input_ids"),
+                            labels=padded_batch["completion_labels"],
+                        ).logits
+
+                        if self.calculate_KL:
+                            KL_logits = self.model(
+                                padded_batch["KL_prompt_input_ids"],
+                                attention_mask=padded_batch["KL_prompt_attention_mask"],
+                                decoder_input_ids=padded_batch.get("KL_completion_decoder_input_ids"),
+                                labels=padded_batch["KL_completion_labels"],
+                            ).logits
+                    else:
+                        completion_logits = self.model(
+                            padded_batch["completion_input_ids"],
+                            attention_mask=padded_batch["completion_attention_mask"],
+                        ).logits
+
+                        if self.calculate_KL:
+                            KL_logits = self.model(
+                                padded_batch["KL_completion_input_ids"],
+                                attention_mask=padded_batch["KL_completion_attention_mask"],
+                            ).logits
+            else:
+                if self.is_encoder_decoder:
+                    completion_logits = self.ref_model(
+                        padded_batch["prompt_input_ids"],
+                        attention_mask=padded_batch["prompt_attention_mask"],
+                        decoder_input_ids=padded_batch.get("completion_decoder_input_ids"),
+                        labels=padded_batch["completion_labels"],
+                    ).logits
+
+                    if self.calculate_KL:
+                        KL_logits = self.ref_model(
+                            padded_batch["KL_prompt_input_ids"],
+                            attention_mask=padded_batch["KL_prompt_attention_mask"],
+                            decoder_input_ids=padded_batch.get("KL_completion_decoder_input_ids"),
+                            labels=padded_batch["KL_completion_labels"],
+                        ).logits
+                else:
+                    completion_logits = self.ref_model(
+                        padded_batch["completion_input_ids"], attention_mask=padded_batch["completion_attention_mask"]
+                    ).logits
+
+                    if self.calculate_KL:
+                        KL_logits = self.ref_model(
+                            padded_batch["KL_completion_input_ids"],
+                            attention_mask=padded_batch["KL_completion_attention_mask"],
+                        ).logits
+
+        completion_logps = self.get_batch_logps(
+            completion_logits,
+            padded_batch["completion_labels"],
+            average_log_prob=False,
+            is_encoder_decoder=self.is_encoder_decoder,
+            label_pad_token_id=self.label_pad_token_id,
+        )
+
+        if self.calculate_KL:
+            KL_logps = self.get_batch_logps(
+                KL_logits,
+                padded_batch["KL_completion_labels"],
+                average_log_prob=False,
+                is_encoder_decoder=self.is_encoder_decoder,
+                label_pad_token_id=self.label_pad_token_id,
+            )
+        else:
+            KL_logps = None
+
+        return completion_logps, KL_logps
+
+    @staticmethod
+    def get_batch_logps(
+        logits: torch.FloatTensor,
+        labels: torch.LongTensor,
+        average_log_prob: bool = False,
+        label_pad_token_id: int = -100,
+        is_encoder_decoder: bool = False,
+    ) -> torch.FloatTensor:
+        """Compute the log probabilities of the given labels under the given logits.
+
+        Args:
+            logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)
+            labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length)
+            average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.
+
+        Returns:
+            A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.
+        """
+        if logits.shape[:-1] != labels.shape:
+            raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.")
+
+        if not is_encoder_decoder:
+            labels = labels[:, 1:].clone()
+            logits = logits[:, :-1, :]
+        else:
+            # Fixes end-dec RuntimeError
+            labels = labels.clone()
+
+        loss_mask = labels != label_pad_token_id
+
+        # dummy token; we'll ignore the losses on these tokens later
+        labels[labels == label_pad_token_id] = 0
+
+        per_token_logps = selective_log_softmax(logits, labels)
+
+        if average_log_prob:
+            return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
+        else:
+            return (per_token_logps * loss_mask).sum(-1)
+
+    def forward(
+        self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]]
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+        if self.calculate_KL:
+            KL_logps = None
+            KL_model_kwargs = (
+                {
+                    "input_ids": batch["KL_prompt_input_ids"],
+                    "attention_mask": batch["KL_prompt_attention_mask"],
+                    "labels": batch["KL_completion_labels"],
+                    "decoder_input_ids": batch.get("KL_completion_decoder_input_ids"),
+                }
+                if self.is_encoder_decoder
+                else {
+                    "input_ids": batch["KL_completion_input_ids"],
+                    "attention_mask": batch["KL_completion_attention_mask"],
+                }
+            )
+            with torch.no_grad():
+                KL_logits = model(
+                    **KL_model_kwargs,
+                ).logits
+
+            KL_logps = self.get_batch_logps(
+                KL_logits,
+                batch["KL_completion_labels"],
+                average_log_prob=False,
+                is_encoder_decoder=self.is_encoder_decoder,
+                label_pad_token_id=self.label_pad_token_id,
+            )
+        else:
+            KL_logps = None
+
+        model_kwargs = (
+            {
+                "labels": batch["completion_labels"],
+                "decoder_input_ids": batch.get("completion_decoder_input_ids"),
+            }
+            if self.is_encoder_decoder
+            else {}
+        )
+        if self.aux_loss_enabled:
+            model_kwargs["output_router_logits"] = True
+
+        outputs = model(
+            batch["completion_input_ids"],
+            attention_mask=batch["completion_attention_mask"],
+            **model_kwargs,
+        )
+        completion_logits = outputs.logits
+
+        completion_logps = self.get_batch_logps(
+            completion_logits,
+            batch["completion_labels"],
+            average_log_prob=False,
+            is_encoder_decoder=self.is_encoder_decoder,
+            label_pad_token_id=self.label_pad_token_id,
+        )
+
+        if completion_logps.shape[0] != len(batch["label"]):
+            raise ValueError(
+                "There is a mismatch between the number of examples in this batch and the number of "
+                "examples for which an output sequence was predicted."
+            )
+
+        chosen_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is True]
+        rejected_idx = [i for i in range(completion_logps.shape[0]) if batch["label"][i] is False]
+
+        chosen_logps = completion_logps[chosen_idx, ...]
+        rejected_logps = completion_logps[rejected_idx, ...]
+
+        chosen_logits = completion_logits[chosen_idx, ...]
+        rejected_logits = completion_logits[rejected_idx, ...]
+
+        if self.aux_loss_enabled:
+            return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, KL_logps, outputs.aux_loss)
+        else:
+            return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, KL_logps)
+
+    def kto_loss(
+        self,
+        policy_chosen_logps: torch.FloatTensor,
+        policy_rejected_logps: torch.FloatTensor,
+        policy_KL_logps: torch.FloatTensor,
+        reference_chosen_logps: torch.FloatTensor,
+        reference_rejected_logps: torch.FloatTensor,
+        reference_KL_logps: torch.FloatTensor,
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+        """Compute the KTO loss for a batch of policy and reference model log probabilities.
+
+        Args:
+            policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (num(chosen) in batch_size,)
+            policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (num(rejected) in batch_size,)
+            policy_KL_logps: Log probabilities of the policy model for the KL responses. Shape: (batch_size,)
+            reference_chosen_logps: Log probabilities of the reference model for the chosen responses. Shape: (num(chosen) in batch_size,)
+            reference_rejected_logps: Log probabilities of the reference model for the rejected responses. Shape: (num(rejected) in batch_size,)
+            reference_KL_logps: Log probabilities of the reference model for the KL responses. Shape: (batch_size,)
+
+        Returns:
+            A tuple of four tensors: (losses, chosen_rewards, rejected_rewards, KL).
+            The losses tensor contains the KTO loss for each example in the batch.
+            The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.
+            The KL tensor contains the detached KL divergence estimate between the policy and reference models.
+        """
+        if self.calculate_KL:
+            kl = (policy_KL_logps - reference_KL_logps).mean().detach()
+            kl = self.accelerator.gather_for_metrics(kl).mean().clamp(min=0)
+        else:
+            kl = torch.zeros(1).to(policy_chosen_logps.device)
+
+        # Chosen losses
+        if policy_chosen_logps.shape[0] != 0 or reference_chosen_logps.shape[0] != 0:
+            chosen_logratios = policy_chosen_logps - reference_chosen_logps
+
+            if self.loss_type == "kto":
+                # Eqn (7) of the KTO paper (https://huggingface.co/papers/2402.01306)
+                chosen_losses = 1 - F.sigmoid(self.beta * (chosen_logratios - kl))
+            elif self.loss_type == "apo_zero_unpaired":
+                # Unpaired variant of Eqn (7) of the APO paper (https://huggingface.co/papers/2408.06266)
+                # Use this loss when you believe the chosen outputs are better than your model's default output
+                chosen_losses = 1 - F.sigmoid(self.beta * chosen_logratios)
+
+            chosen_rewards = self.beta * chosen_logratios.detach()
+
+        else:
+            # lists can't be empty -- if they are, then accelerate.gather will hang
+            chosen_losses = torch.Tensor([]).to(self.accelerator.device)
+            chosen_rewards = torch.Tensor([]).to(self.accelerator.device)
+
+        # Rejected losses
+        if policy_rejected_logps.shape[0] != 0 or reference_rejected_logps.shape[0] != 0:
+            rejected_logratios = policy_rejected_logps - reference_rejected_logps
+
+            if self.loss_type == "kto":
+                rejected_losses = 1 - F.sigmoid(self.beta * (kl - rejected_logratios))
+            elif self.loss_type == "apo_zero_unpaired":
+                rejected_losses = F.sigmoid(self.beta * rejected_logratios)
+
+            rejected_rewards = self.beta * rejected_logratios.detach()
+        else:
+            # lists can't be empty -- if they are, then accelerate.gather will hang
+            rejected_losses = torch.Tensor([]).to(self.accelerator.device)
+            rejected_rewards = torch.Tensor([]).to(self.accelerator.device)
+
+        losses = torch.cat(
+            (self.desirable_weight * chosen_losses, self.undesirable_weight * rejected_losses),
+            0,
+        )
+
+        return losses, chosen_rewards, rejected_rewards, kl
+
+    def get_batch_loss_metrics(
+        self,
+        model,
+        batch: dict[str, Union[list, torch.LongTensor]],
+    ):
+        """Compute the KTO loss and other metrics for the given batch of inputs for train or test."""
+        metrics = {}
+        batch = {k: (v.to(self.accelerator.device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()}
+
+        forward_output = self.forward(model, batch)
+        (
+            policy_chosen_logps,
+            policy_rejected_logps,
+            policy_chosen_logits,
+            policy_rejected_logits,
+            policy_KL_logps,
+        ) = forward_output[:5]
+        if self.aux_loss_enabled:
+            aux_loss = forward_output[5]
+
+        # if reference_logps in batch use them, otherwise use the reference model
+        if "reference_logps" in batch:
+            chosen_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is True]
+            rejected_idx = [i for i in range(batch["reference_logps"].shape[0]) if batch["label"][i] is False]
+
+            reference_chosen_logps = batch["reference_logps"][chosen_idx, ...]
+            reference_rejected_logps = batch["reference_logps"][rejected_idx, ...]
+            if self.calculate_KL:
+                reference_KL_logps = batch["reference_KL_logps"]
+            else:
+                reference_KL_logps = None
+        else:
+            with torch.no_grad():
+                if self.ref_model is None:
+                    with self.null_ref_context():
+                        (
+                            reference_chosen_logps,
+                            reference_rejected_logps,
+                            _,
+                            _,
+                            reference_KL_logps,
+                        ) = self.forward(self.model, batch)[:5]
+                else:
+                    (
+                        reference_chosen_logps,
+                        reference_rejected_logps,
+                        _,
+                        _,
+                        reference_KL_logps,
+                    ) = self.forward(self.ref_model, batch)[:5]
+
+        losses, chosen_rewards, rejected_rewards, kl = self.kto_loss(
+            policy_chosen_logps,
+            policy_rejected_logps,
+            policy_KL_logps,
+            reference_chosen_logps,
+            reference_rejected_logps,
+            reference_KL_logps,
+        )
+        metrics["kl"] = kl.item()
+
+        num_chosen = torch.Tensor([len(chosen_rewards)]).to(self.accelerator.device)
+        num_rejected = torch.Tensor([len(rejected_rewards)]).to(self.accelerator.device)
+
+        all_num_chosen = self.accelerator.gather_for_metrics(num_chosen).sum().item()
+        all_num_rejected = self.accelerator.gather_for_metrics(num_rejected).sum().item()
+
+        if all_num_chosen > 0:
+            metrics["rewards/chosen_sum"] = (
+                self.accelerator.gather_for_metrics(chosen_rewards.nansum()).nansum().item()
+            )
+            metrics["logps/chosen_sum"] = (
+                self.accelerator.gather_for_metrics(policy_chosen_logps.nansum()).nansum().item()
+            )
+            metrics["logits/chosen_sum"] = (
+                self.accelerator.gather_for_metrics(policy_chosen_logits.nansum()).nansum().item()
+            )
+            metrics["count/chosen"] = all_num_chosen
+
+        if all_num_rejected > 0:
+            metrics["rewards/rejected_sum"] = (
+                self.accelerator.gather_for_metrics(rejected_rewards.nansum()).nansum().item()
+            )
+            metrics["logps/rejected_sum"] = (
+                self.accelerator.gather_for_metrics(policy_rejected_logps.nansum()).nansum().item()
+            )
+            metrics["logits/rejected_sum"] = (
+                self.accelerator.gather_for_metrics(policy_rejected_logits.nansum()).nansum().item()
+            )
+            metrics["count/rejected"] = all_num_rejected
+
+        loss = losses.nanmean()
+        if self.aux_loss_enabled:
+            loss += self.aux_loss_coef * aux_loss
+
+        return loss, metrics
+
+    def compute_loss(
+        self,
+        model: Union[PreTrainedModel, nn.Module],
+        inputs: dict[str, Union[torch.Tensor, Any]],
+        return_outputs=False,
+        num_items_in_batch=None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]:
+        compute_loss_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+
+        with compute_loss_context_manager:
+            loss, metrics = self.get_batch_loss_metrics(model, inputs)
+
+        # Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class:
+        loss = loss.to(self.args.device)
+        # force log the metrics
+        if self.accelerator.is_main_process:
+            self.store_metrics(metrics, train_eval="train")
+
+        if return_outputs:
+            return (loss, metrics)
+        return loss
+
+    def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None:
+        for key, value in metrics.items():
+            self._stored_metrics[train_eval][key].append(value)
+
+    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
+        if self.train_dataset is None or not has_length(self.train_dataset):
+            return None
+        return SequentialSampler(self.train_dataset)
+
+    def generate_from_model_and_ref(self, model, batch: dict[str, torch.LongTensor]) -> tuple[str, str]:
+        """Generate samples from the model and reference model for the given batch of inputs."""
+
+        # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with
+        # the torch cuda amp context manager as some hidden states are silently casted to full precision.
+        generate_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+
+        with generate_context_manager:
+            policy_output = model.generate(
+                input_ids=batch["prompt_input_ids"],
+                attention_mask=batch["prompt_attention_mask"],
+                max_length=self.max_length,
+                do_sample=True,
+                pad_token_id=self.processing_class.pad_token_id,
+            )
+
+            # if reference_output in batch use that otherwise use the reference model
+            if "reference_output" in batch:
+                reference_output = batch["reference_output"]
+            else:
+                if self.ref_model is None:
+                    with self.null_ref_context():
+                        reference_output = self.model.generate(
+                            input_ids=batch["prompt_input_ids"],
+                            attention_mask=batch["prompt_attention_mask"],
+                            max_length=self.max_length,
+                            do_sample=True,
+                            pad_token_id=self.processing_class.pad_token_id,
+                        )
+                else:
+                    reference_output = self.ref_model.generate(
+                        input_ids=batch["prompt_input_ids"],
+                        attention_mask=batch["prompt_attention_mask"],
+                        max_length=self.max_length,
+                        do_sample=True,
+                        pad_token_id=self.processing_class.pad_token_id,
+                    )
+
+        policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id)
+        policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True)
+
+        reference_output = pad_to_length(reference_output, self.max_length, self.processing_class.pad_token_id)
+        reference_output_decoded = self.processing_class.batch_decode(reference_output, skip_special_tokens=True)
+
+        return policy_output_decoded, reference_output_decoded
+
+    def prediction_step(
+        self,
+        model: Union[PreTrainedModel, nn.Module],
+        inputs: dict[str, Union[torch.Tensor, Any]],
+        prediction_loss_only: bool,
+        ignore_keys: Optional[list[str]] = None,
+    ):
+        if ignore_keys is None:
+            if hasattr(model, "config"):
+                ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", [])
+            else:
+                ignore_keys = []
+
+        prediction_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+        with torch.no_grad(), prediction_context_manager:
+            loss, metrics = self.get_batch_loss_metrics(model, inputs)
+
+        # force log the metrics
+        if self.accelerator.is_main_process:
+            self.store_metrics(metrics, train_eval="eval")
+
+        if prediction_loss_only:
+            return (loss.detach(), None, None)
+
+        # logits for the chosen and rejected samples from model
+        logits_dict = {
+            "eval_logits/chosen": metrics["logits/chosen"],
+            "eval_logits/rejected": metrics["logits/rejected"],
+        }
+        logits = torch.tensor(
+            [v for k, v in logits_dict.items() if k not in ignore_keys], device=self.accelerator.device
+        )
+        labels = torch.zeros(logits.shape[0], device=self.accelerator.device)
+
+        return (loss.detach(), logits, labels)
+
+    def evaluation_loop(
+        self,
+        dataloader: DataLoader,
+        description: str,
+        prediction_loss_only: Optional[bool] = None,
+        ignore_keys: Optional[list[str]] = None,
+        metric_key_prefix: str = "eval",
+    ) -> EvalLoopOutput:
+        """
+        Overriding built-in evaluation loop to store metrics for each batch.
+        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.
+
+        Works both with or without labels.
+        """
+
+        # Sample and save to game log if requested (for one batch to save time)
+        if self.generate_during_eval:
+            # Generate random indices within the range of the total number of samples
+            num_samples = len(dataloader.dataset)
+            random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size)
+
+            # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader
+            random_batch_dataset = dataloader.dataset.select(random_indices)
+            random_batch = self.data_collator(random_batch_dataset)
+            random_batch = self._prepare_inputs(random_batch)
+
+            target_indicies = [i for i in range(len(random_batch["label"])) if random_batch["label"][i] is False]
+            target_batch = {
+                "prompt_input_ids": random_batch["prompt_input_ids"][target_indicies],
+                "prompt_attention_mask": random_batch["prompt_attention_mask"][target_indicies],
+                "prompt": itemgetter(*target_indicies)(random_batch["prompt"]),
+            }
+            policy_output_decoded, ref_output_decoded = self.generate_from_model_and_ref(self.model, target_batch)
+
+            table = pd.DataFrame(
+                columns=["Prompt", "Policy", "Ref Model"],
+                data=[
+                    [prompt, pol[len(prompt) :], ref[len(prompt) :]]
+                    for prompt, pol, ref in zip(target_batch["prompt"], policy_output_decoded, ref_output_decoded)
+                ],
+            )
+            if "wandb" in self.args.report_to:
+                wandb.log({"game_log": wandb.Table(data=table)})
+
+            if "comet_ml" in self.args.report_to:
+                log_table_to_comet_experiment(
+                    name="game_log.csv",
+                    table=table,
+                )
+
+        # Base evaluation
+        initial_output = super().evaluation_loop(
+            dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix
+        )
+
+        return initial_output
+
+    def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None:
+        """
+        Log `logs` on the various objects watching training, including stored metrics.
+
+        Args:
+            logs (`dict[str, float]`):
+                The values to log.
+            start_time (`float` or `None`, *optional*, defaults to `None`):
+                Start time of the training.
+        """
+        # logs either has 'loss' or 'eval_loss'
+        train_eval = "train" if "loss" in logs else "eval"
+        # train metrics should have no prefix, eval should have 'eval_'
+        prefix = "eval_" if train_eval == "eval" else ""
+        # accumulate average metrics from sums and lengths
+        for split in ["chosen", "rejected"]:
+            if f"count/{split}" in self._stored_metrics[train_eval]:
+                count_sum = torch.Tensor(self._stored_metrics[train_eval][f"count/{split}"]).sum().item()
+                for metric in ["rewards", "logps", "logits"]:
+                    logs[f"{prefix}{metric}/{split}"] = (
+                        torch.Tensor(self._stored_metrics[train_eval][f"{metric}/{split}_sum"]).sum().item()
+                        / count_sum
+                    )
+                    # delete obsolete metric
+                    del self._stored_metrics[train_eval][f"{metric}/{split}_sum"]
+                del self._stored_metrics[train_eval][f"count/{split}"]
+        # calculate reward margin
+        if f"{prefix}rewards/chosen" in logs and f"{prefix}rewards/rejected" in logs:
+            logs[f"{prefix}rewards/margins"] = logs[f"{prefix}rewards/chosen"] - logs[f"{prefix}rewards/rejected"]
+        # Add averaged stored metrics to logs
+        for key, metrics in self._stored_metrics[train_eval].items():
+            logs[f"{prefix}{key}"] = torch.Tensor(metrics).mean().item()
+        del self._stored_metrics[train_eval]
+
+        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
+            return super().log(logs, start_time)
+        else:  # transformers<=4.46
+            return super().log(logs)
+
+    def create_model_card(
+        self,
+        model_name: Optional[str] = None,
+        dataset_name: Optional[str] = None,
+        tags: Union[str, list[str], None] = None,
+    ):
+        """
+        Creates a draft of a model card using the information available to the `Trainer`.
+
+        Args:
+            model_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the model.
+            dataset_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the dataset used for training.
+            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
+                Tags to be associated with the model card.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
+            base_model = self.model.config._name_or_path
+        else:
+            base_model = None
+
+        tags = tags or []
+        if isinstance(tags, str):
+            tags = [tags]
+
+        if hasattr(self.model.config, "unsloth_version"):
+            tags.append("unsloth")
+
+        citation = textwrap.dedent("""\
+        @article{ethayarajh2024kto,
+            title        = {{KTO: Model Alignment as Prospect Theoretic Optimization}},
+            author       = {Kawin Ethayarajh and Winnie Xu and Niklas Muennighoff and Dan Jurafsky and Douwe Kiela},
+            year         = 2024,
+            eprint       = {arXiv:2402.01306},
+        }""")
+
+        model_card = generate_model_card(
+            base_model=base_model,
+            model_name=model_name,
+            hub_model_id=self.hub_model_id,
+            dataset_name=dataset_name,
+            tags=tags,
+            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
+            comet_url=get_comet_experiment_url(),
+            trainer_name="KTO",
+            trainer_citation=citation,
+            paper_title="KTO: Model Alignment as Prospect Theoretic Optimization",
+            paper_id="2402.01306",
+        )
+
+        model_card.save(os.path.join(self.args.output_dir, "README.md"))
+class UnslothKTOTrainer(_UnslothKTOTrainer):
+    """
+    
+    Initialize KTOTrainer.
+
+    Args:
+        model (`transformers.PreTrainedModel`):
+            The model to train, preferably an `AutoModelForSequenceClassification`.
+        ref_model (`PreTrainedModelWrapper`):
+            Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no
+            reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized.
+        args (`KTOConfig`):
+            The arguments to use for training.
+        train_dataset (`datasets.Dataset`):
+            The dataset to use for training.
+        eval_dataset (`datasets.Dataset`):
+            The dataset to use for evaluation.
+        processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
+            Processing class used to process the data. If provided, will be used to automatically process the inputs
+            for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
+            reuse the fine-tuned model.
+        data_collator (`transformers.DataCollator`, *optional*, defaults to `None`):
+            The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used
+            which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
+        model_init (`Callable[[], transformers.PreTrainedModel]`):
+            The model initializer to use for training. If None is specified, the default model initializer will be used.
+        callbacks (`list[transformers.TrainerCallback]`):
+            The callbacks to use for training.
+        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
+            The optimizer and scheduler to use for training.
+        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
+            The function to use to preprocess the logits before computing the metrics.
+        peft_config (`dict`, defaults to `None`):
+            The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.
+        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
+            The function to use to compute the metrics. Must take a `EvalPrediction` and return
+            a dictionary string to metric values.
+        model_adapter_name (`str`, defaults to `None`):
+            Name of the train target PEFT adapter, when using LoRA with multiple adapters.
+        ref_adapter_name (`str`, defaults to `None`):
+            Name of the reference PEFT adapter, when using LoRA with multiple adapters.
+    
+    """
+    def __init__(
+        self,
+        model = None,
+        ref_model = None,
+        args = None,
+        train_dataset = None,
+        eval_dataset = None,
+        processing_class = None,
+        data_collator = None,
+        model_init = None,
+        callbacks = None,
+        preprocess_logits_for_metrics = None,
+        peft_config = None,
+        compute_metrics = None,
+        model_adapter_name = None,
+        ref_adapter_name = None,
+        **kwargs
+    ):
+        if args is None: args = UnslothKTOConfig()
+        use_bf16 = getattr(args, 'bf16', False)
+        use_fp16 = getattr(args, 'fp16', False)
+        force_float32 = False
+        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':
+            print('Unsloth: Switching to float32 training since model cannot work with float16')
+            force_float32 = True
+        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
+        dtype = getattr(model.config, 'torch_dtype', None)
+        if dtype is None: dtype = model.get_input_embeddings().dtype
+        from unsloth_zoo.utils import _get_dtype
+        dtype = _get_dtype(dtype)
+        float16 = dtype == torch.float16
+        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
+        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
+        if force_float32:
+            args.fp16 = False
+            args.bf16 = False
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
+        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
+            args.fp16 = float16
+            args.bf16 = not float16
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
+        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
+            args.eval_strategy = 'steps'
+            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
+        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
+        if ga_steps is not None and ga_steps > 1:
+            from transformers import __version__ as transformers_version
+            if Version(transformers_version) <= Version('4.45.2'):
+                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
+                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
+        if getattr(args, 'eval_strategy', 'no') != 'no':
+            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
+            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
+            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
+        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
+        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
+        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
+        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
+        if force_float32:
+            args.bf16_full_eval = False
+            args.fp16_full_eval = False
+        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
+            args.bf16_full_eval = True
+            args.fp16_full_eval = False
+        elif not bf16_full_eval and not fp16_full_eval:
+            args.bf16_full_eval = args.bf16
+            args.fp16_full_eval = args.fp16
+        _output_logits = False
+        if locals().get('compute_metrics', None) is not None: _output_logits = True
+        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
+        if _output_logits:
+            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
+        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
+            pass
+        else:
+            model_max_seq_length = getattr(model, 'max_seq_length', None)
+            args_max_seq_length  = getattr(args,  'max_seq_length', None)
+            if args_max_seq_length is None and model_max_seq_length is not None:
+                max_seq_length = model.max_seq_length
+                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
+        if model is not None and hasattr(model, 'for_training'):
+            model.for_training()
+        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
+        if 'processing_class' in locals():
+            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
+            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
+        __tokenizer = processing_class if 'processing_class' in locals() else tokenizer
+        from unsloth_zoo.vision_utils import UnslothVisionDataCollator
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
+                data_collator = DataCollatorForLanguageModeling(__tokenizer, mlm = False)
+            elif isinstance(data_collator, DataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
+                data_collator = DataCollatorForSeq2Seq(__tokenizer)
+        else:
+            if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
+            if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
+            if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
+                if isinstance(data_collator, DataCollatorForSeq2Seq):
+                    data_collator = DataCollatorForSeq2Seq(__tokenizer.tokenizer)
+                else:
+                    data_collator = DataCollatorForLanguageModeling(__tokenizer.tokenizer, mlm = False)
+        other_metrics = []
+        
+        from unsloth_zoo.logging_utils import PatchRLStatistics
+        PatchRLStatistics('kto_trainer', other_metrics)
+        
+        super().__init__(
+            model = model,
+            ref_model = ref_model,
+            args = args,
+            train_dataset = train_dataset,
+            eval_dataset = eval_dataset,
+            processing_class = processing_class,
+            data_collator = data_collator,
+            model_init = model_init,
+            callbacks = callbacks,
+            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
+            peft_config = peft_config,
+            compute_metrics = compute_metrics,
+            model_adapter_name = model_adapter_name,
+            ref_adapter_name = ref_adapter_name,**kwargs)
+        if hasattr(self, 'neftune_hook_handle'):
+            self.neftune_hook_handle.remove()
+            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
+        if getattr(args, 'neftune_noise_alpha', None) is not None:
+            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
+        pass
+        
+pass

unsloth_compiled_cache/UnslothNashMDTrainer.py

@@ -0,0 +1,949 @@
+"""
+2025.4.1
+2025.4.1
+4.51.3
+0.15.2
+__UNSLOTH_VERSIONING__
+"""
+from torch import Tensor
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from trl.trainer.nash_md_trainer import (Any, BaseImageProcessor, BasePairwiseJudge, Callable, Dataset, EvalPrediction, F, FeatureExtractionMixin, GeometricMixtureWrapper, IterableDataset, NashMDConfig, NashMDTrainer, OnlineDPOTrainer, OptimizerNames, Optional, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SIMPLE_CHAT_TEMPLATE, TrainerCallback, Union, empty_cache, generate_model_card, get_comet_experiment_url, get_reward, is_conversational, is_wandb_available, jinja2, maybe_apply_chat_template, nn, os, textwrap, torch, truncate_right, unwrap_model_for_generation)
+
+
+import os
+from typing import *
+from dataclasses import dataclass, field
+from packaging.version import Version
+import torch
+import numpy as np
+from contextlib import nullcontext
+from torch.nn import functional as F
+from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
+
+torch_compile_options = {
+    "epilogue_fusion"   : True,
+    "max_autotune"      : False,
+    "shape_padding"     : True,
+    "trace.enabled"     : False,
+    "triton.cudagraphs" : False,
+}
+
+@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
+def selective_log_softmax(logits, index):
+    logits = logits.to(torch.float32)
+    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
+    # loop to reduce peak mem consumption
+    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
+    logsumexp_values = torch.logsumexp(logits, dim = -1)
+    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
+    return per_token_logps
+@dataclass
+class UnslothNashMDConfig(NashMDConfig):
+    """
+    
+    Configuration class for the [`NashMDTrainer`].
+
+    Subclass of [`OnlineDPOConfig`] we can use all its arguments and add the following:
+
+    Parameters:
+        mixture_coef (`float` or `list[float]`, *optional*, defaults to `0.5`):
+            Logit mixture coefficient for the model and reference model. If a list of floats is provided then the
+            mixture coefficient is selected for each new epoch and the last coefficient is used for the rest of the
+            epochs.
+    
+    """
+    vllm_sampling_params: Optional[Any] = field(
+        default = None,
+        metadata = {'help': 'vLLM SamplingParams'},
+    )
+    unsloth_num_chunks : Optional[int] = field(
+        default = -1,
+        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
+    )
+    def __init__(
+        self,
+        output_dir = None,
+        overwrite_output_dir = None,
+        do_train = False,
+        do_eval = False,
+        do_predict = False,
+        eval_strategy = 'no',
+        prediction_loss_only = False,
+        per_device_train_batch_size = 4,
+        per_device_eval_batch_size = 4,
+        per_gpu_train_batch_size = None,
+        per_gpu_eval_batch_size = None,
+        gradient_accumulation_steps = 2,
+        eval_accumulation_steps = 2,
+        eval_delay = 0,
+        torch_empty_cache_steps = 250,
+        learning_rate = 5e-05,
+        weight_decay = 0.01,
+        adam_beta1 = 0.9,
+        adam_beta2 = 0.999,
+        adam_epsilon = 1e-08,
+        max_grad_norm = 1.0,
+        num_train_epochs = 3.0,
+        max_steps = -1,
+        lr_scheduler_type = 'linear',
+        warmup_ratio = 0.1,
+        warmup_steps = 0,
+        log_level = 'passive',
+        log_level_replica = 'warning',
+        log_on_each_node = True,
+        logging_dir = None,
+        logging_strategy = 'steps',
+        logging_first_step = False,
+        logging_steps = 1,
+        logging_nan_inf_filter = False,
+        save_strategy = 'steps',
+        save_steps = 500,
+        save_total_limit = None,
+        save_safetensors = True,
+        save_on_each_node = False,
+        save_only_model = False,
+        restore_callback_states_from_checkpoint = False,
+        no_cuda = False,
+        use_cpu = False,
+        use_mps_device = False,
+        seed = 3407,
+        data_seed = 3407,
+        jit_mode_eval = False,
+        use_ipex = False,
+        bf16 = False,
+        fp16 = False,
+        fp16_opt_level = 'O1',
+        half_precision_backend = 'auto',
+        bf16_full_eval = False,
+        fp16_full_eval = False,
+        tf32 = None,
+        local_rank = -1,
+        ddp_backend = None,
+        tpu_num_cores = None,
+        tpu_metrics_debug = False,
+        debug = '',
+        dataloader_drop_last = False,
+        eval_steps = None,
+        dataloader_num_workers = 0,
+        dataloader_prefetch_factor = None,
+        past_index = -1,
+        run_name = None,
+        disable_tqdm = None,
+        remove_unused_columns = True,
+        label_names = None,
+        load_best_model_at_end = False,
+        metric_for_best_model = None,
+        greater_is_better = None,
+        ignore_data_skip = False,
+        fsdp = '',
+        fsdp_min_num_params = 0,
+        fsdp_config = None,
+        tp_size = 0,
+        fsdp_transformer_layer_cls_to_wrap = None,
+        accelerator_config = None,
+        deepspeed = None,
+        label_smoothing_factor = 0.0,
+        optim = 'adamw_8bit',
+        optim_args = None,
+        adafactor = False,
+        group_by_length = False,
+        length_column_name = 'length',
+        report_to = None,
+        ddp_find_unused_parameters = None,
+        ddp_bucket_cap_mb = None,
+        ddp_broadcast_buffers = None,
+        dataloader_pin_memory = True,
+        dataloader_persistent_workers = False,
+        skip_memory_metrics = True,
+        use_legacy_prediction_loop = False,
+        push_to_hub = False,
+        resume_from_checkpoint = None,
+        hub_model_id = None,
+        hub_strategy = 'every_save',
+        hub_token = None,
+        hub_private_repo = None,
+        hub_always_push = False,
+        gradient_checkpointing = False,
+        gradient_checkpointing_kwargs = None,
+        include_inputs_for_metrics = False,
+        eval_do_concat_batches = True,
+        fp16_backend = 'auto',
+        push_to_hub_model_id = None,
+        push_to_hub_organization = None,
+        push_to_hub_token = None,
+        mp_parameters = '',
+        auto_find_batch_size = False,
+        full_determinism = False,
+        torchdynamo = None,
+        ray_scope = 'last',
+        ddp_timeout = 1800,
+        torch_compile = False,
+        torch_compile_backend = None,
+        torch_compile_mode = None,
+        include_tokens_per_second = False,
+        include_num_input_tokens_seen = False,
+        neftune_noise_alpha = None,
+        optim_target_modules = None,
+        batch_eval_metrics = False,
+        eval_on_start = False,
+        use_liger_kernel = False,
+        eval_use_gather_object = False,
+        average_tokens_across_devices = False,
+        reward_model_path = None,
+        judge = None,
+        max_new_tokens = 64,
+        max_length = 512,
+        temperature = 0.9,
+        missing_eos_penalty = None,
+        loss_type = 'sigmoid',
+        dataset_num_proc = None,
+        disable_dropout = True,
+        use_vllm = False,
+        ds3_gather_for_generation = True,
+        vllm_sampling_params = None,
+        unsloth_num_chunks = -1,
+        **kwargs,
+    ):
+        if learning_rate < 1e-7: raise FloatingPointError(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
+        if learning_rate > 1: raise OverflowError(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
+        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
+            output_dir = 'unsloth_training_checkpoints'
+            save_strategy = 'no'
+        if dataset_num_proc is None:
+            from multiprocessing import cpu_count
+            dataset_num_proc = cpu_count()
+        
+        super().__init__(
+            output_dir = output_dir,
+            overwrite_output_dir = overwrite_output_dir,
+            do_train = do_train,
+            do_eval = do_eval,
+            do_predict = do_predict,
+            eval_strategy = eval_strategy,
+            prediction_loss_only = prediction_loss_only,
+            per_device_train_batch_size = per_device_train_batch_size,
+            per_device_eval_batch_size = per_device_eval_batch_size,
+            per_gpu_train_batch_size = per_gpu_train_batch_size,
+            per_gpu_eval_batch_size = per_gpu_eval_batch_size,
+            gradient_accumulation_steps = gradient_accumulation_steps,
+            eval_accumulation_steps = eval_accumulation_steps,
+            eval_delay = eval_delay,
+            torch_empty_cache_steps = torch_empty_cache_steps,
+            learning_rate = learning_rate,
+            weight_decay = weight_decay,
+            adam_beta1 = adam_beta1,
+            adam_beta2 = adam_beta2,
+            adam_epsilon = adam_epsilon,
+            max_grad_norm = max_grad_norm,
+            num_train_epochs = num_train_epochs,
+            max_steps = max_steps,
+            lr_scheduler_type = lr_scheduler_type,
+            warmup_ratio = warmup_ratio,
+            warmup_steps = warmup_steps,
+            log_level = log_level,
+            log_level_replica = log_level_replica,
+            log_on_each_node = log_on_each_node,
+            logging_dir = logging_dir,
+            logging_strategy = logging_strategy,
+            logging_first_step = logging_first_step,
+            logging_steps = logging_steps,
+            logging_nan_inf_filter = logging_nan_inf_filter,
+            save_strategy = save_strategy,
+            save_steps = save_steps,
+            save_total_limit = save_total_limit,
+            save_safetensors = save_safetensors,
+            save_on_each_node = save_on_each_node,
+            save_only_model = save_only_model,
+            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
+            no_cuda = no_cuda,
+            use_cpu = use_cpu,
+            use_mps_device = use_mps_device,
+            seed = seed,
+            data_seed = data_seed,
+            jit_mode_eval = jit_mode_eval,
+            use_ipex = use_ipex,
+            bf16 = bf16,
+            fp16 = fp16,
+            fp16_opt_level = fp16_opt_level,
+            half_precision_backend = half_precision_backend,
+            bf16_full_eval = bf16_full_eval,
+            fp16_full_eval = fp16_full_eval,
+            tf32 = tf32,
+            local_rank = local_rank,
+            ddp_backend = ddp_backend,
+            tpu_num_cores = tpu_num_cores,
+            tpu_metrics_debug = tpu_metrics_debug,
+            debug = debug,
+            dataloader_drop_last = dataloader_drop_last,
+            eval_steps = eval_steps,
+            dataloader_num_workers = dataloader_num_workers,
+            dataloader_prefetch_factor = dataloader_prefetch_factor,
+            past_index = past_index,
+            run_name = run_name,
+            disable_tqdm = disable_tqdm,
+            remove_unused_columns = remove_unused_columns,
+            label_names = label_names,
+            load_best_model_at_end = load_best_model_at_end,
+            metric_for_best_model = metric_for_best_model,
+            greater_is_better = greater_is_better,
+            ignore_data_skip = ignore_data_skip,
+            fsdp = fsdp,
+            fsdp_min_num_params = fsdp_min_num_params,
+            fsdp_config = fsdp_config,
+            tp_size = tp_size,
+            fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
+            accelerator_config = accelerator_config,
+            deepspeed = deepspeed,
+            label_smoothing_factor = label_smoothing_factor,
+            optim = optim,
+            optim_args = optim_args,
+            adafactor = adafactor,
+            group_by_length = group_by_length,
+            length_column_name = length_column_name,
+            report_to = report_to,
+            ddp_find_unused_parameters = ddp_find_unused_parameters,
+            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
+            ddp_broadcast_buffers = ddp_broadcast_buffers,
+            dataloader_pin_memory = dataloader_pin_memory,
+            dataloader_persistent_workers = dataloader_persistent_workers,
+            skip_memory_metrics = skip_memory_metrics,
+            use_legacy_prediction_loop = use_legacy_prediction_loop,
+            push_to_hub = push_to_hub,
+            resume_from_checkpoint = resume_from_checkpoint,
+            hub_model_id = hub_model_id,
+            hub_strategy = hub_strategy,
+            hub_token = hub_token,
+            hub_private_repo = hub_private_repo,
+            hub_always_push = hub_always_push,
+            gradient_checkpointing = gradient_checkpointing,
+            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
+            include_inputs_for_metrics = include_inputs_for_metrics,
+            eval_do_concat_batches = eval_do_concat_batches,
+            fp16_backend = fp16_backend,
+            push_to_hub_model_id = push_to_hub_model_id,
+            push_to_hub_organization = push_to_hub_organization,
+            push_to_hub_token = push_to_hub_token,
+            mp_parameters = mp_parameters,
+            auto_find_batch_size = auto_find_batch_size,
+            full_determinism = full_determinism,
+            torchdynamo = torchdynamo,
+            ray_scope = ray_scope,
+            ddp_timeout = ddp_timeout,
+            torch_compile = torch_compile,
+            torch_compile_backend = torch_compile_backend,
+            torch_compile_mode = torch_compile_mode,
+            include_tokens_per_second = include_tokens_per_second,
+            include_num_input_tokens_seen = include_num_input_tokens_seen,
+            neftune_noise_alpha = neftune_noise_alpha,
+            optim_target_modules = optim_target_modules,
+            batch_eval_metrics = batch_eval_metrics,
+            eval_on_start = eval_on_start,
+            use_liger_kernel = use_liger_kernel,
+            eval_use_gather_object = eval_use_gather_object,
+            average_tokens_across_devices = average_tokens_across_devices,
+            reward_model_path = reward_model_path,
+            judge = judge,
+            max_new_tokens = max_new_tokens,
+            max_length = max_length,
+            temperature = temperature,
+            missing_eos_penalty = missing_eos_penalty,
+            loss_type = loss_type,
+            dataset_num_proc = dataset_num_proc,
+            disable_dropout = disable_dropout,
+            use_vllm = use_vllm,
+            ds3_gather_for_generation = ds3_gather_for_generation,**kwargs)
+        self.vllm_sampling_params = vllm_sampling_params
+        self.unsloth_num_chunks = unsloth_num_chunks
+pass
+
+class _UnslothNashMDTrainer(OnlineDPOTrainer):
+    r""""""
+
+    _tag_names = ["trl", "nash-md"]
+
+    def __init__(
+        self,
+        model: Union[PreTrainedModel, nn.Module] = None,
+        ref_model: Union[PreTrainedModel, nn.Module] = None,
+        reward_model: Union[PreTrainedModel, nn.Module, None] = None,
+        judge: Optional[BasePairwiseJudge] = None,
+        args: Optional[NashMDConfig] = None,
+        data_collator: Optional[Callable] = None,
+        train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
+        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
+        processing_class: Optional[
+            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
+        ] = None,
+        peft_config: Optional[dict] = None,
+        compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
+        callbacks: Optional[list[TrainerCallback]] = None,
+        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
+        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+    ) -> None:
+        super().__init__(
+            model=model,
+            ref_model=ref_model,
+            reward_model=reward_model,
+            judge=judge,
+            args=args,
+            data_collator=data_collator,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            processing_class=processing_class,
+            reward_processing_class=processing_class,  # for now, NashMDTrainer can't use any reward model
+            peft_config=peft_config,
+            compute_metrics=compute_metrics,
+            callbacks=callbacks,
+            optimizers=optimizers,
+            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+        )
+
+        self._mixture_coef = self.args.mixture_coef
+
+        # Overwrite the stats dictionary to include NashMD specific statistics
+        self.stats = {
+            # Remove "non_score_reward", "rlhf_reward", "scores_margin"
+            # Add "mixture_coef"
+            "loss/kl": [],
+            "objective/entropy": [],
+            "loss/score": [],
+            "rewards/probabilities": [],
+            "rewards/accuracies": [],
+            "rewards/margins": [],
+            "logps/chosen": [],
+            "logps/rejected": [],
+            "val/model_contain_eos_token": [],
+            "val/ref_contain_eos_token": [],
+            "beta": [],
+            "mixture_coef": [],
+        }
+        if self.reward_model is not None:
+            self.stats["rewards/chosen"] = []
+            self.stats["rewards/rejected"] = []
+
+    @property
+    def mixture_coef(self):
+        if isinstance(self._mixture_coef, list):
+            epoch = self.state.epoch
+            return self._mixture_coef[epoch] if epoch < len(self._mixture_coef) else self._mixture_coef[-1]
+        else:
+            return self._mixture_coef
+
+    def _generate_completions(self, model, prompts):
+        with unwrap_model_for_generation(model, self.accelerator) as unwrapped_model:
+            model_output = unwrapped_model.generate(
+                input_ids=prompts["input_ids"],
+                attention_mask=prompts["attention_mask"],
+                generation_config=self.generation_config,
+            )
+
+            ref_model = model if self.ref_model is None else self.ref_model
+            with torch.no_grad(), unwrap_model_for_generation(ref_model, self.accelerator) as unwrapped_ref_model:
+                mixture_model = GeometricMixtureWrapper(
+                    model=unwrapped_model,
+                    ref_model=unwrapped_ref_model,
+                    generation_config=self.generation_config,
+                    mixture_coef=self.mixture_coef,
+                    device=self.accelerator.device,
+                )
+
+                mixture_output = mixture_model.generate(
+                    input_ids=prompts["input_ids"],
+                    attention_mask=prompts["attention_mask"],
+                    generation_config=self.generation_config,
+                )
+
+        return model_output, mixture_output
+
+    def _process_completions(self, model_output, mixture_output, prompts):
+        context_length = prompts["input_ids"].shape[1]
+
+        # Process model completions
+        model_completion_ids = model_output[:, context_length:]
+        model_completion_ids, model_completion_mask = truncate_right(
+            model_completion_ids, self.processing_class.eos_token_id, self.processing_class.pad_token_id
+        )
+        model_data = {
+            "input_ids": torch.cat((prompts["input_ids"], model_completion_ids), dim=1),
+            "attention_mask": torch.cat((prompts["attention_mask"], model_completion_mask), dim=1),
+            "raw": prompts["raw"],
+        }
+
+        # Process reference model completions
+        mixture_completion_ids = mixture_output[:, context_length:]
+        mixture_completion_ids, mixture_completion_mask = truncate_right(
+            mixture_completion_ids, self.processing_class.eos_token_id, self.processing_class.pad_token_id
+        )
+        mixture_data = {
+            "input_ids": torch.cat((prompts["input_ids"], mixture_completion_ids), dim=1),
+            "attention_mask": torch.cat((prompts["attention_mask"], mixture_completion_mask), dim=1),
+            "raw": prompts["raw"],
+        }
+
+        return model_data, mixture_data
+
+    def _compute_rewards(self, model_data, mixture_data, context_length):
+        with torch.no_grad():
+            _, model_scores, _ = get_reward(
+                self.reward_model, model_data["input_ids"], self.processing_class.pad_token_id, context_length
+            )
+            _, mixture_scores, _ = get_reward(
+                self.reward_model, mixture_data["input_ids"], self.processing_class.pad_token_id, context_length
+            )
+
+        # Apply EOS penalty if needed
+        if self.args.missing_eos_penalty is not None:
+            model_contain_eos = torch.any(model_data["input_ids"] == self.processing_class.eos_token_id, dim=-1)
+            mixture_contain_eos = torch.any(mixture_data["input_ids"] == self.processing_class.eos_token_id, dim=-1)
+            model_scores[~model_contain_eos] -= self.args.missing_eos_penalty
+            mixture_scores[~mixture_contain_eos] -= self.args.missing_eos_penalty
+
+        return model_scores, mixture_scores
+
+    def _compute_judge(self, model_data, mixture_data, context_length):
+        prompts = model_data["raw"]
+        model_data_completions = self.processing_class.batch_decode(
+            model_data["input_ids"][:, context_length:], skip_special_tokens=True
+        )
+        model_data_completions = [completion.strip() for completion in model_data_completions]
+
+        mixture_data_completions = self.processing_class.batch_decode(
+            mixture_data["input_ids"][:, context_length:], skip_special_tokens=True
+        )
+        mixture_data_completions = [completion.strip() for completion in mixture_data_completions]
+        if is_conversational({"prompt": prompts[0]}):
+            model_data_completions = [
+                [{"role": "assistant", "content": completion}] for completion in model_data_completions
+            ]
+            environment = jinja2.Environment()
+            template = environment.from_string(SIMPLE_CHAT_TEMPLATE)
+            prompts = [template.render(messages=message) for message in prompts]
+            model_data_completions = [template.render(messages=completion) for completion in model_data_completions]
+
+            mixture_data_completions = [
+                [{"role": "assistant", "content": completion}] for completion in mixture_data_completions
+            ]
+            mixture_data_completions = [
+                template.render(messages=completion) for completion in mixture_data_completions
+            ]
+
+        probability = self.judge.judge(
+            prompts,
+            list(zip(model_data_completions, mixture_data_completions)),
+            return_scores=True,
+        )
+        return torch.tensor(probability, device=model_data["input_ids"].device)
+
+    def _compute_logprobs(self, model, model_data, context_length):
+        def compute_logprobs_for_data(m, data):
+            output = m(data["input_ids"], attention_mask=data["attention_mask"])
+            logits = output.logits[:, context_length - 1 : -1]
+            token_logprobs = selective_log_softmax(logits, data["input_ids"][:, context_length:])
+            return token_logprobs
+
+        # Compute logprobs for model completions under the model
+        model_logprobs_model_data = compute_logprobs_for_data(model, model_data)
+
+        # Compute logprobs of model completions under the reference model
+        with torch.no_grad():
+            if self.ref_model is None:
+                with model.disable_adapter():
+                    ref_logprobs_model_data = compute_logprobs_for_data(model, model_data)
+            else:
+                ref_logprobs_model_data = compute_logprobs_for_data(self.ref_model, model_data)
+
+        # Mask padding tokens
+        model_padding_mask = model_data["attention_mask"][:, context_length:] == 0
+        model_logprobs_model_data = model_logprobs_model_data.masked_fill(model_padding_mask, 0.0)
+        ref_logprobs_model_data = ref_logprobs_model_data.masked_fill(model_padding_mask, 0.0)
+
+        return (model_logprobs_model_data, ref_logprobs_model_data)
+
+    def _compute_losses(
+        self,
+        model_logprobs_model_data,
+        ref_logprobs_model_data,
+        probability,
+    ):
+        # reinforce score where 0.5 is a control variate
+        score = (probability - 0.5) * model_logprobs_model_data.sum(1)
+
+        # kl divergence via reinforce
+        with torch.no_grad():
+            log_ratio = model_logprobs_model_data - ref_logprobs_model_data
+            kl_div_log = log_ratio.sum(1)
+        kl_div_loss = (log_ratio * model_logprobs_model_data).sum(1)
+
+        # final loss
+        loss = self.beta * kl_div_loss - score
+
+        return loss.mean(), score, kl_div_log
+
+    def _log_statistics(
+        self,
+        model_data,
+        mixture_data,
+        model_logprobs_model_data,
+        ref_logprobs_model_data,
+        probability,
+        score,
+        kl_div,
+        context_length,
+        model_scores=None,
+        mixture_scores=None,
+    ):
+        # Helper function to gather and compute mean
+        def gather_mean(tensor):
+            return self.accelerator.gather_for_metrics(tensor).mean().item()
+
+        # Log score
+        self.stats["loss/score"].append(gather_mean(score))
+        # Log KL divergence
+        self.stats["loss/kl"].append(gather_mean(kl_div))
+
+        # Log logprobs
+        model_logprobs_model_data_sum = model_logprobs_model_data.sum(1)
+        ref_logprobs_model_data_sum = ref_logprobs_model_data.sum(1)
+
+        self.stats["logps/chosen"].append(gather_mean(model_logprobs_model_data_sum))
+        self.stats["logps/rejected"].append(gather_mean(ref_logprobs_model_data_sum))
+
+        # Log rewards
+        if self.reward_model is not None:
+            self.stats["rewards/chosen"].append(gather_mean(model_scores))
+            self.stats["rewards/rejected"].append(gather_mean(mixture_scores))
+
+        # Log probabilities
+        self.stats["rewards/probabilities"].append(gather_mean(probability))
+
+        # Calculate entropy for model data
+        entropy_model_data = -model_logprobs_model_data.sum(1)
+        self.stats["objective/entropy"].append(gather_mean(entropy_model_data))
+
+        # Calculate margins
+        margin = model_logprobs_model_data_sum - ref_logprobs_model_data_sum
+        self.stats["rewards/margins"].append(gather_mean(margin))
+
+        # Calculate accuracy
+        accuracy = (margin > 0).float()
+        self.stats["rewards/accuracies"].append(gather_mean(accuracy))
+
+        # Log EOS token statistics
+        model_eos = (model_data["input_ids"][:, context_length:] == self.processing_class.eos_token_id).any(dim=1)
+        mixture_eos = (mixture_data["input_ids"][:, context_length:] == self.processing_class.eos_token_id).any(dim=1)
+        self.stats["val/model_contain_eos_token"].append(gather_mean(model_eos.float()))
+        self.stats["val/ref_contain_eos_token"].append(gather_mean(mixture_eos.float()))
+
+        # Log beta and mixture coef
+        self.stats["beta"].append(self.beta)
+        self.stats["mixture_coef"].append(self.mixture_coef)
+
+    def training_step(
+        self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None
+    ) -> torch.Tensor:
+        model.train()
+
+        # Apply chat template and tokenize the input
+        batch_size = len(next(iter(inputs.values())))
+        prompts = inputs["prompt"]
+        inputs = [{k: v[i] for k, v in inputs.items()} for i in range(batch_size)]
+        inputs = [maybe_apply_chat_template(x, self.processing_class) for x in inputs]
+        inputs = [self.tokenize_row(x, self.model.config.is_encoder_decoder, self.processing_class) for x in inputs]
+        inputs = self.data_collator(inputs)
+
+        # need the prompt_ only
+        inputs = self._prepare_inputs(inputs)
+        context_length = inputs["prompt_input_ids"].shape[1]
+        prompts = {
+            "input_ids": inputs["prompt_input_ids"],
+            "attention_mask": inputs["prompt_attention_mask"],
+            "raw": prompts,
+        }
+        del inputs
+
+        # Sample completions from both the model and the reference model
+        model_output, mixture_output = self._generate_completions(model, prompts)
+
+        # Process model completions
+        model_data, mixture_data = self._process_completions(model_output, mixture_output, prompts)
+
+        # Compute rewards
+        if self.reward_model is not None:
+            model_scores, mixture_scores = self._compute_rewards(model_data, mixture_data, context_length)
+            # probability of the model data vs the mixture data
+            probability = F.sigmoid(model_scores - mixture_scores)
+        else:
+            model_scores, mixture_scores = None, None
+            probability = self._compute_judge(model_data, mixture_data, context_length)
+
+        # Compute logprobs
+        model_logprobs_model_data, ref_logprobs_model_data = self._compute_logprobs(model, model_data, context_length)
+
+        # Compute loss
+        loss, score, kl_div = self._compute_losses(model_logprobs_model_data, ref_logprobs_model_data, probability)
+
+        # Log everything
+        self._log_statistics(
+            model_data,
+            mixture_data,
+            model_logprobs_model_data.detach(),
+            ref_logprobs_model_data,
+            probability,
+            score.detach(),
+            kl_div.detach(),
+            context_length,
+            model_scores,
+            mixture_scores,
+        )
+
+        if (
+            self.args.torch_empty_cache_steps is not None
+            and self.state.global_step % self.args.torch_empty_cache_steps == 0
+        ):
+            empty_cache()
+
+        kwargs = {}
+        # For LOMO optimizers you need to explicitly use the learning rate
+        if self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]:
+            kwargs["learning_rate"] = self._get_learning_rate()
+
+        if self.args.n_gpu > 1:
+            loss = loss.mean()  # mean() to average on multi-gpu parallel training
+
+        if self.use_apex:
+            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
+                scaled_loss.backward()
+        else:
+            self.accelerator.backward(loss, **kwargs)
+
+        return loss.detach() / self.args.gradient_accumulation_steps
+
+    def create_model_card(
+        self,
+        model_name: Optional[str] = None,
+        dataset_name: Optional[str] = None,
+        tags: Union[str, list[str], None] = None,
+    ):
+        """
+        Creates a draft of a model card using the information available to the `Trainer`.
+
+        Args:
+            model_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the model.
+            dataset_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the dataset used for training.
+            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
+                Tags to be associated with the model card.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
+            base_model = self.model.config._name_or_path
+        else:
+            base_model = None
+
+        tags = tags or []
+        if isinstance(tags, str):
+            tags = [tags]
+
+        if hasattr(self.model.config, "unsloth_version"):
+            tags.append("unsloth")
+
+        citation = textwrap.dedent("""\
+        @inproceedings{munos2024nash,
+            title        = {{Nash Learning from Human Feedback}},
+            author       = {R{\'{e}}mi Munos and Michal Valko and Daniele Calandriello and Mohammad Gheshlaghi Azar and Mark Rowland and Zhaohan Daniel Guo and Yunhao Tang and Matthieu Geist and Thomas Mesnard and C{\\^{o}}me Fiegel and Andrea Michi and Marco Selvi and Sertan Girgin and Nikola Momchev and Olivier Bachem and Daniel J. Mankowitz and Doina Precup and Bilal Piot},
+            year         = 2024,
+            booktitle    = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
+            publisher    = {OpenReview.net},
+            url          = {https://openreview.net/forum?id=Y5AmNYiyCQ}
+        }""")
+
+        model_card = generate_model_card(
+            base_model=base_model,
+            model_name=model_name,
+            hub_model_id=self.hub_model_id,
+            dataset_name=dataset_name,
+            tags=tags,
+            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
+            comet_url=get_comet_experiment_url(),
+            trainer_name="Nash-MD",
+            trainer_citation=citation,
+            paper_title="Nash Learning from Human Feedback",
+            paper_id="2312.00886",
+        )
+
+        model_card.save(os.path.join(self.args.output_dir, "README.md"))
+class UnslothNashMDTrainer(_UnslothNashMDTrainer):
+    """
+    
+    Initialize NashMDTrainer as a subclass of [`OnlineDPOConfig`].
+
+    Args:
+        model (`transformers.PreTrainedModel`):
+            The model to train, preferably an `AutoModelForCausalLM`.
+        ref_model (`PreTrainedModelWrapper`):
+            Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no
+            reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized.
+        reward_model (`transformers.PreTrainedModel`):
+            The reward model to score completions with, preferably an `AutoModelForSequenceClassification`.
+        judge (`BasePairwiseJudge`):
+            The judge to use for pairwise comparison of model completions.
+        args (`NashMDConfig`):
+            The NashMD config arguments to use for training.
+        data_collator (`transformers.DataCollator`):
+            The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used
+            which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
+        train_dataset (`datasets.Dataset`):
+            The dataset to use for training.
+        eval_dataset (`datasets.Dataset`):
+            The dataset to use for evaluation.
+        processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
+            Processing class used to process the data. If provided, will be used to automatically process the inputs
+            for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
+            reuse the fine-tuned model.
+        peft_config (`dict`):
+            The peft config to use for training.
+        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
+            The function to use to compute the metrics. Must take a `EvalPrediction` and return
+            a dictionary string to metric values.
+        callbacks (`list[transformers.TrainerCallback]`):
+            The callbacks to use for training.
+        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
+            The optimizer and scheduler to use for training.
+        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
+            The function to use to preprocess the logits before computing the metrics.
+    
+    """
+    def __init__(
+        self,
+        model = None,
+        ref_model = None,
+        reward_model = None,
+        judge = None,
+        args = None,
+        data_collator = None,
+        train_dataset = None,
+        eval_dataset = None,
+        processing_class = None,
+        peft_config = None,
+        compute_metrics = None,
+        callbacks = None,
+        preprocess_logits_for_metrics = None,
+        **kwargs
+    ):
+        if args is None: args = UnslothNashMDConfig()
+        use_bf16 = getattr(args, 'bf16', False)
+        use_fp16 = getattr(args, 'fp16', False)
+        force_float32 = False
+        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':
+            print('Unsloth: Switching to float32 training since model cannot work with float16')
+            force_float32 = True
+        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
+        dtype = getattr(model.config, 'torch_dtype', None)
+        if dtype is None: dtype = model.get_input_embeddings().dtype
+        from unsloth_zoo.utils import _get_dtype
+        dtype = _get_dtype(dtype)
+        float16 = dtype == torch.float16
+        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
+        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
+        if force_float32:
+            args.fp16 = False
+            args.bf16 = False
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
+        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
+            args.fp16 = float16
+            args.bf16 = not float16
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
+        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
+            args.eval_strategy = 'steps'
+            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
+        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
+        if ga_steps is not None and ga_steps > 1:
+            from transformers import __version__ as transformers_version
+            if Version(transformers_version) <= Version('4.45.2'):
+                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
+                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
+        if getattr(args, 'eval_strategy', 'no') != 'no':
+            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
+            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
+            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
+        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
+        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
+        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
+        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
+        if force_float32:
+            args.bf16_full_eval = False
+            args.fp16_full_eval = False
+        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
+            args.bf16_full_eval = True
+            args.fp16_full_eval = False
+        elif not bf16_full_eval and not fp16_full_eval:
+            args.bf16_full_eval = args.bf16
+            args.fp16_full_eval = args.fp16
+        _output_logits = False
+        if locals().get('compute_metrics', None) is not None: _output_logits = True
+        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
+        if _output_logits:
+            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
+        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
+            pass
+        else:
+            model_max_seq_length = getattr(model, 'max_seq_length', None)
+            args_max_seq_length  = getattr(args,  'max_seq_length', None)
+            if args_max_seq_length is None and model_max_seq_length is not None:
+                max_seq_length = model.max_seq_length
+                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
+        if model is not None and hasattr(model, 'for_training'):
+            model.for_training()
+        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
+        if 'processing_class' in locals():
+            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
+            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
+        __tokenizer = processing_class if 'processing_class' in locals() else tokenizer
+        from unsloth_zoo.vision_utils import UnslothVisionDataCollator
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
+                data_collator = DataCollatorForLanguageModeling(__tokenizer, mlm = False)
+            elif isinstance(data_collator, DataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
+                data_collator = DataCollatorForSeq2Seq(__tokenizer)
+        else:
+            if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
+            if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
+            if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
+                if isinstance(data_collator, DataCollatorForSeq2Seq):
+                    data_collator = DataCollatorForSeq2Seq(__tokenizer.tokenizer)
+                else:
+                    data_collator = DataCollatorForLanguageModeling(__tokenizer.tokenizer, mlm = False)
+        other_metrics = []
+        
+        from unsloth_zoo.logging_utils import PatchRLStatistics
+        PatchRLStatistics('nash_md_trainer', other_metrics)
+        
+        super().__init__(
+            model = model,
+            ref_model = ref_model,
+            reward_model = reward_model,
+            judge = judge,
+            args = args,
+            data_collator = data_collator,
+            train_dataset = train_dataset,
+            eval_dataset = eval_dataset,
+            processing_class = processing_class,
+            peft_config = peft_config,
+            compute_metrics = compute_metrics,
+            callbacks = callbacks,
+            preprocess_logits_for_metrics = preprocess_logits_for_metrics,**kwargs)
+        if hasattr(self, 'neftune_hook_handle'):
+            self.neftune_hook_handle.remove()
+            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
+        if getattr(args, 'neftune_noise_alpha', None) is not None:
+            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
+        pass
+        
+pass

unsloth_compiled_cache/UnslothORPOTrainer.py

@@ -0,0 +1,1537 @@
+"""
+2025.4.1
+2025.4.1
+4.51.3
+0.15.2
+__UNSLOTH_VERSIONING__
+"""
+from torch import Tensor
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from trl.trainer.orpo_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, Literal, ORPOConfig, ORPOTrainer, Optional, PartialState, PeftModel, PreTrainedModel, PreTrainedModelWrapper, PreTrainedTokenizerBase, ProcessorMixin, Trainer, TrainerCallback, Union, add_bos_token_if_needed, add_eos_token_if_needed, amp, deepcopy, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, inspect, is_comet_available, is_peft_available, is_torch_fx_proxy, is_torch_xla_available, is_wandb_available, log_table_to_comet_experiment, maybe_apply_chat_template, maybe_extract_prompt, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_model_for_kbit_training, random, textwrap, torch, transformers, version, warnings)
+
+
+import os
+from typing import *
+from dataclasses import dataclass, field
+from packaging.version import Version
+import torch
+import numpy as np
+from contextlib import nullcontext
+from torch.nn import functional as F
+from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
+
+torch_compile_options = {
+    "epilogue_fusion"   : True,
+    "max_autotune"      : False,
+    "shape_padding"     : True,
+    "trace.enabled"     : False,
+    "triton.cudagraphs" : False,
+}
+
+@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
+def selective_log_softmax(logits, index):
+    logits = logits.to(torch.float32)
+    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
+    # loop to reduce peak mem consumption
+    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
+    logsumexp_values = torch.logsumexp(logits, dim = -1)
+    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
+    return per_token_logps
+@dataclass
+class UnslothORPOConfig(ORPOConfig):
+    """
+    
+    Configuration class for the [`ORPOTrainer`].
+
+    Using [`~transformers.HfArgumentParser`] we can turn this class into
+    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
+    command line.
+
+    Parameters:
+        learning_rate (`float`, *optional*, defaults to `1e-6`):
+            Initial learning rate for [`AdamW`] optimizer. The default value replaces that of
+            [`~transformers.TrainingArguments`].
+        max_length (`int` or `None`, *optional*, defaults to `1024`):
+            Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want
+            to use the default data collator.
+        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
+            Maximum length of the prompt. This argument is required if you want to use the default data collator.
+        max_completion_length (`int` or `None`, *optional*, defaults to `None`):
+            Maximum length of the completion. This argument is required if you want to use the default data collator
+            and your model is an encoder-decoder.
+        beta (`float`, *optional*, defaults to `0.1`):
+            Parameter controlling the relative ratio loss weight in the ORPO loss. In the [paper](https://huggingface.co/papers/2403.07691),
+            it is denoted by λ. In the [code](https://github.com/xfactlab/orpo), it is denoted by `alpha`.
+        disable_dropout (`bool`, *optional*, defaults to `True`):
+            Whether to disable dropout in the model.
+        label_pad_token_id (`int`, *optional*, defaults to `-100`):
+            Label pad token id. This argument is required if you want to use the default data collator.
+        padding_value (`int` or `None`, *optional*, defaults to `None`):
+            Padding value to use. If `None`, the padding value of the tokenizer is used.
+        truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
+            Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`.
+            This argument is required if you want to use the default data collator.
+        generate_during_eval (`bool`, *optional*, defaults to `False`):
+            If `True`, generates and logs completions from the model to W&B or Comet during evaluation.
+        is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`):
+            When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument,
+            you need to specify if the model returned by the callable is an encoder-decoder model.
+        model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
+            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
+            string.
+        dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
+            Number of processes to use for processing the dataset.
+    
+    """
+    vllm_sampling_params: Optional[Any] = field(
+        default = None,
+        metadata = {'help': 'vLLM SamplingParams'},
+    )
+    unsloth_num_chunks : Optional[int] = field(
+        default = -1,
+        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
+    )
+    def __init__(
+        self,
+        output_dir = None,
+        overwrite_output_dir = None,
+        do_train = False,
+        do_eval = False,
+        do_predict = False,
+        eval_strategy = 'no',
+        prediction_loss_only = False,
+        per_device_train_batch_size = 4,
+        per_device_eval_batch_size = 4,
+        per_gpu_train_batch_size = None,
+        per_gpu_eval_batch_size = None,
+        gradient_accumulation_steps = 2,
+        eval_accumulation_steps = 2,
+        eval_delay = 0,
+        torch_empty_cache_steps = 250,
+        learning_rate = 5e-05,
+        weight_decay = 0.01,
+        adam_beta1 = 0.9,
+        adam_beta2 = 0.999,
+        adam_epsilon = 1e-08,
+        max_grad_norm = 1.0,
+        num_train_epochs = 3.0,
+        max_steps = -1,
+        lr_scheduler_type = 'linear',
+        warmup_ratio = 0.1,
+        warmup_steps = 0,
+        log_level = 'passive',
+        log_level_replica = 'warning',
+        log_on_each_node = True,
+        logging_dir = None,
+        logging_strategy = 'steps',
+        logging_first_step = False,
+        logging_steps = 1,
+        logging_nan_inf_filter = False,
+        save_strategy = 'steps',
+        save_steps = 500,
+        save_total_limit = None,
+        save_safetensors = True,
+        save_on_each_node = False,
+        save_only_model = False,
+        restore_callback_states_from_checkpoint = False,
+        no_cuda = False,
+        use_cpu = False,
+        use_mps_device = False,
+        seed = 3407,
+        data_seed = 3407,
+        jit_mode_eval = False,
+        use_ipex = False,
+        bf16 = False,
+        fp16 = False,
+        fp16_opt_level = 'O1',
+        half_precision_backend = 'auto',
+        bf16_full_eval = False,
+        fp16_full_eval = False,
+        tf32 = None,
+        local_rank = -1,
+        ddp_backend = None,
+        tpu_num_cores = None,
+        tpu_metrics_debug = False,
+        debug = '',
+        dataloader_drop_last = False,
+        eval_steps = None,
+        dataloader_num_workers = 0,
+        dataloader_prefetch_factor = None,
+        past_index = -1,
+        run_name = None,
+        disable_tqdm = None,
+        remove_unused_columns = True,
+        label_names = None,
+        load_best_model_at_end = False,
+        metric_for_best_model = None,
+        greater_is_better = None,
+        ignore_data_skip = False,
+        fsdp = '',
+        fsdp_min_num_params = 0,
+        fsdp_config = None,
+        tp_size = 0,
+        fsdp_transformer_layer_cls_to_wrap = None,
+        accelerator_config = None,
+        deepspeed = None,
+        label_smoothing_factor = 0.0,
+        optim = 'adamw_8bit',
+        optim_args = None,
+        adafactor = False,
+        group_by_length = False,
+        length_column_name = 'length',
+        report_to = None,
+        ddp_find_unused_parameters = None,
+        ddp_bucket_cap_mb = None,
+        ddp_broadcast_buffers = None,
+        dataloader_pin_memory = True,
+        dataloader_persistent_workers = False,
+        skip_memory_metrics = True,
+        use_legacy_prediction_loop = False,
+        push_to_hub = False,
+        resume_from_checkpoint = None,
+        hub_model_id = None,
+        hub_strategy = 'every_save',
+        hub_token = None,
+        hub_private_repo = None,
+        hub_always_push = False,
+        gradient_checkpointing = False,
+        gradient_checkpointing_kwargs = None,
+        include_inputs_for_metrics = False,
+        eval_do_concat_batches = True,
+        fp16_backend = 'auto',
+        push_to_hub_model_id = None,
+        push_to_hub_organization = None,
+        push_to_hub_token = None,
+        mp_parameters = '',
+        auto_find_batch_size = False,
+        full_determinism = False,
+        torchdynamo = None,
+        ray_scope = 'last',
+        ddp_timeout = 1800,
+        torch_compile = False,
+        torch_compile_backend = None,
+        torch_compile_mode = None,
+        include_tokens_per_second = False,
+        include_num_input_tokens_seen = False,
+        neftune_noise_alpha = None,
+        optim_target_modules = None,
+        batch_eval_metrics = False,
+        eval_on_start = False,
+        use_liger_kernel = False,
+        eval_use_gather_object = False,
+        average_tokens_across_devices = False,
+        max_length = 1024,
+        max_prompt_length = 512,
+        max_completion_length = None,
+        beta = 0.1,
+        disable_dropout = True,
+        label_pad_token_id = -100,
+        padding_value = None,
+        truncation_mode = 'keep_end',
+        generate_during_eval = False,
+        is_encoder_decoder = None,
+        model_init_kwargs = None,
+        dataset_num_proc = None,
+        vllm_sampling_params = None,
+        unsloth_num_chunks = -1,
+        **kwargs,
+    ):
+        if learning_rate < 1e-7: raise FloatingPointError(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
+        if learning_rate > 1: raise OverflowError(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
+        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
+            output_dir = 'unsloth_training_checkpoints'
+            save_strategy = 'no'
+        if dataset_num_proc is None:
+            from multiprocessing import cpu_count
+            dataset_num_proc = cpu_count()
+        
+        super().__init__(
+            output_dir = output_dir,
+            overwrite_output_dir = overwrite_output_dir,
+            do_train = do_train,
+            do_eval = do_eval,
+            do_predict = do_predict,
+            eval_strategy = eval_strategy,
+            prediction_loss_only = prediction_loss_only,
+            per_device_train_batch_size = per_device_train_batch_size,
+            per_device_eval_batch_size = per_device_eval_batch_size,
+            per_gpu_train_batch_size = per_gpu_train_batch_size,
+            per_gpu_eval_batch_size = per_gpu_eval_batch_size,
+            gradient_accumulation_steps = gradient_accumulation_steps,
+            eval_accumulation_steps = eval_accumulation_steps,
+            eval_delay = eval_delay,
+            torch_empty_cache_steps = torch_empty_cache_steps,
+            learning_rate = learning_rate,
+            weight_decay = weight_decay,
+            adam_beta1 = adam_beta1,
+            adam_beta2 = adam_beta2,
+            adam_epsilon = adam_epsilon,
+            max_grad_norm = max_grad_norm,
+            num_train_epochs = num_train_epochs,
+            max_steps = max_steps,
+            lr_scheduler_type = lr_scheduler_type,
+            warmup_ratio = warmup_ratio,
+            warmup_steps = warmup_steps,
+            log_level = log_level,
+            log_level_replica = log_level_replica,
+            log_on_each_node = log_on_each_node,
+            logging_dir = logging_dir,
+            logging_strategy = logging_strategy,
+            logging_first_step = logging_first_step,
+            logging_steps = logging_steps,
+            logging_nan_inf_filter = logging_nan_inf_filter,
+            save_strategy = save_strategy,
+            save_steps = save_steps,
+            save_total_limit = save_total_limit,
+            save_safetensors = save_safetensors,
+            save_on_each_node = save_on_each_node,
+            save_only_model = save_only_model,
+            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
+            no_cuda = no_cuda,
+            use_cpu = use_cpu,
+            use_mps_device = use_mps_device,
+            seed = seed,
+            data_seed = data_seed,
+            jit_mode_eval = jit_mode_eval,
+            use_ipex = use_ipex,
+            bf16 = bf16,
+            fp16 = fp16,
+            fp16_opt_level = fp16_opt_level,
+            half_precision_backend = half_precision_backend,
+            bf16_full_eval = bf16_full_eval,
+            fp16_full_eval = fp16_full_eval,
+            tf32 = tf32,
+            local_rank = local_rank,
+            ddp_backend = ddp_backend,
+            tpu_num_cores = tpu_num_cores,
+            tpu_metrics_debug = tpu_metrics_debug,
+            debug = debug,
+            dataloader_drop_last = dataloader_drop_last,
+            eval_steps = eval_steps,
+            dataloader_num_workers = dataloader_num_workers,
+            dataloader_prefetch_factor = dataloader_prefetch_factor,
+            past_index = past_index,
+            run_name = run_name,
+            disable_tqdm = disable_tqdm,
+            remove_unused_columns = remove_unused_columns,
+            label_names = label_names,
+            load_best_model_at_end = load_best_model_at_end,
+            metric_for_best_model = metric_for_best_model,
+            greater_is_better = greater_is_better,
+            ignore_data_skip = ignore_data_skip,
+            fsdp = fsdp,
+            fsdp_min_num_params = fsdp_min_num_params,
+            fsdp_config = fsdp_config,
+            tp_size = tp_size,
+            fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
+            accelerator_config = accelerator_config,
+            deepspeed = deepspeed,
+            label_smoothing_factor = label_smoothing_factor,
+            optim = optim,
+            optim_args = optim_args,
+            adafactor = adafactor,
+            group_by_length = group_by_length,
+            length_column_name = length_column_name,
+            report_to = report_to,
+            ddp_find_unused_parameters = ddp_find_unused_parameters,
+            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
+            ddp_broadcast_buffers = ddp_broadcast_buffers,
+            dataloader_pin_memory = dataloader_pin_memory,
+            dataloader_persistent_workers = dataloader_persistent_workers,
+            skip_memory_metrics = skip_memory_metrics,
+            use_legacy_prediction_loop = use_legacy_prediction_loop,
+            push_to_hub = push_to_hub,
+            resume_from_checkpoint = resume_from_checkpoint,
+            hub_model_id = hub_model_id,
+            hub_strategy = hub_strategy,
+            hub_token = hub_token,
+            hub_private_repo = hub_private_repo,
+            hub_always_push = hub_always_push,
+            gradient_checkpointing = gradient_checkpointing,
+            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
+            include_inputs_for_metrics = include_inputs_for_metrics,
+            eval_do_concat_batches = eval_do_concat_batches,
+            fp16_backend = fp16_backend,
+            push_to_hub_model_id = push_to_hub_model_id,
+            push_to_hub_organization = push_to_hub_organization,
+            push_to_hub_token = push_to_hub_token,
+            mp_parameters = mp_parameters,
+            auto_find_batch_size = auto_find_batch_size,
+            full_determinism = full_determinism,
+            torchdynamo = torchdynamo,
+            ray_scope = ray_scope,
+            ddp_timeout = ddp_timeout,
+            torch_compile = torch_compile,
+            torch_compile_backend = torch_compile_backend,
+            torch_compile_mode = torch_compile_mode,
+            include_tokens_per_second = include_tokens_per_second,
+            include_num_input_tokens_seen = include_num_input_tokens_seen,
+            neftune_noise_alpha = neftune_noise_alpha,
+            optim_target_modules = optim_target_modules,
+            batch_eval_metrics = batch_eval_metrics,
+            eval_on_start = eval_on_start,
+            use_liger_kernel = use_liger_kernel,
+            eval_use_gather_object = eval_use_gather_object,
+            average_tokens_across_devices = average_tokens_across_devices,
+            max_length = max_length,
+            max_prompt_length = max_prompt_length,
+            max_completion_length = max_completion_length,
+            beta = beta,
+            disable_dropout = disable_dropout,
+            label_pad_token_id = label_pad_token_id,
+            padding_value = padding_value,
+            truncation_mode = truncation_mode,
+            generate_during_eval = generate_during_eval,
+            is_encoder_decoder = is_encoder_decoder,
+            model_init_kwargs = model_init_kwargs,
+            dataset_num_proc = dataset_num_proc,**kwargs)
+        self.vllm_sampling_params = vllm_sampling_params
+        self.unsloth_num_chunks = unsloth_num_chunks
+pass
+
+class _UnslothORPOTrainer(Trainer):
+    r""""""
+
+    _tag_names = ["trl", "orpo"]
+
+    def __init__(
+        self,
+        model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
+        args: Optional[ORPOConfig] = None,
+        data_collator: Optional[DataCollator] = None,
+        train_dataset: Optional[Dataset] = None,
+        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
+        processing_class: Optional[
+            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
+        ] = None,
+        model_init: Optional[Callable[[], PreTrainedModel]] = None,
+        callbacks: Optional[list[TrainerCallback]] = None,
+        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
+        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+        peft_config: Optional[dict] = None,
+        compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None,
+    ):
+        if args.model_init_kwargs is None:
+            model_init_kwargs = {}
+        elif not isinstance(model, str):
+            raise ValueError("You passed model_kwargs to the ORPOTrainer. But your model is already instantiated.")
+        else:
+            model_init_kwargs = args.model_init_kwargs
+            torch_dtype = model_init_kwargs.get("torch_dtype")
+            if torch_dtype is not None:
+                # Convert to `torch.dtype` if an str is passed
+                if isinstance(torch_dtype, str) and torch_dtype != "auto":
+                    torch_dtype = getattr(torch, torch_dtype)
+                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
+                    raise ValueError(
+                        f"Invalid `torch_dtype` passed to the ORPOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
+                    )
+                model_init_kwargs["torch_dtype"] = torch_dtype
+
+        if isinstance(model, str):
+            model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)
+
+        # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16`
+        # has been called in order to properly call autocast if needed.
+        self._peft_has_been_casted_to_bf16 = False
+
+        if not is_peft_available() and peft_config is not None:
+            raise ValueError(
+                "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models"
+            )
+        elif is_peft_available() and peft_config is not None:
+            # if model is a peft model and we have a peft_config, we merge and unload it first
+            if isinstance(model, PeftModel):
+                model = model.merge_and_unload()
+
+            if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
+                _support_gc_kwargs = hasattr(
+                    args, "gradient_checkpointing_kwargs"
+                ) and "gradient_checkpointing_kwargs" in list(
+                    inspect.signature(prepare_model_for_kbit_training).parameters
+                )
+
+                prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
+
+                if _support_gc_kwargs:
+                    prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
+
+                model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)
+            elif getattr(args, "gradient_checkpointing", False):
+                # For backward compatibility with older versions of transformers
+                if hasattr(model, "enable_input_require_grads"):
+                    model.enable_input_require_grads()
+                else:
+
+                    def make_inputs_require_grad(module, input, output):
+                        output.requires_grad_(True)
+
+                    model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+            # get peft model with the given config
+            model = model
+            if args.bf16 and getattr(model, "is_loaded_in_4bit", False):
+                peft_module_casting_to_bf16(model)
+                # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager
+                self._peft_has_been_casted_to_bf16 = True
+
+        # For models that use gradient_checkpointing, we need to attach a hook that enables input
+        # to explicitly have `requires_grad=True`, otherwise training will either silently
+        # fail or completely fail.
+        elif getattr(args, "gradient_checkpointing", False):
+            # For backward compatibility with older versions of transformers
+            if hasattr(model, "enable_input_require_grads"):
+                model.enable_input_require_grads()
+            else:
+
+                def make_inputs_require_grad(module, input, output):
+                    output.requires_grad_(True)
+
+                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+        if args.generate_during_eval and not (is_wandb_available() or is_comet_available()):
+            raise ValueError(
+                "`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
+                " Please install `wandb` or `comet-ml` to resolve."
+            )
+
+        if model is not None:
+            self.is_encoder_decoder = model.config.is_encoder_decoder
+        elif args.is_encoder_decoder is None:
+            raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.")
+        else:
+            self.is_encoder_decoder = args.is_encoder_decoder
+
+        if self.is_encoder_decoder:
+            self.decoder_start_token_id = model.config.decoder_start_token_id
+            self.pad_token_id = model.config.pad_token_id
+
+        if processing_class is None:
+            raise ValueError("processing_class must be specified to tokenize a ORPO dataset.")
+        if args.max_length is None:
+            warnings.warn(
+                "`max_length` is not set in the ORPOConfig's init"
+                " it will default to `512` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_length = 512
+        else:
+            max_length = args.max_length
+        if args.max_prompt_length is None:
+            warnings.warn(
+                "`max_prompt_length` is not set in the ORPOConfig's init"
+                " it will default to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_prompt_length = 128
+        else:
+            max_prompt_length = args.max_prompt_length
+
+        if args.max_completion_length is None and self.is_encoder_decoder:
+            warnings.warn(
+                "When using an encoder decoder architecture, you should set `max_completion_length` in the ORPOConfig's init"
+                " it will default to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            self.max_completion_length = 128
+        else:
+            self.max_completion_length = args.max_completion_length
+
+        if data_collator is None:
+            data_collator = DPODataCollatorWithPadding(
+                pad_token_id=processing_class.pad_token_id,
+                label_pad_token_id=args.label_pad_token_id,
+                is_encoder_decoder=self.is_encoder_decoder,
+            )
+
+            if args.remove_unused_columns:
+                args.remove_unused_columns = False
+                # warn users
+                warnings.warn(
+                    "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments"
+                    " we have set it for you, but you should do it yourself in the future.",
+                    UserWarning,
+                )
+
+            self.use_dpo_data_collator = True
+        else:
+            self.use_dpo_data_collator = False
+
+        # Disable dropout in the model and reference model
+        if args.disable_dropout:
+            disable_dropout_in_model(model)
+
+        self.max_length = max_length
+        self.generate_during_eval = args.generate_during_eval
+        self.label_pad_token_id = args.label_pad_token_id
+        self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id
+        self.max_prompt_length = max_prompt_length
+        self.truncation_mode = args.truncation_mode
+        self.processing_class = processing_class
+
+        self.beta = args.beta
+        self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)
+        self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0)
+        if self.aux_loss_enabled and self.aux_loss_coef == 0.0:
+            warnings.warn(
+                "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to "
+                "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value "
+                "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary "
+                "loss.",
+                UserWarning,
+            )
+
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+
+        # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
+        # input tensor associated with the key "input_ids". However, in ORPO, the sampled data does not include the
+        # "input_ids" key. Instead, the available keys are "prompt_input_ids", "chosen_input_ids", and
+        # "rejected_input_ids". As a result, the trainer issues the warning: "Could not estimate the number of tokens
+        # of the input, floating-point operations will not be computed." To suppress this warning, we set the
+        # "estimate_tokens" key in the model's "warnings_issued" dictionary to True. This acts as a flag to indicate
+        # that the warning has already been issued.
+        model.warnings_issued["estimate_tokens"] = True
+
+        # Compute that only on the main process for faster data processing.
+        # see: https://github.com/huggingface/trl/pull/1255
+        with PartialState().local_main_process_first():
+            # Extract the prompt if needed, and apply the chat template if needed
+            train_dataset = train_dataset.map(maybe_extract_prompt, num_proc=args.dataset_num_proc)
+            train_dataset = train_dataset.map(
+                maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}, num_proc=args.dataset_num_proc
+            )
+            train_dataset = train_dataset.map(self.tokenize_row, num_proc=args.dataset_num_proc)
+            if eval_dataset is not None:
+                eval_dataset = eval_dataset.map(maybe_extract_prompt, num_proc=args.dataset_num_proc)
+                eval_dataset = eval_dataset.map(
+                    maybe_apply_chat_template,
+                    fn_kwargs={"tokenizer": processing_class},
+                    num_proc=args.dataset_num_proc,
+                )
+                eval_dataset = eval_dataset.map(self.tokenize_row, num_proc=args.dataset_num_proc)
+
+        super().__init__(
+            model=model,
+            args=args,
+            data_collator=data_collator,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            processing_class=processing_class,
+            model_init=model_init,
+            compute_metrics=compute_metrics,
+            callbacks=callbacks,
+            optimizers=optimizers,
+            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+        )
+
+        # Add tags for models that have been loaded with the correct transformers version
+        if hasattr(self.model, "add_model_tags"):
+            self.model.add_model_tags(self._tag_names)
+
+        if not hasattr(self, "accelerator"):
+            raise AttributeError(
+                "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`."
+            )
+
+    def _prepare_deepspeed(self, model: PreTrainedModelWrapper):
+        # Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
+        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
+        config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config)
+
+        if model is not None:
+            if hasattr(model, "config"):
+                hidden_size = (
+                    max(model.config.hidden_sizes)
+                    if getattr(model.config, "hidden_sizes", None)
+                    else getattr(model.config, "hidden_size", None)
+                )
+                if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3:
+                    # Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0`
+                    # This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081
+                    config_kwargs.update(
+                        {
+                            "zero_optimization.reduce_bucket_size": hidden_size * hidden_size,
+                            "zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size,
+                            "zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size,
+                        }
+                    )
+
+        # If ZeRO-3 is used, we shard both the active and reference model.
+        # Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0)
+        if config_kwargs["zero_optimization"]["stage"] != 3:
+            config_kwargs["zero_optimization"]["stage"] = 0
+        model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
+        model.eval()
+        return model
+
+    def build_tokenized_answer(self, prompt, answer):
+        """
+        Llama tokenizer does satisfy `enc(a + b) = enc(a) + enc(b)`.
+        It does ensure `enc(a + b) = enc(a) + enc(a + b)[len(enc(a)):]`.
+        Reference:
+            https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257
+        """
+
+        full_tokenized = self.processing_class(prompt + answer, add_special_tokens=False)
+        prompt_input_ids = self.processing_class(prompt, add_special_tokens=False)["input_ids"]
+
+        answer_input_ids = full_tokenized["input_ids"][len(prompt_input_ids) :]
+        answer_attention_mask = full_tokenized["attention_mask"][len(prompt_input_ids) :]
+
+        # Concat tokens to form `enc(a) + enc(a + b)[len(enc(a)):]`
+        full_concat_input_ids = np.concatenate([prompt_input_ids, answer_input_ids])
+
+        # Prepare input tokens for token by token comparison
+        full_input_ids = np.array(full_tokenized["input_ids"])
+
+        if len(full_input_ids) != len(full_concat_input_ids):
+            raise ValueError("Prompt input ids and answer input ids should have the same length.")
+
+        # On some tokenizers, like Llama-2 tokenizer, there are occasions where tokens
+        # can be merged together when tokenizing prompt+answer. This could result
+        # on the last token from the prompt being different when tokenized on its own
+        # vs when done as prompt+answer.
+        response_token_ids_start_idx = len(prompt_input_ids)
+
+        # If tokenized prompt is different than both prompt+answer, then it means the
+        # last token has changed due to merging.
+        if prompt_input_ids != full_tokenized["input_ids"][:response_token_ids_start_idx]:
+            response_token_ids_start_idx -= 1
+
+        prompt_input_ids = full_tokenized["input_ids"][:response_token_ids_start_idx]
+        prompt_attention_mask = full_tokenized["attention_mask"][:response_token_ids_start_idx]
+
+        if len(prompt_input_ids) != len(prompt_attention_mask):
+            raise ValueError("Prompt input ids and attention mask should have the same length.")
+
+        answer_input_ids = full_tokenized["input_ids"][response_token_ids_start_idx:]
+        answer_attention_mask = full_tokenized["attention_mask"][response_token_ids_start_idx:]
+
+        return dict(
+            prompt_input_ids=prompt_input_ids,
+            prompt_attention_mask=prompt_attention_mask,
+            input_ids=answer_input_ids,
+            attention_mask=answer_attention_mask,
+        )
+
+    def tokenize_row(self, feature, model: Optional[Union[PreTrainedModel, nn.Module]] = None) -> dict:
+        """Tokenize a single row from a ORPO specific dataset.
+
+        At this stage, we don't convert to PyTorch tensors yet; we just handle the truncation
+        in case the prompt + chosen or prompt + rejected responses is/are too long. First
+        we truncate the prompt; if we're still too long, we truncate the chosen/rejected.
+
+        We also create the labels for the chosen/rejected responses, which are of length equal to
+        the sum of the length of the prompt and the chosen/rejected response, with
+        label_pad_token_id  for the prompt tokens.
+        """
+        batch = {}
+        prompt = feature["prompt"]
+        chosen = feature["chosen"]
+        rejected = feature["rejected"]
+
+        if not self.is_encoder_decoder:
+            # Check issues below for more details
+            #  1. https://github.com/huggingface/trl/issues/907
+            #  2. https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257
+            #  3. https://github.com/LianjiaTech/BELLE/issues/337
+
+            if not isinstance(prompt, str):
+                raise ValueError(f"prompt should be an str but got {type(prompt)}")
+            prompt_tokens = self.processing_class(prompt, add_special_tokens=False)
+            prompt_tokens = {f"prompt_{k}": v for k, v in prompt_tokens.items()}
+
+            if not isinstance(chosen, str):
+                raise ValueError(f"chosen should be an str but got {type(chosen)}")
+            chosen_tokens = self.build_tokenized_answer(prompt, chosen)
+
+            if not isinstance(rejected, str):
+                raise ValueError(f"rejected should be an str but got {type(rejected)}")
+            rejected_tokens = self.build_tokenized_answer(prompt, rejected)
+
+            # Last prompt token might get merged by tokenizer and
+            # it should not be included for generation if that happens
+            prompt_len_input_ids = len(prompt_tokens["prompt_input_ids"])
+
+            chosen_prompt_len_input_ids = len(chosen_tokens["prompt_input_ids"])
+            rejected_prompt_len_input_ids = len(rejected_tokens["prompt_input_ids"])
+            prompt_len_input_ids = min(chosen_prompt_len_input_ids, rejected_prompt_len_input_ids)
+
+            for k, v in prompt_tokens.items():
+                prompt_tokens[k] = v[:prompt_len_input_ids]
+
+            # Make sure prompts only have one different token at most an
+            # and length only differs by 1 at most
+            num_diff_tokens = sum(
+                [a != b for a, b in zip(chosen_tokens["prompt_input_ids"], rejected_tokens["prompt_input_ids"])]
+            )
+            num_diff_len = abs(chosen_prompt_len_input_ids - rejected_prompt_len_input_ids)
+            if num_diff_tokens > 1 or num_diff_len > 1:
+                raise ValueError(
+                    "Chosen and rejected prompt_input_ids might only differ on the "
+                    "last token due to tokenizer merge ops."
+                )
+
+            # add BOS token to head of prompt. Avoid adding if it's already there
+            prompt_tokens, chosen_tokens, rejected_tokens = add_bos_token_if_needed(
+                self.processing_class.bos_token_id,
+                prompt_len_input_ids,
+                prompt_tokens,
+                chosen_prompt_len_input_ids,
+                chosen_tokens,
+                rejected_prompt_len_input_ids,
+                rejected_tokens,
+            )
+
+            # add EOS token to end of answer. Avoid adding if it's already there
+            chosen_tokens, rejected_tokens = add_eos_token_if_needed(
+                self.processing_class.eos_token_id, chosen_tokens, rejected_tokens
+            )
+
+            longer_response_length = max(len(chosen_tokens["input_ids"]), len(rejected_tokens["input_ids"]))
+
+            # if combined sequence is too long, truncate the prompt
+            for answer_tokens in [chosen_tokens, rejected_tokens, prompt_tokens]:
+                if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length:
+                    if self.truncation_mode == "keep_start":
+                        for k in ["prompt_input_ids", "prompt_attention_mask"]:
+                            answer_tokens[k] = answer_tokens[k][: self.max_prompt_length]
+                    elif self.truncation_mode == "keep_end":
+                        for k in ["prompt_input_ids", "prompt_attention_mask"]:
+                            answer_tokens[k] = answer_tokens[k][-self.max_prompt_length :]
+                    else:
+                        raise ValueError(f"Unknown truncation mode: {self.truncation_mode}")
+
+            # if that's still too long, truncate the response
+            for answer_tokens in [chosen_tokens, rejected_tokens]:
+                if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length:
+                    for k in ["input_ids", "attention_mask"]:
+                        answer_tokens[k] = answer_tokens[k][: self.max_length - self.max_prompt_length]
+
+            # Create labels
+            chosen_sequence_tokens = {
+                k: chosen_tokens[f"prompt_{k}"] + chosen_tokens[k] for k in ["input_ids", "attention_mask"]
+            }
+            rejected_sequence_tokens = {
+                k: rejected_tokens[f"prompt_{k}"] + rejected_tokens[k] for k in ["input_ids", "attention_mask"]
+            }
+            chosen_sequence_tokens["labels"] = chosen_sequence_tokens["input_ids"][:]
+            chosen_sequence_tokens["labels"][: len(chosen_tokens["prompt_input_ids"])] = [
+                self.label_pad_token_id
+            ] * len(chosen_tokens["prompt_input_ids"])
+            rejected_sequence_tokens["labels"] = rejected_sequence_tokens["input_ids"][:]
+            rejected_sequence_tokens["labels"][: len(rejected_tokens["prompt_input_ids"])] = [
+                self.label_pad_token_id
+            ] * len(rejected_tokens["prompt_input_ids"])
+
+            for k, toks in {
+                "chosen_": chosen_sequence_tokens,
+                "rejected_": rejected_sequence_tokens,
+                "": prompt_tokens,
+            }.items():
+                for type_key, tokens in toks.items():
+                    if type_key == "token_type_ids":
+                        continue
+                    batch[f"{k}{type_key}"] = tokens
+
+        else:
+            chosen_tokens = self.processing_class(
+                chosen, truncation=True, max_length=self.max_completion_length, add_special_tokens=True
+            )
+            rejected_tokens = self.processing_class(
+                rejected, truncation=True, max_length=self.max_completion_length, add_special_tokens=True
+            )
+            prompt_tokens = self.processing_class(
+                prompt, truncation=True, max_length=self.max_prompt_length, add_special_tokens=True
+            )
+
+            batch["chosen_labels"] = chosen_tokens["input_ids"]
+            batch["rejected_labels"] = rejected_tokens["input_ids"]
+            batch["prompt_input_ids"] = prompt_tokens["input_ids"]
+            batch["prompt_attention_mask"] = prompt_tokens["attention_mask"]
+
+            if model is not None and hasattr(model, "prepare_decoder_input_ids_from_labels"):
+                batch["rejected_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels(
+                    labels=torch.tensor(batch["rejected_labels"])
+                )
+                batch["chosen_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels(
+                    labels=torch.tensor(batch["chosen_labels"])
+                )
+
+        if is_torch_xla_available():
+            # Pad the sequences to global max_length to avoid TorchXLA recompilation
+            for k in batch:
+                if "labels" in k or self.is_encoder_decoder:
+                    pad_value = self.label_pad_token_id
+                elif k.endswith("_input_ids"):
+                    pad_value = self.padding_value
+                elif k.endswith("_attention_mask"):
+                    pad_value = 0
+                batch[k] = batch[k] + [pad_value] * (self.max_length - len(batch[k]))
+        return batch
+
+    @staticmethod
+    def concatenated_inputs(
+        batch: dict[str, Union[list, torch.LongTensor]],
+        is_encoder_decoder: bool = False,
+        label_pad_token_id: int = -100,
+        padding_value: int = 0,
+        device: Optional[torch.device] = None,
+    ) -> dict[str, torch.LongTensor]:
+        """Concatenate the chosen and rejected inputs into a single tensor.
+
+        Args:
+            batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length).
+            is_encoder_decoder: Whether the model is an encoder-decoder model.
+            label_pad_token_id: The label pad token id.
+            padding_value: The padding value to use for the concatenated inputs_ids.
+            device: The device for the concatenated inputs.
+
+        Returns:
+            A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'.
+        """
+        concatenated_batch = {}
+
+        if is_encoder_decoder:
+            max_length = max(batch["chosen_labels"].shape[1], batch["rejected_labels"].shape[1])
+        else:
+            max_length = max(batch["chosen_input_ids"].shape[1], batch["rejected_input_ids"].shape[1])
+
+        for k in batch:
+            if k.startswith("chosen") and isinstance(batch[k], torch.Tensor):
+                if "labels" in k or is_encoder_decoder:
+                    pad_value = label_pad_token_id
+                elif k.endswith("_input_ids"):
+                    pad_value = padding_value
+                elif k.endswith("_attention_mask"):
+                    pad_value = 0
+                concatenated_key = k.replace("chosen", "concatenated")
+                concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value)
+        for k in batch:
+            if k.startswith("rejected") and isinstance(batch[k], torch.Tensor):
+                if "labels" in k or is_encoder_decoder:
+                    pad_value = label_pad_token_id
+                elif k.endswith("_input_ids"):
+                    pad_value = padding_value
+                elif k.endswith("_attention_mask"):
+                    pad_value = 0
+                concatenated_key = k.replace("rejected", "concatenated")
+                concatenated_batch[concatenated_key] = torch.cat(
+                    (
+                        concatenated_batch[concatenated_key],
+                        pad_to_length(batch[k], max_length, pad_value=pad_value),
+                    ),
+                    dim=0,
+                ).to(device=device)
+
+        if is_encoder_decoder:
+            concatenated_batch["concatenated_input_ids"] = batch["prompt_input_ids"].repeat(2, 1).to(device=device)
+            concatenated_batch["concatenated_attention_mask"] = (
+                batch["prompt_attention_mask"].repeat(2, 1).to(device=device)
+            )
+
+        return concatenated_batch
+
+    def odds_ratio_loss(
+        self,
+        policy_chosen_logps: torch.FloatTensor,
+        policy_rejected_logps: torch.FloatTensor,
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+        """Compute ORPO's odds ratio (OR) loss for a batch of policy and reference model log probabilities.
+
+        Args:
+            policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,)
+            policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_size,)
+
+        Returns:
+            A tuple of three tensors: (losses, chosen_rewards, rejected_rewards).
+            The losses tensor contains the ORPO loss for each example in the batch.
+            The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.
+            The log odds ratio of the chosen responses over the rejected responses ratio for logging purposes.
+            The `log(sigmoid(log_odds_chosen))` for logging purposes.
+        """
+
+        # Derived from Eqs. (4) and (7) from https://huggingface.co/papers/2403.07691 by using log identities and exp(log(P(y|x)) = P(y|x)
+        log_odds = (policy_chosen_logps - policy_rejected_logps) - (
+            torch.log1p(-torch.exp(policy_chosen_logps)) - torch.log1p(-torch.exp(policy_rejected_logps))
+        )
+        ratio = F.logsigmoid(log_odds)
+        losses = self.beta * ratio
+
+        chosen_rewards = self.beta * (policy_chosen_logps.to(self.accelerator.device)).detach()
+        rejected_rewards = self.beta * (policy_rejected_logps.to(self.accelerator.device)).detach()
+
+        return losses, chosen_rewards, rejected_rewards, torch.mean(ratio), torch.mean(log_odds)
+
+    @staticmethod
+    def get_batch_logps(
+        logits: torch.FloatTensor,
+        labels: torch.LongTensor,
+        average_log_prob: bool = False,
+        label_pad_token_id: int = -100,
+        is_encoder_decoder: bool = False,
+    ) -> torch.FloatTensor:
+        """Compute the log probabilities of the given labels under the given logits.
+
+        Args:
+            logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)
+            labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length)
+            average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.
+            label_pad_token_id: The label pad token id.
+            is_encoder_decoder: Whether the model is an encoder-decoder model.
+
+        Returns:
+            A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.
+        """
+        if logits.shape[:-1] != labels.shape:
+            raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.")
+
+        if not is_encoder_decoder:
+            labels = labels[:, 1:].clone()
+            logits = logits[:, :-1, :]
+        loss_mask = labels != label_pad_token_id
+
+        # dummy token; we'll ignore the losses on these tokens later
+        labels = torch.where(labels == label_pad_token_id, 0, labels)
+
+        per_token_logps = selective_log_softmax(logits, labels)
+
+        if average_log_prob:
+            return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
+        else:
+            return (per_token_logps * loss_mask).sum(-1)
+
+    def concatenated_forward(
+        self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]]
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
+        """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.
+
+        We do this to avoid doing two forward passes, because it's faster for FSDP.
+        """
+        concatenated_batch = self.concatenated_inputs(
+            batch,
+            is_encoder_decoder=self.is_encoder_decoder,
+            label_pad_token_id=self.label_pad_token_id,
+            padding_value=self.padding_value,
+            device=self.accelerator.device,
+        )
+        len_chosen = batch["chosen_labels"].shape[0]
+
+        model_kwargs = (
+            {
+                "decoder_input_ids": self._shift_right(concatenated_batch["concatenated_labels"]),
+            }
+            if self.is_encoder_decoder
+            else {}
+        )
+
+        if self.aux_loss_enabled:
+            model_kwargs["output_router_logits"] = True
+
+        outputs = model(
+            concatenated_batch["concatenated_input_ids"],
+            attention_mask=concatenated_batch["concatenated_attention_mask"],
+            use_cache=False,
+            **model_kwargs,
+        )
+        all_logits = outputs.logits
+
+        def cross_entropy_loss(logits, labels):
+            if not self.is_encoder_decoder:
+                # Shift so that tokens < n predict n
+                logits = logits[..., :-1, :].contiguous()
+                labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            logits = logits.view(-1, logits.shape[-1])
+            labels = labels.view(-1)
+            # Enable model parallelism
+            labels = labels.to(logits.device)
+            loss = loss_fct(logits, labels)
+            return loss
+
+        if self.is_encoder_decoder:
+            labels = concatenated_batch["concatenated_labels"].clone()
+        else:
+            labels = concatenated_batch["concatenated_input_ids"].clone()
+            attention_mask = concatenated_batch["concatenated_attention_mask"]
+            labels = torch.where(attention_mask == 1, labels, self.label_pad_token_id)
+        # orpo chosen nll loss is computed over the full prompt and response
+        chosen_nll_loss = cross_entropy_loss(all_logits[:len_chosen], labels[:len_chosen])
+
+        all_logps = self.get_batch_logps(
+            all_logits,
+            concatenated_batch["concatenated_labels"],
+            average_log_prob=True,
+            is_encoder_decoder=self.is_encoder_decoder,
+            label_pad_token_id=self.label_pad_token_id,
+        )
+
+        chosen_logps = all_logps[:len_chosen]
+        rejected_logps = all_logps[len_chosen:]
+
+        if not self.is_encoder_decoder:
+            chosen_logits = all_logits[:len_chosen, :-1, :]
+            rejected_logits = all_logits[len_chosen:, :-1, :]
+        else:
+            chosen_logits = all_logits[:len_chosen]
+            rejected_logits = all_logits[len_chosen:]
+
+        if self.aux_loss_enabled:
+            return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, chosen_nll_loss, outputs.aux_loss)
+
+        return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, chosen_nll_loss)
+
+    def get_batch_loss_metrics(
+        self,
+        model,
+        batch: dict[str, Union[list, torch.LongTensor]],
+        train_eval: Literal["train", "eval"] = "train",
+    ):
+        """Compute the ORPO loss and other metrics for the given batch of inputs for train or test."""
+        metrics = {}
+
+        forward_output = self.concatenated_forward(model, batch)
+        (
+            policy_chosen_logps,
+            policy_rejected_logps,
+            policy_chosen_logits,
+            policy_rejected_logits,
+            policy_nll_loss,
+        ) = forward_output[:5]
+        if self.aux_loss_enabled:
+            aux_loss = forward_output[5]
+
+        losses, chosen_rewards, rejected_rewards, log_odds_ratio, log_odds_chosen = self.odds_ratio_loss(
+            policy_chosen_logps, policy_rejected_logps
+        )
+        # full ORPO loss
+        loss = policy_nll_loss - losses.mean()
+
+        reward_accuracies = (chosen_rewards > rejected_rewards).float()
+
+        prefix = "eval_" if train_eval == "eval" else ""
+        metrics[f"{prefix}rewards/chosen"] = self.accelerator.gather_for_metrics(chosen_rewards).mean()
+        metrics[f"{prefix}rewards/rejected"] = self.accelerator.gather_for_metrics(rejected_rewards).mean()
+        metrics[f"{prefix}rewards/accuracies"] = self.accelerator.gather_for_metrics(reward_accuracies).mean()
+        metrics[f"{prefix}rewards/margins"] = self.accelerator.gather_for_metrics(
+            chosen_rewards - rejected_rewards
+        ).mean()
+        metrics[f"{prefix}logps/rejected"] = self.accelerator.gather_for_metrics(policy_rejected_logps).detach().mean()
+        metrics[f"{prefix}logps/chosen"] = self.accelerator.gather_for_metrics(policy_chosen_logps).detach().mean()
+        metrics[f"{prefix}logits/rejected"] = (
+            self.accelerator.gather_for_metrics(policy_rejected_logits).detach().mean()
+        )
+        metrics[f"{prefix}logits/chosen"] = self.accelerator.gather_for_metrics(policy_chosen_logits).detach().mean()
+        metrics[f"{prefix}nll_loss"] = self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean()
+        metrics[f"{prefix}log_odds_ratio"] = self.accelerator.gather_for_metrics(log_odds_ratio).mean()
+        metrics[f"{prefix}log_odds_chosen"] = self.accelerator.gather_for_metrics(log_odds_chosen).mean()
+        if is_torch_xla_available():
+            xm.mark_step()  # needed because .item() calls
+        for k, v in metrics.items():
+            metrics[k] = v.item()
+        if self.aux_loss_enabled:
+            loss += self.aux_loss_coef * aux_loss
+
+        return loss, metrics
+
+    def compute_loss(
+        self,
+        model: Union[PreTrainedModel, nn.Module],
+        inputs: dict[str, Union[torch.Tensor, Any]],
+        return_outputs=False,
+        num_items_in_batch=None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]:
+        compute_loss_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+
+        with compute_loss_context_manager:
+            loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="train")
+
+        # Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class:
+        loss = loss.to(self.args.device)
+
+        # force log the metrics
+        self.store_metrics(metrics, train_eval="train")
+
+        if return_outputs:
+            return (loss, metrics)
+        return loss
+
+    def generate_from_model(self, model, batch: dict[str, torch.LongTensor]) -> str:
+        """Generate samples from the model and reference model for the given batch of inputs."""
+
+        # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with
+        # the torch cuda amp context manager as some hidden states are silently casted to full precision.
+        generate_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+
+        with generate_context_manager:
+            policy_output = model.generate(
+                input_ids=batch["prompt_input_ids"],
+                attention_mask=batch["prompt_attention_mask"],
+                max_length=self.max_length,
+                do_sample=True,
+                pad_token_id=self.processing_class.pad_token_id,
+            )
+
+        policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id)
+        policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True)
+
+        return policy_output_decoded
+
+    def prediction_step(
+        self,
+        model: Union[PreTrainedModel, nn.Module],
+        inputs: dict[str, Union[torch.Tensor, Any]],
+        prediction_loss_only: bool,
+        ignore_keys: Optional[list[str]] = None,
+    ):
+        if not self.use_dpo_data_collator:
+            warnings.warn(
+                "prediction_step is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than "
+                "DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator"
+            )
+        if ignore_keys is None:
+            if hasattr(model, "config"):
+                ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", [])
+            else:
+                ignore_keys = []
+
+        prediction_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()
+
+        with torch.no_grad(), prediction_context_manager:
+            loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="eval")
+
+        # force log the metrics
+        self.store_metrics(metrics, train_eval="eval")
+
+        if prediction_loss_only:
+            return (loss.detach(), None, None)
+
+        # logits for the chosen and rejected samples from model
+        logits_dict = {
+            "eval_logits/chosen": metrics["eval_logits/chosen"],
+            "eval_logits/rejected": metrics["eval_logits/rejected"],
+        }
+        logits = tuple(v.unsqueeze(dim=0) for k, v in logits_dict.items() if k not in ignore_keys)
+        logits = torch.stack(logits).mean(axis=1).to(self.accelerator.device)
+        labels = torch.zeros(logits.shape[0], device=self.accelerator.device)
+
+        return (loss.detach(), logits, labels)
+
+    def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None:
+        for key, value in metrics.items():
+            self._stored_metrics[train_eval][key].append(value)
+
+    def evaluation_loop(
+        self,
+        dataloader: DataLoader,
+        description: str,
+        prediction_loss_only: Optional[bool] = None,
+        ignore_keys: Optional[list[str]] = None,
+        metric_key_prefix: str = "eval",
+    ) -> EvalLoopOutput:
+        """
+        Overriding built-in evaluation loop to store metrics for each batch.
+        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.
+
+        Works both with or without labels.
+        """
+
+        # Sample and save to game log if requested (for one batch to save time)
+        if self.generate_during_eval:
+            # Generate random indices within the range of the total number of samples
+            num_samples = len(dataloader.dataset)
+            random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size)
+
+            # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader
+            random_batch_dataset = dataloader.dataset.select(random_indices)
+            random_batch = self.data_collator(random_batch_dataset)
+            random_batch = self._prepare_inputs(random_batch)
+
+            policy_output_decoded = self.generate_from_model(self.model, random_batch)
+
+            table = pd.DataFrame(
+                columns=["Prompt", "Policy"],
+                data=[
+                    [prompt, pol[len(prompt) :]] for prompt, pol in zip(random_batch["prompt"], policy_output_decoded)
+                ],
+            )
+            if "wandb" in self.args.report_to:
+                wandb.log({"game_log": wandb.Table(data=table)})
+
+            if "comet_ml" in self.args.report_to:
+                log_table_to_comet_experiment(
+                    name="game_log.csv",
+                    table=table,
+                )
+
+        # Base evaluation
+        initial_output = super().evaluation_loop(
+            dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix
+        )
+
+        return initial_output
+
+    def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None:
+        """
+        Log `logs` on the various objects watching training, including stored metrics.
+
+        Args:
+            logs (`dict[str, float]`):
+                The values to log.
+            start_time (`float` or `None`, *optional*, defaults to `None`):
+                Start time of the training.
+        """
+        # logs either has 'loss' or 'eval_loss'
+        train_eval = "train" if "loss" in logs else "eval"
+        # Add averaged stored metrics to logs
+        for key, metrics in self._stored_metrics[train_eval].items():
+            logs[key] = torch.tensor(metrics).mean().item()
+        del self._stored_metrics[train_eval]
+
+        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
+            return super().log(logs, start_time)
+        else:  # transformers<=4.46
+            return super().log(logs)
+
+    def _shift_right(self, input_ids):
+        if self.decoder_start_token_id is None:
+            raise ValueError(
+                "model.config.decoder_start_token_id has to be defined. It is usually set to the pad_token_id."
+            )
+
+        # shift inputs to the right
+        if is_torch_fx_proxy(input_ids):
+            # Item assignment is not supported natively for proxies.
+            shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), self.decoder_start_token_id)
+            shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
+        else:
+            shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+            shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
+            shifted_input_ids[..., 0] = self.decoder_start_token_id
+
+        if self.pad_token_id is None:
+            raise ValueError("model.config.pad_token_id has to be defined.")
+        # replace possible -100 values in labels by `pad_token_id`
+        shifted_input_ids.masked_fill_(shifted_input_ids == -100, self.pad_token_id)
+
+        return shifted_input_ids
+
+    def create_model_card(
+        self,
+        model_name: Optional[str] = None,
+        dataset_name: Optional[str] = None,
+        tags: Union[str, list[str], None] = None,
+    ):
+        """
+        Creates a draft of a model card using the information available to the `Trainer`.
+
+        Args:
+            model_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the model.
+            dataset_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the dataset used for training.
+            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
+                Tags to be associated with the model card.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
+            base_model = self.model.config._name_or_path
+        else:
+            base_model = None
+
+        tags = tags or []
+        if isinstance(tags, str):
+            tags = [tags]
+
+        if hasattr(self.model.config, "unsloth_version"):
+            tags.append("unsloth")
+
+        citation = textwrap.dedent("""\
+        @article{hong2024orpo,
+            title        = {{ORPO: Monolithic Preference Optimization without Reference Model}},
+            author       = {Jiwoo Hong and Noah Lee and James Thorne},
+            year         = 2024,
+            eprint       = {arXiv:2403.07691}
+        }""")
+
+        model_card = generate_model_card(
+            base_model=base_model,
+            model_name=model_name,
+            hub_model_id=self.hub_model_id,
+            dataset_name=dataset_name,
+            tags=tags,
+            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
+            comet_url=get_comet_experiment_url(),
+            trainer_name="ORPO",
+            trainer_citation=citation,
+            paper_title="ORPO: Monolithic Preference Optimization without Reference Model",
+            paper_id="2403.07691",
+        )
+
+        model_card.save(os.path.join(self.args.output_dir, "README.md"))
+class UnslothORPOTrainer(_UnslothORPOTrainer):
+    """
+    
+    Initialize ORPOTrainer.
+
+    Args:
+        model (`transformers.PreTrainedModel`):
+            The model to train, preferably an `AutoModelForSequenceClassification`.
+        args (`ORPOConfig`):
+            The ORPO config arguments to use for training.
+        data_collator (`transformers.DataCollator`):
+            The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used
+            which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
+        train_dataset (`datasets.Dataset`):
+            The dataset to use for training.
+        eval_dataset (`datasets.Dataset`):
+            The dataset to use for evaluation.
+        processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
+            Processing class used to process the data. If provided, will be used to automatically process the inputs
+            for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
+            reuse the fine-tuned model.
+        model_init (`Callable[[], transformers.PreTrainedModel]`):
+            The model initializer to use for training. If None is specified, the default model initializer will be used.
+        callbacks (`list[transformers.TrainerCallback]`):
+            The callbacks to use for training.
+        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
+            The optimizer and scheduler to use for training.
+        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
+            The function to use to preprocess the logits before computing the metrics.
+        peft_config (`dict`, defaults to `None`):
+            The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.
+        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
+            The function to use to compute the metrics. Must take a `EvalPrediction` and return
+            a dictionary string to metric values.
+    
+    """
+    def __init__(
+        self,
+        model = None,
+        args = None,
+        data_collator = None,
+        train_dataset = None,
+        eval_dataset = None,
+        processing_class = None,
+        model_init = None,
+        callbacks = None,
+        preprocess_logits_for_metrics = None,
+        peft_config = None,
+        compute_metrics = None,
+        **kwargs
+    ):
+        if args is None: args = UnslothORPOConfig()
+        use_bf16 = getattr(args, 'bf16', False)
+        use_fp16 = getattr(args, 'fp16', False)
+        force_float32 = False
+        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':
+            print('Unsloth: Switching to float32 training since model cannot work with float16')
+            force_float32 = True
+        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
+        dtype = getattr(model.config, 'torch_dtype', None)
+        if dtype is None: dtype = model.get_input_embeddings().dtype
+        from unsloth_zoo.utils import _get_dtype
+        dtype = _get_dtype(dtype)
+        float16 = dtype == torch.float16
+        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
+        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
+        if force_float32:
+            args.fp16 = False
+            args.bf16 = False
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
+        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
+            args.fp16 = float16
+            args.bf16 = not float16
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
+        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
+            args.eval_strategy = 'steps'
+            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
+        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
+        if ga_steps is not None and ga_steps > 1:
+            from transformers import __version__ as transformers_version
+            if Version(transformers_version) <= Version('4.45.2'):
+                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
+                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
+        if getattr(args, 'eval_strategy', 'no') != 'no':
+            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
+            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
+            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
+        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
+        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
+        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
+        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
+        if force_float32:
+            args.bf16_full_eval = False
+            args.fp16_full_eval = False
+        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
+            args.bf16_full_eval = True
+            args.fp16_full_eval = False
+        elif not bf16_full_eval and not fp16_full_eval:
+            args.bf16_full_eval = args.bf16
+            args.fp16_full_eval = args.fp16
+        _output_logits = False
+        if locals().get('compute_metrics', None) is not None: _output_logits = True
+        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
+        if _output_logits:
+            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
+        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
+            pass
+        else:
+            model_max_seq_length = getattr(model, 'max_seq_length', None)
+            args_max_seq_length  = getattr(args,  'max_seq_length', None)
+            if args_max_seq_length is None and model_max_seq_length is not None:
+                max_seq_length = model.max_seq_length
+                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
+        if model is not None and hasattr(model, 'for_training'):
+            model.for_training()
+        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
+        if 'processing_class' in locals():
+            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
+            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
+        __tokenizer = processing_class if 'processing_class' in locals() else tokenizer
+        from unsloth_zoo.vision_utils import UnslothVisionDataCollator
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
+                data_collator = DataCollatorForLanguageModeling(__tokenizer, mlm = False)
+            elif isinstance(data_collator, DataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
+                data_collator = DataCollatorForSeq2Seq(__tokenizer)
+        else:
+            if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
+            if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
+            if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
+                if isinstance(data_collator, DataCollatorForSeq2Seq):
+                    data_collator = DataCollatorForSeq2Seq(__tokenizer.tokenizer)
+                else:
+                    data_collator = DataCollatorForLanguageModeling(__tokenizer.tokenizer, mlm = False)
+        other_metrics = []
+        
+        from unsloth_zoo.logging_utils import PatchRLStatistics
+        PatchRLStatistics('orpo_trainer', other_metrics)
+        
+        super().__init__(
+            model = model,
+            args = args,
+            data_collator = data_collator,
+            train_dataset = train_dataset,
+            eval_dataset = eval_dataset,
+            processing_class = processing_class,
+            model_init = model_init,
+            callbacks = callbacks,
+            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
+            peft_config = peft_config,
+            compute_metrics = compute_metrics,**kwargs)
+        if hasattr(self, 'neftune_hook_handle'):
+            self.neftune_hook_handle.remove()
+            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
+        if getattr(args, 'neftune_noise_alpha', None) is not None:
+            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
+        pass
+        
+pass

unsloth_compiled_cache/UnslothOnlineDPOTrainer.py

@@ -0,0 +1,1263 @@
+"""
+2025.4.1
+2025.4.1
+4.51.3
+0.15.2
+__UNSLOTH_VERSIONING__
+"""
+from torch import Tensor
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from trl.trainer.online_dpo_trainer import (Any, BaseImageProcessor, BasePairwiseJudge, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalPrediction, F, FeatureExtractionMixin, GenerationConfig, IterableDataset, OnlineDPOConfig, OnlineDPOTrainer, OptimizerNames, Optional, PREFIX_CHECKPOINT_DIR, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SIMPLE_CHAT_TEMPLATE, Trainer, TrainerCallback, Union, apply_chat_template, create_reference_model, datasets, disable_dropout_in_model, empty_cache, generate_model_card, get_comet_experiment_url, get_reward, is_conversational, is_peft_available, is_wandb_available, jinja2, logging, maybe_apply_chat_template, nn, np, os, prepare_deepspeed, seed_worker, textwrap, torch, transformers, truncate_right, unwrap_model_for_generation, version, warnings, wraps, F, is_conversational, os, torch)
+
+
+import os
+from typing import *
+from dataclasses import dataclass, field
+from packaging.version import Version
+import torch
+import numpy as np
+from contextlib import nullcontext
+from torch.nn import functional as F
+from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
+
+torch_compile_options = {
+    "epilogue_fusion"   : True,
+    "max_autotune"      : False,
+    "shape_padding"     : True,
+    "trace.enabled"     : False,
+    "triton.cudagraphs" : False,
+}
+
+@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
+def selective_log_softmax(logits, index):
+    logits = logits.to(torch.float32)
+    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
+    # loop to reduce peak mem consumption
+    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
+    logsumexp_values = torch.logsumexp(logits, dim = -1)
+    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
+    return per_token_logps
+def vLLMSamplingParams(**kwargs):
+    from vllm import SamplingParams
+    sampling_params = SamplingParams(**kwargs)
+    sampling_params._set_kwargs = kwargs
+    return sampling_params
+@dataclass
+class UnslothOnlineDPOConfig(OnlineDPOConfig):
+    """
+    
+    Configuration class for the [`OnlineDPOTrainer`].
+
+    Using [`~transformers.HfArgumentParser`] we can turn this class into
+    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
+    command line.
+
+    Parameters:
+        learning_rate (`float`, *optional*, defaults to `5e-7`):
+            Initial learning rate for [`AdamW`] optimizer. The default value replaces that of
+            [`~transformers.TrainingArguments`].
+        reward_model_path (`str` or `None`, *optional*, defaults to `None`):
+            Path to the reward model. Either `judge` or `reward_model_path` must be set, but not both.
+        judge (`str` or `None`, *optional*, defaults to `None`):
+            Name of the judge to use. Either `judge` or `reward_model_path` must be set, but not both.
+        max_new_tokens (`int`, *optional*, defaults to `64`):
+            Maximum number of tokens to generate per completion.
+        max_length (`int`, *optional*, defaults to `256`):
+            Maximum total length of the sequence (prompt + completion) used to compute log probabilities. If the
+            sequence exceeds this limit, the leftmost tokens will be truncated to preserve as much of the completion as
+            possible.
+        temperature (`float`, *optional*, defaults to `0.9`):
+            Temperature for sampling. The higher the temperature, the more random the completions.
+        missing_eos_penalty (`float` or `None`, *optional*, defaults to `None`):
+            Penalty applied to the score when the model fails to generate an EOS token. This is useful to encourage
+            to generate completions shorter than the maximum length (`max_new_tokens`). The penalty must be a positive
+            value.
+        beta (`float` or `list[float]`, *optional*, defaults to `0.1`):
+            Parameter controlling the deviation from the reference model. Higher β means less deviation from the
+            reference model. For the IPO loss (`loss_type="ipo"`), β is the regularization parameter denoted by τ in
+            the [paper](https://huggingface.co/papers/2310.12036). If a list of floats is provided then the β is
+            selected for each new epoch and the last β is used for the rest of the epochs.
+        loss_type (`str`, *optional*, defaults to `"sigmoid"`):
+            Type of loss to use. Possible values are:
+
+                - `"sigmoid"`: sigmoid loss from the original [DPO](https://huggingface.co/papers/2305.18290) paper.
+                - `"ipo"`: IPO loss from the [IPO](https://huggingface.co/papers/2310.12036) paper.
+
+        dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
+            Number of processes to use for processing the dataset.
+        disable_dropout (`bool`, *optional*, defaults to `True`):
+            Whether to disable dropout in the model and reference model.
+        use_vllm (`bool`, *optional*, defaults to `False`):
+            Whether to use vLLM for generating completions. Requires vLLM to be installed (`pip install vllm`).
+        ds3_gather_for_generation (`bool`, *optional*, defaults to `True`):
+            This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation,
+            improving generation speed. However, disabling this option allows training models that exceed the VRAM
+            capacity of a single GPU, albeit at the cost of slower generation.
+    
+    """
+    vllm_sampling_params: Optional[Any] = field(
+        default = None,
+        metadata = {'help': 'vLLM SamplingParams'},
+    )
+    unsloth_num_chunks : Optional[int] = field(
+        default = -1,
+        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
+    )
+    def __init__(
+        self,
+        output_dir = None,
+        overwrite_output_dir = None,
+        do_train = False,
+        do_eval = False,
+        do_predict = False,
+        eval_strategy = 'no',
+        prediction_loss_only = False,
+        per_device_train_batch_size = 4,
+        per_device_eval_batch_size = 4,
+        per_gpu_train_batch_size = None,
+        per_gpu_eval_batch_size = None,
+        gradient_accumulation_steps = 2,
+        eval_accumulation_steps = 2,
+        eval_delay = 0,
+        torch_empty_cache_steps = 250,
+        learning_rate = 5e-05,
+        weight_decay = 0.01,
+        adam_beta1 = 0.9,
+        adam_beta2 = 0.999,
+        adam_epsilon = 1e-08,
+        max_grad_norm = 1.0,
+        num_train_epochs = 3.0,
+        max_steps = -1,
+        lr_scheduler_type = 'linear',
+        warmup_ratio = 0.1,
+        warmup_steps = 0,
+        log_level = 'passive',
+        log_level_replica = 'warning',
+        log_on_each_node = True,
+        logging_dir = None,
+        logging_strategy = 'steps',
+        logging_first_step = False,
+        logging_steps = 1,
+        logging_nan_inf_filter = False,
+        save_strategy = 'steps',
+        save_steps = 500,
+        save_total_limit = None,
+        save_safetensors = True,
+        save_on_each_node = False,
+        save_only_model = False,
+        restore_callback_states_from_checkpoint = False,
+        no_cuda = False,
+        use_cpu = False,
+        use_mps_device = False,
+        seed = 3407,
+        data_seed = 3407,
+        jit_mode_eval = False,
+        use_ipex = False,
+        bf16 = False,
+        fp16 = False,
+        fp16_opt_level = 'O1',
+        half_precision_backend = 'auto',
+        bf16_full_eval = False,
+        fp16_full_eval = False,
+        tf32 = None,
+        local_rank = -1,
+        ddp_backend = None,
+        tpu_num_cores = None,
+        tpu_metrics_debug = False,
+        debug = '',
+        dataloader_drop_last = False,
+        eval_steps = None,
+        dataloader_num_workers = 0,
+        dataloader_prefetch_factor = None,
+        past_index = -1,
+        run_name = None,
+        disable_tqdm = None,
+        remove_unused_columns = True,
+        label_names = None,
+        load_best_model_at_end = False,
+        metric_for_best_model = None,
+        greater_is_better = None,
+        ignore_data_skip = False,
+        fsdp = '',
+        fsdp_min_num_params = 0,
+        fsdp_config = None,
+        tp_size = 0,
+        fsdp_transformer_layer_cls_to_wrap = None,
+        accelerator_config = None,
+        deepspeed = None,
+        label_smoothing_factor = 0.0,
+        optim = 'adamw_8bit',
+        optim_args = None,
+        adafactor = False,
+        group_by_length = False,
+        length_column_name = 'length',
+        report_to = None,
+        ddp_find_unused_parameters = None,
+        ddp_bucket_cap_mb = None,
+        ddp_broadcast_buffers = None,
+        dataloader_pin_memory = True,
+        dataloader_persistent_workers = False,
+        skip_memory_metrics = True,
+        use_legacy_prediction_loop = False,
+        push_to_hub = False,
+        resume_from_checkpoint = None,
+        hub_model_id = None,
+        hub_strategy = 'every_save',
+        hub_token = None,
+        hub_private_repo = None,
+        hub_always_push = False,
+        gradient_checkpointing = False,
+        gradient_checkpointing_kwargs = None,
+        include_inputs_for_metrics = False,
+        eval_do_concat_batches = True,
+        fp16_backend = 'auto',
+        push_to_hub_model_id = None,
+        push_to_hub_organization = None,
+        push_to_hub_token = None,
+        mp_parameters = '',
+        auto_find_batch_size = False,
+        full_determinism = False,
+        torchdynamo = None,
+        ray_scope = 'last',
+        ddp_timeout = 1800,
+        torch_compile = False,
+        torch_compile_backend = None,
+        torch_compile_mode = None,
+        include_tokens_per_second = False,
+        include_num_input_tokens_seen = False,
+        neftune_noise_alpha = None,
+        optim_target_modules = None,
+        batch_eval_metrics = False,
+        eval_on_start = False,
+        use_liger_kernel = False,
+        eval_use_gather_object = False,
+        average_tokens_across_devices = False,
+        reward_model_path = None,
+        judge = None,
+        max_new_tokens = 64,
+        max_length = 512,
+        temperature = 0.9,
+        missing_eos_penalty = None,
+        loss_type = 'sigmoid',
+        dataset_num_proc = None,
+        disable_dropout = True,
+        use_vllm = False,
+        ds3_gather_for_generation = True,
+        vllm_sampling_params = None,
+        unsloth_num_chunks = -1,
+        **kwargs,
+    ):
+        if learning_rate < 1e-7: raise FloatingPointError(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
+        if learning_rate > 1: raise OverflowError(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
+        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
+            output_dir = 'unsloth_training_checkpoints'
+            save_strategy = 'no'
+        if dataset_num_proc is None:
+            from multiprocessing import cpu_count
+            dataset_num_proc = cpu_count()
+        
+        super().__init__(
+            output_dir = output_dir,
+            overwrite_output_dir = overwrite_output_dir,
+            do_train = do_train,
+            do_eval = do_eval,
+            do_predict = do_predict,
+            eval_strategy = eval_strategy,
+            prediction_loss_only = prediction_loss_only,
+            per_device_train_batch_size = per_device_train_batch_size,
+            per_device_eval_batch_size = per_device_eval_batch_size,
+            per_gpu_train_batch_size = per_gpu_train_batch_size,
+            per_gpu_eval_batch_size = per_gpu_eval_batch_size,
+            gradient_accumulation_steps = gradient_accumulation_steps,
+            eval_accumulation_steps = eval_accumulation_steps,
+            eval_delay = eval_delay,
+            torch_empty_cache_steps = torch_empty_cache_steps,
+            learning_rate = learning_rate,
+            weight_decay = weight_decay,
+            adam_beta1 = adam_beta1,
+            adam_beta2 = adam_beta2,
+            adam_epsilon = adam_epsilon,
+            max_grad_norm = max_grad_norm,
+            num_train_epochs = num_train_epochs,
+            max_steps = max_steps,
+            lr_scheduler_type = lr_scheduler_type,
+            warmup_ratio = warmup_ratio,
+            warmup_steps = warmup_steps,
+            log_level = log_level,
+            log_level_replica = log_level_replica,
+            log_on_each_node = log_on_each_node,
+            logging_dir = logging_dir,
+            logging_strategy = logging_strategy,
+            logging_first_step = logging_first_step,
+            logging_steps = logging_steps,
+            logging_nan_inf_filter = logging_nan_inf_filter,
+            save_strategy = save_strategy,
+            save_steps = save_steps,
+            save_total_limit = save_total_limit,
+            save_safetensors = save_safetensors,
+            save_on_each_node = save_on_each_node,
+            save_only_model = save_only_model,
+            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
+            no_cuda = no_cuda,
+            use_cpu = use_cpu,
+            use_mps_device = use_mps_device,
+            seed = seed,
+            data_seed = data_seed,
+            jit_mode_eval = jit_mode_eval,
+            use_ipex = use_ipex,
+            bf16 = bf16,
+            fp16 = fp16,
+            fp16_opt_level = fp16_opt_level,
+            half_precision_backend = half_precision_backend,
+            bf16_full_eval = bf16_full_eval,
+            fp16_full_eval = fp16_full_eval,
+            tf32 = tf32,
+            local_rank = local_rank,
+            ddp_backend = ddp_backend,
+            tpu_num_cores = tpu_num_cores,
+            tpu_metrics_debug = tpu_metrics_debug,
+            debug = debug,
+            dataloader_drop_last = dataloader_drop_last,
+            eval_steps = eval_steps,
+            dataloader_num_workers = dataloader_num_workers,
+            dataloader_prefetch_factor = dataloader_prefetch_factor,
+            past_index = past_index,
+            run_name = run_name,
+            disable_tqdm = disable_tqdm,
+            remove_unused_columns = remove_unused_columns,
+            label_names = label_names,
+            load_best_model_at_end = load_best_model_at_end,
+            metric_for_best_model = metric_for_best_model,
+            greater_is_better = greater_is_better,
+            ignore_data_skip = ignore_data_skip,
+            fsdp = fsdp,
+            fsdp_min_num_params = fsdp_min_num_params,
+            fsdp_config = fsdp_config,
+            tp_size = tp_size,
+            fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
+            accelerator_config = accelerator_config,
+            deepspeed = deepspeed,
+            label_smoothing_factor = label_smoothing_factor,
+            optim = optim,
+            optim_args = optim_args,
+            adafactor = adafactor,
+            group_by_length = group_by_length,
+            length_column_name = length_column_name,
+            report_to = report_to,
+            ddp_find_unused_parameters = ddp_find_unused_parameters,
+            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
+            ddp_broadcast_buffers = ddp_broadcast_buffers,
+            dataloader_pin_memory = dataloader_pin_memory,
+            dataloader_persistent_workers = dataloader_persistent_workers,
+            skip_memory_metrics = skip_memory_metrics,
+            use_legacy_prediction_loop = use_legacy_prediction_loop,
+            push_to_hub = push_to_hub,
+            resume_from_checkpoint = resume_from_checkpoint,
+            hub_model_id = hub_model_id,
+            hub_strategy = hub_strategy,
+            hub_token = hub_token,
+            hub_private_repo = hub_private_repo,
+            hub_always_push = hub_always_push,
+            gradient_checkpointing = gradient_checkpointing,
+            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
+            include_inputs_for_metrics = include_inputs_for_metrics,
+            eval_do_concat_batches = eval_do_concat_batches,
+            fp16_backend = fp16_backend,
+            push_to_hub_model_id = push_to_hub_model_id,
+            push_to_hub_organization = push_to_hub_organization,
+            push_to_hub_token = push_to_hub_token,
+            mp_parameters = mp_parameters,
+            auto_find_batch_size = auto_find_batch_size,
+            full_determinism = full_determinism,
+            torchdynamo = torchdynamo,
+            ray_scope = ray_scope,
+            ddp_timeout = ddp_timeout,
+            torch_compile = torch_compile,
+            torch_compile_backend = torch_compile_backend,
+            torch_compile_mode = torch_compile_mode,
+            include_tokens_per_second = include_tokens_per_second,
+            include_num_input_tokens_seen = include_num_input_tokens_seen,
+            neftune_noise_alpha = neftune_noise_alpha,
+            optim_target_modules = optim_target_modules,
+            batch_eval_metrics = batch_eval_metrics,
+            eval_on_start = eval_on_start,
+            use_liger_kernel = use_liger_kernel,
+            eval_use_gather_object = eval_use_gather_object,
+            average_tokens_across_devices = average_tokens_across_devices,
+            reward_model_path = reward_model_path,
+            judge = judge,
+            max_new_tokens = max_new_tokens,
+            max_length = max_length,
+            temperature = temperature,
+            missing_eos_penalty = missing_eos_penalty,
+            loss_type = loss_type,
+            dataset_num_proc = dataset_num_proc,
+            disable_dropout = disable_dropout,
+            use_vllm = use_vllm,
+            ds3_gather_for_generation = ds3_gather_for_generation,**kwargs)
+        self.vllm_sampling_params = vllm_sampling_params
+        self.unsloth_num_chunks = unsloth_num_chunks
+pass
+
+class _UnslothOnlineDPOTrainer(Trainer):
+    r""""""
+
+    _tag_names = ["trl", "online-dpo"]
+
+    def __init__(
+        self,
+        model: Union[PreTrainedModel, nn.Module],
+        ref_model: Union[PreTrainedModel, nn.Module, None] = None,
+        reward_model: Union[PreTrainedModel, nn.Module, None] = None,
+        judge: Optional[BasePairwiseJudge] = None,
+        args: Optional[OnlineDPOConfig] = None,
+        data_collator: Optional[DataCollator] = None,
+        train_dataset: Optional[Union[Dataset, IterableDataset, "datasets.Dataset"]] = None,
+        eval_dataset: Optional[Union[Dataset, dict[str, Dataset], "datasets.Dataset"]] = None,
+        processing_class: Optional[
+            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
+        ] = None,
+        reward_processing_class: Optional[PreTrainedTokenizerBase] = None,
+        peft_config: Optional[dict] = None,
+        compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
+        callbacks: Optional[list[TrainerCallback]] = None,
+        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
+        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+    ) -> None:
+
+        if hasattr(model, 'vllm_engine') and hasattr(args, 'use_vllm') and (getattr(args, 'use_vllm', False) == False): args.use_vllm = True
+        if ref_model is model:
+            raise ValueError(
+                "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the "
+                "same as `model`, either omit the `ref_model` argument or pass `None`."
+            )
+
+        self.ref_model = ref_model
+
+        if reward_model is not None and judge is not None:
+            warnings.warn(
+                "Both `reward_model` and `judge` are provided. Please choose provide only one of them. "
+                "Ignoring `judge` and using `reward_model`.",
+                UserWarning,
+            )
+            judge = None
+        elif reward_model is None and judge is None:
+            raise ValueError("Either `reward_model` or `judge` must be provided.")
+
+        self.reward_model = reward_model
+        self.reward_processing_class = reward_processing_class
+        self.judge = judge
+
+        if args.missing_eos_penalty is not None and judge is not None:
+            raise ValueError("`missing_eos_penalty` is not supported when `judge` is provided.")
+
+        if args is None:
+            raise ValueError("`args` must be provided.")
+
+        # Check that the processing_class is provided
+        if processing_class is None:
+            raise ValueError("`processing_class` must be provided.")
+
+        # Convert to PEFT model if peft_config is provided
+        if False:
+            # Check if PEFT is available
+            if not is_peft_available():
+                raise ImportError(
+                    "PEFT is not available and passed `peft_config`. Please install PEFT with "
+                    "`pip install peft` to use it."
+                )
+
+            # If the model is already a PeftModel, we need to merge and unload it.
+            # Further information here: https://huggingface.co/docs/trl/dpo_trainer#reference-model-considerations-with-peft
+            if isinstance(model, PeftModel):
+                model = model.merge_and_unload()
+
+            # Get peft model with the given config
+            model = model
+
+        # Disable dropout in the model and reference model
+        if args.disable_dropout:
+            disable_dropout_in_model(model)
+            if self.ref_model is not None:
+                disable_dropout_in_model(self.ref_model)
+
+        # Handle the ref_model
+        # Usually, the user wants the ref model to be the initial version of the model. When using PEFT, it's easy to
+        # get the ref model, as it's just the model with a disabled adapter. When not using PEFT, we need to create
+        # the ref model from the model by copying it and disable the gradients and set it in evaluation mode.
+        if ref_model is None:  # No ref model provided, the most common case
+            if False:
+                self.ref_model = create_reference_model(model)  # copy, disable gradients, set eval mode
+            else:
+                self.ref_model = None  # we don't need a ref model here, we can just disable the adapter.
+        else:  # rare case, the user provided a ref model
+            self.ref_model = ref_model
+            self.ref_model.eval()
+
+        # Disable the gradient and set the reward model in eval mode
+        if self.reward_model is not None:
+            self.reward_model.eval()
+
+        # Define the collator is not provided
+        if data_collator is None:
+            data_collator = DPODataCollatorWithPadding(pad_token_id=processing_class.pad_token_id)
+
+        self.max_length = args.max_length
+
+        self.stats = {
+            "objective/kl": [],
+            "objective/entropy": [],
+            "objective/non_score_reward": [],
+            "rewards/chosen": [],
+            "rewards/rejected": [],
+            "rewards/accuracies": [],
+            "rewards/margins": [],
+            "logps/chosen": [],
+            "logps/rejected": [],
+            "val/contain_eos_token": [],
+            "beta": [],
+        }
+        if self.reward_model is not None:
+            self.stats["objective/rlhf_reward"] = []
+            self.stats["objective/scores_margin"] = []
+            self.stats["objective/scores"] = []
+
+        if args.use_vllm:
+            self.llm = model.vllm_engine; self._last_loaded_step = 0; self.generation_config = SamplingParams(
+                n=2,                  max_tokens=args.max_new_tokens,
+                temperature=args.temperature,
+                top_k=50,
+                top_p=1.0,
+                detokenize=False,**getattr(getattr(args, 'vllm_sampling_params', vLLMSamplingParams()), '_set_kwargs', {}),)
+        else:
+            self.generation_config = GenerationConfig(
+                max_new_tokens=args.max_new_tokens,
+                temperature=args.temperature,
+                top_k=50,
+                top_p=1.0,
+                do_sample=True,
+                use_cache=False if args.gradient_checkpointing else True,
+            )
+
+        # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
+        # input tensor associated with the key "input_ids". However, in Online DPO, the sampled data does not include
+        # the "input_ids" key. As a result, the trainer issues the warning: "Could not estimate the number of tokens
+        # of the input, floating-point operations will not be computed." To suppress this warning, we set the
+        # "estimate_tokens" key in the model's "warnings_issued" dictionary to True. This acts as a flag to indicate
+        # that the warning has already been issued.
+        model.warnings_issued["estimate_tokens"] = True
+
+        super().__init__(
+            model=model,
+            args=args,
+            data_collator=data_collator,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            processing_class=processing_class,
+            compute_metrics=compute_metrics,
+            callbacks=callbacks,
+            optimizers=optimizers,
+            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+        )
+
+        # Add tags for models that have been loaded with the correct transformers version
+        if hasattr(self.model, "add_model_tags"):
+            self.model.add_model_tags(self._tag_names)
+
+        self._beta = args.beta
+
+        # Placed after the super().__init__ because we need self.is_deepspeed_enabled and self.accelerator
+        if self.is_deepspeed_enabled:
+            if self.reward_model is not None:
+                self.reward_model = prepare_deepspeed(
+                    self.reward_model, args.per_device_train_batch_size, args.fp16, args.bf16
+                )
+            if self.ref_model is not None:
+                self.ref_model = prepare_deepspeed(
+                    self.ref_model, args.per_device_train_batch_size, args.fp16, args.bf16
+                )
+        else:
+            if self.ref_model is not None:
+                self.ref_model = self.ref_model.to(self.accelerator.device)
+            if self.reward_model is not None:
+                self.reward_model = self.reward_model.to(self.accelerator.device)
+
+    @property
+    def beta(self):
+        if isinstance(self._beta, list):
+            epoch = self.state.epoch
+            return self._beta[epoch] if epoch < len(self._beta) else self._beta[-1]
+        else:
+            return self._beta
+
+    @staticmethod
+    def tokenize_row(feature, is_encoder_decoder: bool, tokenizer: PreTrainedTokenizerBase) -> dict[str, Any]:
+        """Tokenize a single row from a DPO specific dataset."""
+        if not is_encoder_decoder:
+            batch = tokenizer(feature["prompt"], add_special_tokens=False)
+            # Add BOS token to head of prompt. Avoid adding if it's already there
+            if tokenizer.bos_token_id is not None:
+                prompt_len_input_ids = len(batch["input_ids"])
+                if prompt_len_input_ids == 0 or tokenizer.bos_token_id != batch["input_ids"][0]:
+                    batch["input_ids"] = [tokenizer.bos_token_id] + batch["input_ids"]
+                    batch["attention_mask"] = [1] + batch["attention_mask"]
+        else:
+            batch = tokenizer(feature["prompt"], add_special_tokens=True)
+        batch = {f"prompt_{key}": value for key, value in batch.items()}
+        return batch
+
+    # Same as Trainer.get_train_dataloader but skip the "remove_unused_columns".
+    @wraps(Trainer.get_train_dataloader)
+    def get_train_dataloader(self) -> DataLoader:
+        if self.train_dataset is None:
+            raise ValueError("Trainer: training requires a train_dataset.")
+
+        train_dataset = self.train_dataset
+        data_collator = self.data_collator
+        dataloader_params = {
+            "batch_size": self._train_batch_size,
+            "collate_fn": data_collator,
+            "num_workers": self.args.dataloader_num_workers,
+            "pin_memory": self.args.dataloader_pin_memory,
+            "persistent_workers": self.args.dataloader_persistent_workers,
+        }
+
+        if not isinstance(train_dataset, torch.utils.data.IterableDataset):
+            dataloader_params["sampler"] = self._get_train_sampler()
+            dataloader_params["drop_last"] = self.args.dataloader_drop_last
+            dataloader_params["worker_init_fn"] = seed_worker
+            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
+
+        return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params))
+
+    # Same as Trainer.get_eval_dataloader but skip the "remove_unused_columns".
+    @wraps(Trainer.get_eval_dataloader)
+    def get_eval_dataloader(self, eval_dataset: Optional[Union[str, Dataset]] = None) -> DataLoader:
+        if eval_dataset is None and self.eval_dataset is None:
+            raise ValueError("Trainer: evaluation requires an eval_dataset.")
+
+        # If we have persistent workers, don't do a fork bomb especially as eval datasets
+        # don't change during training
+        dataloader_key = eval_dataset if isinstance(eval_dataset, str) else "eval"
+        if (
+            hasattr(self, "_eval_dataloaders")
+            and dataloader_key in self._eval_dataloaders
+            and self.args.dataloader_persistent_workers
+        ):
+            return self.accelerator.prepare(self._eval_dataloaders[dataloader_key])
+
+        eval_dataset = (
+            self.eval_dataset[eval_dataset]
+            if isinstance(eval_dataset, str)
+            else eval_dataset
+            if eval_dataset is not None
+            else self.eval_dataset
+        )
+        data_collator = self.data_collator
+
+        dataloader_params = {
+            "batch_size": self.args.eval_batch_size,
+            "collate_fn": data_collator,
+            "num_workers": self.args.dataloader_num_workers,
+            "pin_memory": self.args.dataloader_pin_memory,
+            "persistent_workers": self.args.dataloader_persistent_workers,
+        }
+
+        if not isinstance(eval_dataset, torch.utils.data.IterableDataset):
+            dataloader_params["sampler"] = self._get_eval_sampler(eval_dataset)
+            dataloader_params["drop_last"] = self.args.dataloader_drop_last
+            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
+
+        # accelerator.free_memory() will destroy the references, so
+        # we need to store the non-prepared version
+        eval_dataloader = DataLoader(eval_dataset, **dataloader_params)
+        if self.args.dataloader_persistent_workers:
+            if hasattr(self, "_eval_dataloaders"):
+                self._eval_dataloaders[dataloader_key] = eval_dataloader
+            else:
+                self._eval_dataloaders = {dataloader_key: eval_dataloader}
+
+        return self.accelerator.prepare(eval_dataloader)
+
+    def _generate_vllm(self, model, prompts):
+        eos_token_id = self.processing_class.eos_token_id
+        pad_token_id = self.processing_class.pad_token_id
+
+        # Load the latest weights
+
+        pass
+
+        pass
+
+        if is_conversational({"prompt": prompts[0]}):
+            outputs = self.llm.chat(prompts, self.generation_config, use_tqdm=False, lora_request = self.model.load_lora('online_dpo_trainer_lora_model', load_tensors = True))
+        else:
+            outputs = self.llm.generate(prompts, self.generation_config, use_tqdm=False, lora_request = self.model.load_lora('online_dpo_trainer_lora_model', load_tensors = True))
+
+        completion_ids = [list(output.outputs[i].token_ids) for i in range(2) for output in outputs]
+        prompt_ids = [list(output.prompt_token_ids) for _ in range(2) for output in outputs]
+
+        # Create mask and pad the prompt and completion
+        max_prompt_length = max(len(ids) for ids in prompt_ids)
+        prompt_mask = [[0] * (max_prompt_length - len(ids)) + [1] * len(ids) for ids in prompt_ids]
+        prompt_ids = [[pad_token_id] * (max_prompt_length - len(ids)) + ids for ids in prompt_ids]
+        max_tokens = self.generation_config.max_tokens
+        completion_mask = [[1] * len(ids) + [0] * (max_tokens - len(ids)) for ids in completion_ids]
+        completion_ids = [
+            ids + [eos_token_id] if ids[-1] != eos_token_id and len(ids) < max_tokens else ids
+            for ids in completion_ids
+        ]
+        completion_ids = [ids + [pad_token_id] * (max_tokens - len(ids)) for ids in completion_ids]
+
+        # Convert to tensors
+        prompt_ids = torch.tensor(prompt_ids, device=self.accelerator.device)
+        prompt_mask = torch.tensor(prompt_mask, device=self.accelerator.device)
+        completion_ids = torch.tensor(completion_ids, device=self.accelerator.device)
+        completion_mask = torch.tensor(completion_mask, device=self.accelerator.device)
+
+        return prompt_ids, prompt_mask, completion_ids, completion_mask
+
+    def _generate(self, model, prompts):
+        eos_token_id = self.processing_class.eos_token_id
+        pad_token_id = self.processing_class.pad_token_id
+
+        # Apply chat template and tokenize the input. We do this on-the-fly to enable the use of reward models and
+        # policies with different tokenizers / chat templates.
+        inputs = [{"prompt": prompt} for prompt in prompts]
+        inputs = [maybe_apply_chat_template(x, self.processing_class) for x in inputs]
+        inputs = [self.tokenize_row(x, model.config.is_encoder_decoder, self.processing_class) for x in inputs]
+        inputs = self.data_collator(inputs)
+
+        # Sample 2 completions per prompt of size `max_new_tokens` from the model
+        inputs = self._prepare_inputs(inputs)
+        prompt_ids = inputs["prompt_input_ids"].repeat(2, 1)
+        prompt_mask = inputs["prompt_attention_mask"].repeat(2, 1)
+        with unwrap_model_for_generation(
+            model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
+        ) as unwrapped_model:
+            output = unwrapped_model.generate(
+                input_ids=prompt_ids,
+                attention_mask=prompt_mask,
+                generation_config=self.generation_config,
+            )
+
+        completion_ids = output[:, prompt_ids.size(1) :]
+        completion_ids, completion_mask = truncate_right(completion_ids, eos_token_id, pad_token_id)
+
+        return prompt_ids, prompt_mask, completion_ids, completion_mask
+
+    def _forward(self, model, prompt_ids, prompt_mask, completion_ids, completion_mask):
+        # Get the number of tokens to truncate from prompt
+        num_tokens_to_truncate = max(prompt_ids.size(1) + completion_ids.size(1) - self.max_length, 0)
+
+        # Truncate left to avoid oom
+        prompt_ids = prompt_ids[:, num_tokens_to_truncate:]
+        prompt_mask = prompt_mask[:, num_tokens_to_truncate:]
+
+        # Concat the prompt and completion
+        prompt_completion_ids = torch.cat((prompt_ids, completion_ids), dim=1)
+        prompt_completion_mask = torch.cat((prompt_mask, completion_mask), dim=1)
+
+        # Get the logprobs of the completions from the model
+        output = model(prompt_completion_ids, attention_mask=prompt_completion_mask)
+
+        # There is 1 offset, because the model predict the next token
+        logits = output.logits[:, prompt_ids.size(1) - 1 : -1]
+
+        # Take the completion tokens logprob
+        logprobs = torch.take_along_dim(logits.log_softmax(dim=-1), completion_ids.unsqueeze(-1), dim=2).squeeze(-1)
+        return logprobs
+
+    def training_step(
+        self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None
+    ) -> torch.Tensor:
+        model.train()
+
+        prompts = inputs["prompt"]
+        batch_size = len(prompts)
+
+        if self.args.use_vllm:
+            prompt_ids, prompt_mask, completion_ids, completion_mask = self._generate_vllm(model, prompts)
+        else:
+            prompt_ids, prompt_mask, completion_ids, completion_mask = self._generate(model, prompts)
+
+        contain_eos_token = torch.any(completion_ids == self.processing_class.eos_token_id, dim=-1)
+
+        logprobs = self._forward(model, prompt_ids, prompt_mask, completion_ids, completion_mask)
+        with torch.no_grad():
+            if self.ref_model is not None:
+                ref_logprobs = self._forward(self.ref_model, prompt_ids, prompt_mask, completion_ids, completion_mask)
+            else:  # peft case: we just need to disable the adapter
+                with self.model.disable_adapter():
+                    ref_logprobs = self._forward(self.model, prompt_ids, prompt_mask, completion_ids, completion_mask)
+
+        # Decode the completions, and format them if the input is conversational
+        device = logprobs.device
+        completions = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True)
+        if is_conversational({"prompt": prompts[0]}):
+            completions = [[{"role": "assistant", "content": completion}] for completion in completions]
+
+        # Get the reward from the reward model or judge
+        if self.judge is not None:
+            # Once formatted, conversational data may contain special tokens (such as <|im_start|>) that are not
+            # directly understandable by the judge and could alter its judgment. To avoid this and make the judge
+            # independent of the model's chat template, we use the raw conversation data, and apply our own chat
+            # template to it.
+            if is_conversational({"prompt": prompts[0]}):
+                environment = jinja2.Environment()
+                template = environment.from_string(SIMPLE_CHAT_TEMPLATE)
+                prompts = [template.render(messages=prompt) for prompt in prompts]
+                completions = [template.render(messages=completion) for completion in completions]
+
+            ranks_of_first_completion = self.judge.judge(
+                prompts, list(zip(completions[:batch_size], completions[batch_size:]))
+            )
+
+            # convert ranks to a True/False mask:
+            # when rank == 0, it means the first completion is the best
+            # when rank == 1, it means the second completion is the best
+            mask = torch.tensor([rank == 0 for rank in ranks_of_first_completion], device=device)
+        else:
+            # The reward model may not have the same chat template or tokenizer as the model, so we need to use the
+            # raw data (string), apply the chat template (if needed), and tokenize it with the reward processing class.
+            prompts = 2 * prompts  # repeat the prompt: [prompt0, prompt1] -> [prompt0, prompt1, prompt0, prompt1]
+            if is_conversational({"prompt": prompts[0]}):
+                examples = [{"prompt": p, "completion": c} for p, c in zip(prompts, completions)]
+                examples = [apply_chat_template(example, self.reward_processing_class) for example in examples]
+                prompts = [example["prompt"] for example in examples]
+                completions = [example["completion"] for example in examples]
+
+            # Tokenize the prompts
+            prompts_ids = self.reward_processing_class(
+                prompts, padding=True, return_tensors="pt", padding_side="left"
+            )["input_ids"].to(device)
+            context_length = prompts_ids.shape[1]
+
+            # Tokenize the completions
+            completions_ids = self.reward_processing_class(
+                completions, padding=True, return_tensors="pt", padding_side="right"
+            )["input_ids"].to(device)
+
+            # Concatenate the prompts and completions and get the reward
+            prompt_completion_ids = torch.cat((prompts_ids, completions_ids), dim=1)
+            with torch.inference_mode():
+                _, scores, _ = get_reward(
+                    self.reward_model, prompt_completion_ids, self.reward_processing_class.pad_token_id, context_length
+                )
+
+                # Filter completion. Ensure that the sample contains stop_token_id
+                # Completions not passing that filter will receive a lower score.
+                if self.args.missing_eos_penalty is not None:
+                    scores[~contain_eos_token] -= self.args.missing_eos_penalty
+
+            # Split the scores in 2 (the prompts of the first half are the same as the second half)
+            first_half, second_half = scores.split(batch_size)
+
+            # Get the indices of the chosen and rejected examples
+            mask = first_half >= second_half
+
+        batch_range = torch.arange(batch_size, device=device)
+        chosen_indices = batch_range + (~mask * batch_size)
+        rejected_indices = batch_range + (mask * batch_size)
+
+        # Build tensor so that the first half is the chosen examples and the second half the rejected examples
+        cr_indices = torch.cat((chosen_indices, rejected_indices), dim=0)  # cr = chosen and rejected
+        cr_logprobs = logprobs[cr_indices]
+        cr_ref_logprobs = ref_logprobs[cr_indices]
+
+        # mask out the padding tokens
+        padding_mask = ~completion_mask.bool()
+        cr_padding_mask = padding_mask[cr_indices]
+
+        cr_logprobs_sum = (cr_logprobs * ~cr_padding_mask).sum(1)
+        cr_ref_logprobs_sum = (cr_ref_logprobs * ~cr_padding_mask).sum(1)
+
+        # Split the chosen and rejected examples
+        chosen_logprobs_sum, rejected_logprobs_sum = torch.split(cr_logprobs_sum, batch_size)
+        chosen_ref_logprobs_sum, rejected_ref_logprobs_sum = torch.split(cr_ref_logprobs_sum, batch_size)
+        pi_logratios = chosen_logprobs_sum - rejected_logprobs_sum
+        ref_logratios = chosen_ref_logprobs_sum - rejected_ref_logprobs_sum
+
+        logits = pi_logratios - ref_logratios
+
+        if self.args.loss_type == "sigmoid":
+            losses = -F.logsigmoid(self.beta * logits)
+        elif self.args.loss_type == "ipo":
+            losses = (logits - 1 / (2 * self.beta)) ** 2
+        else:
+            raise NotImplementedError(f"invalid loss type {self.loss_type}")
+
+        loss = losses.mean()
+
+        # Log everything
+        if self.reward_model is not None:
+            scores_margin = scores[chosen_indices] - scores[rejected_indices]
+            self.stats["objective/scores_margin"].append(
+                self.accelerator.gather_for_metrics(scores_margin.mean()).mean().item()
+            )
+            self.stats["objective/scores"].append(self.accelerator.gather_for_metrics(scores.mean()).mean().item())
+        self.stats["val/contain_eos_token"].append(contain_eos_token.float().mean().item())
+        self.stats["logps/chosen"].append(self.accelerator.gather_for_metrics(chosen_logprobs_sum).mean().item())
+        self.stats["logps/rejected"].append(self.accelerator.gather_for_metrics(rejected_logprobs_sum).mean().item())
+
+        kl = logprobs - ref_logprobs
+        mean_kl = kl.sum(1).mean()
+        self.stats["objective/kl"].append(self.accelerator.gather_for_metrics(mean_kl).mean().item())
+        non_score_reward = (-self.beta * kl).sum(1)
+        mean_non_score_reward = non_score_reward.mean()
+        self.stats["objective/non_score_reward"].append(
+            self.accelerator.gather_for_metrics(mean_non_score_reward).mean().item()
+        )
+        if self.reward_model is not None:
+            rlhf_reward = scores + non_score_reward
+            self.stats["objective/rlhf_reward"].append(self.accelerator.gather_for_metrics(rlhf_reward).mean().item())
+        mean_entropy = -logprobs.sum(1).mean()
+        self.stats["objective/entropy"].append(self.accelerator.gather_for_metrics(mean_entropy).mean().item())
+        chosen_rewards = self.beta * (chosen_logprobs_sum - chosen_ref_logprobs_sum)
+        gathered_chosen_rewards = self.accelerator.gather_for_metrics(chosen_rewards)
+        self.stats["rewards/chosen"].append(gathered_chosen_rewards.mean().item())
+        rejected_rewards = self.beta * (rejected_logprobs_sum - rejected_ref_logprobs_sum)
+        gathered_rejected_rewards = self.accelerator.gather_for_metrics(rejected_rewards)
+        self.stats["rewards/rejected"].append(gathered_rejected_rewards.mean().item())
+        margin = gathered_chosen_rewards - gathered_rejected_rewards
+        self.stats["rewards/margins"].append(margin.mean().item())
+        accuracy = margin > 0
+        self.stats["rewards/accuracies"].append(accuracy.float().mean().item())
+        self.stats["beta"].append(self.beta)
+
+        if (
+            self.args.torch_empty_cache_steps is not None
+            and self.state.global_step % self.args.torch_empty_cache_steps == 0
+        ):
+            empty_cache()
+
+        kwargs = {}
+
+        # For LOMO optimizers you need to explicitly use the learnign rate
+        if self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]:
+            kwargs["learning_rate"] = self._get_learning_rate()
+
+        if self.args.n_gpu > 1:
+            loss = loss.mean()  # mean() to average on multi-gpu parallel training
+
+        if self.use_apex:
+            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
+                scaled_loss.backward()
+        else:
+            self.accelerator.backward(loss, **kwargs)
+
+        return loss.detach() / self.args.gradient_accumulation_steps
+
+    # Same as Trainer._maybe_log_save_evaluate but log our metrics
+    # start_time defaults to None to allow compatibility with transformers<=4.46
+    def _maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time=None):
+        if self.control.should_log and self.state.global_step > self._globalstep_last_logged:
+            logs: dict[str, float] = {}
+
+            # all_gather + mean() to get average loss over all processes
+            tr_loss_scalar = self._nested_gather(tr_loss).mean().item()
+
+            # reset tr_loss to zero
+            tr_loss -= tr_loss
+
+            logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
+            if grad_norm is not None:
+                logs["grad_norm"] = grad_norm.detach().item() if isinstance(grad_norm, torch.Tensor) else grad_norm
+            logs["learning_rate"] = self._get_learning_rate()
+
+            # Add our metrics
+            for key, val in self.stats.items():
+                logs[key] = sum(val) / len(val)
+            self.stats = {key: [] for key in self.stats}  # reset stats
+
+            self._total_loss_scalar += tr_loss_scalar
+            self._globalstep_last_logged = self.state.global_step
+            self.store_flos()
+
+            if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
+                self.log(logs, start_time)
+            else:  # transformers<=4.46
+                self.log(logs)
+
+        metrics = None
+        if self.control.should_evaluate:
+            metrics = self._evaluate(trial, ignore_keys_for_eval)
+            is_new_best_metric = self._determine_best_metric(metrics=metrics, trial=trial)
+
+            if self.args.save_strategy == "best":
+                self.control.should_save = is_new_best_metric
+
+        if self.control.should_save:
+            self._save_checkpoint(model, trial)
+            self.control = self.callback_handler.on_save(self.args, self.state, self.control)
+
+    # Copy-pasted from transformers.Trainer to maintain compatibility with earlier versions.
+    # This can be removed once the minimum transformers version is updated to 4.47.
+    # Refer to https://github.com/huggingface/trl/pull/2288 for more details.
+    def _determine_best_metric(self, metrics, trial):
+        """
+        Determine if the model should be saved based on the evaluation metrics.
+        If args.metric_for_best_model is not set, the loss is used.
+        Returns:
+            bool: True if a new best metric was found, else False
+        """
+        is_new_best_metric = False
+
+        if self.args.metric_for_best_model is not None:
+            metric_to_check = self.args.metric_for_best_model
+
+            if not metric_to_check.startswith("eval_"):
+                metric_to_check = f"eval_{metric_to_check}"
+
+            try:
+                metric_value = metrics[metric_to_check]
+            except KeyError as exc:
+                raise KeyError(
+                    f"The `metric_for_best_model` training argument is set to '{metric_to_check}', which is not found in the evaluation metrics. "
+                    f"The available evaluation metrics are: {list(metrics.keys())}. Consider changing the `metric_for_best_model` via the TrainingArguments."
+                ) from exc
+
+            operator = np.greater if self.args.greater_is_better else np.less
+
+            if self.state.best_metric is None:
+                self.state.best_metric = float("-inf") if self.args.greater_is_better else float("inf")
+
+            if operator(metric_value, self.state.best_metric):
+                run_dir = self._get_output_dir(trial=trial)
+                checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
+                output_dir = os.path.join(run_dir, checkpoint_folder)
+                self.state.best_metric = metric_value
+                self.state.best_model_checkpoint = output_dir
+
+                is_new_best_metric = True
+
+        return is_new_best_metric
+
+    def create_model_card(
+        self,
+        model_name: Optional[str] = None,
+        dataset_name: Optional[str] = None,
+        tags: Union[str, list[str], None] = None,
+    ):
+        """
+        Creates a draft of a model card using the information available to the `Trainer`.
+
+        Args:
+            model_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the model.
+            dataset_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the dataset used for training.
+            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
+                Tags to be associated with the model card.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
+            base_model = self.model.config._name_or_path
+        else:
+            base_model = None
+
+        tags = tags or []
+        if isinstance(tags, str):
+            tags = [tags]
+
+        if hasattr(self.model.config, "unsloth_version"):
+            tags.append("unsloth")
+
+        citation = textwrap.dedent("""\
+        @article{guo2024direct,
+            title        = {{Direct Language Model Alignment from Online AI Feedback}},
+            author       = {Shangmin Guo and Biao Zhang and Tianlin Liu and Tianqi Liu and Misha Khalman and Felipe Llinares and Alexandre Ram{\'{e}} and Thomas Mesnard and Yao Zhao and Bilal Piot and Johan Ferret and Mathieu Blondel},
+            year         = 2024,
+            eprint       = {arXiv:2402.04792}
+        }""")
+
+        model_card = generate_model_card(
+            base_model=base_model,
+            model_name=model_name,
+            hub_model_id=self.hub_model_id,
+            dataset_name=dataset_name,
+            tags=tags,
+            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
+            comet_url=get_comet_experiment_url(),
+            trainer_name="Online DPO",
+            trainer_citation=citation,
+            paper_title="Direct Language Model Alignment from Online AI Feedback",
+            paper_id="2402.04792",
+        )
+        model_card.save(os.path.join(self.args.output_dir, "README.md"))
+class UnslothOnlineDPOTrainer(_UnslothOnlineDPOTrainer):
+    """
+    
+    Initialize OnlineDPOTrainer.
+
+    Args:
+        model (`transformers.PreTrainedModel` or `torch.nn.Module`):
+            The model to train, preferably an `AutoModelForCausalLM`.
+        ref_model (`transformers.PreTrainedModel` or `torch.nn.Module` or `None`):
+            The reference model to use for training. If None is specified, the reference model will be created from
+            the model.
+        reward_model (`transformers.PreTrainedModel` or `torch.nn.Module` or `None`):
+            The reward model to score completions with, preferably an `AutoModelForSequenceClassification`.
+        judge (`BasePairwiseJudge`):
+            The judge to use for pairwise comparison of model completions.
+        args (`OnlineDPOConfig`):
+            The online DPO config arguments to use for training.
+        data_collator (`transformers.DataCollator`):
+            The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used
+            which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
+        train_dataset (`datasets.Dataset`):
+            The dataset to use for training.
+        eval_dataset (`datasets.Dataset`):
+            The dataset to use for evaluation.
+        processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
+            Processing class used to process the data. If provided, will be used to automatically process the inputs
+            for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
+            reuse the fine-tuned model.
+        peft_config (`dict`):
+            The peft config to use for training.
+        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
+            The function to use to compute the metrics. Must take a `EvalPrediction` and return
+            a dictionary string to metric values.
+        callbacks (`list[transformers.TrainerCallback]`):
+            The callbacks to use for training.
+        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
+            The optimizer and scheduler to use for training.
+        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
+            The function to use to preprocess the logits before computing the metrics.
+    
+    """
+    def __init__(
+        self,
+        model,
+        ref_model = None,
+        reward_model = None,
+        judge = None,
+        args = None,
+        data_collator = None,
+        train_dataset = None,
+        eval_dataset = None,
+        processing_class = None,
+        reward_processing_class = None,
+        peft_config = None,
+        compute_metrics = None,
+        callbacks = None,
+        preprocess_logits_for_metrics = None,
+        **kwargs
+    ):
+        if args is None: args = UnslothOnlineDPOConfig()
+        use_bf16 = getattr(args, 'bf16', False)
+        use_fp16 = getattr(args, 'fp16', False)
+        force_float32 = False
+        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':
+            print('Unsloth: Switching to float32 training since model cannot work with float16')
+            force_float32 = True
+        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
+        dtype = getattr(model.config, 'torch_dtype', None)
+        if dtype is None: dtype = model.get_input_embeddings().dtype
+        from unsloth_zoo.utils import _get_dtype
+        dtype = _get_dtype(dtype)
+        float16 = dtype == torch.float16
+        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
+        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
+        if force_float32:
+            args.fp16 = False
+            args.bf16 = False
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
+        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
+            args.fp16 = float16
+            args.bf16 = not float16
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
+        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
+            args.eval_strategy = 'steps'
+            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
+        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
+        if ga_steps is not None and ga_steps > 1:
+            from transformers import __version__ as transformers_version
+            if Version(transformers_version) <= Version('4.45.2'):
+                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
+                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
+        if getattr(args, 'eval_strategy', 'no') != 'no':
+            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
+            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
+            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
+        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
+        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
+        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
+        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
+        if force_float32:
+            args.bf16_full_eval = False
+            args.fp16_full_eval = False
+        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
+            args.bf16_full_eval = True
+            args.fp16_full_eval = False
+        elif not bf16_full_eval and not fp16_full_eval:
+            args.bf16_full_eval = args.bf16
+            args.fp16_full_eval = args.fp16
+        _output_logits = False
+        if locals().get('compute_metrics', None) is not None: _output_logits = True
+        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
+        if _output_logits:
+            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
+        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
+            pass
+        else:
+            model_max_seq_length = getattr(model, 'max_seq_length', None)
+            args_max_seq_length  = getattr(args,  'max_seq_length', None)
+            if args_max_seq_length is None and model_max_seq_length is not None:
+                max_seq_length = model.max_seq_length
+                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
+        if model is not None and hasattr(model, 'for_training'):
+            model.for_training()
+        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
+        if 'processing_class' in locals():
+            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
+            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
+        __tokenizer = processing_class if 'processing_class' in locals() else tokenizer
+        from unsloth_zoo.vision_utils import UnslothVisionDataCollator
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
+                data_collator = DataCollatorForLanguageModeling(__tokenizer, mlm = False)
+            elif isinstance(data_collator, DataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
+                data_collator = DataCollatorForSeq2Seq(__tokenizer)
+        else:
+            if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
+            if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
+            if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
+                if isinstance(data_collator, DataCollatorForSeq2Seq):
+                    data_collator = DataCollatorForSeq2Seq(__tokenizer.tokenizer)
+                else:
+                    data_collator = DataCollatorForLanguageModeling(__tokenizer.tokenizer, mlm = False)
+        other_metrics = []
+        
+        from unsloth_zoo.logging_utils import PatchRLStatistics
+        PatchRLStatistics('online_dpo_trainer', other_metrics)
+        
+        super().__init__(
+            model = model,
+            ref_model = ref_model,
+            reward_model = reward_model,
+            judge = judge,
+            args = args,
+            data_collator = data_collator,
+            train_dataset = train_dataset,
+            eval_dataset = eval_dataset,
+            processing_class = processing_class,
+            reward_processing_class = reward_processing_class,
+            peft_config = peft_config,
+            compute_metrics = compute_metrics,
+            callbacks = callbacks,
+            preprocess_logits_for_metrics = preprocess_logits_for_metrics,**kwargs)
+        if hasattr(self, 'neftune_hook_handle'):
+            self.neftune_hook_handle.remove()
+            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
+        if getattr(args, 'neftune_noise_alpha', None) is not None:
+            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
+        pass
+        
+pass

unsloth_compiled_cache/UnslothPPOTrainer.py

@@ -0,0 +1,1253 @@
+"""
+2025.4.1
+2025.4.1
+4.51.3
+0.15.2
+__UNSLOTH_VERSIONING__
+"""
+from torch import Tensor
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from trl.trainer.ppo_trainer import (Accelerator, BaseImageProcessor, CallbackHandler, DEFAULT_CALLBACKS, DEFAULT_PROGRESS_CALLBACK, DataCollatorWithPadding, DataLoader, Dataset, ExportableState, FeatureExtractionMixin, GenerationConfig, INVALID_LOGPROB, OnlineTrainerState, Optional, PPOConfig, PPOTrainer, PeftConfig, PeftModel, PolicyAndValueWrapper, PreTrainedTokenizerBase, PrinterCallback, ProcessorMixin, Trainer, TrainerCallback, TrainerControl, Union, batch_generation, broadcast, contextmanager, create_reference_model, defaultdict, disable_dropout_in_model, exact_div, first_true_indices, forward, gather_object, gc, generate_model_card, get_comet_experiment_url, get_peft_model, get_reporting_integration_callbacks, get_reward, is_peft_available, is_wandb_available, log_table_to_comet_experiment, masked_mean, masked_whiten, math, nn, np, nullcontext, os, pd, peft_module_casting_to_bf16, prepare_deepspeed, print_rich_table, textwrap, time, torch, truncate_response, unwrap_model_for_generation)
+
+
+import os
+from typing import *
+from dataclasses import dataclass, field
+from packaging.version import Version
+import torch
+import numpy as np
+from contextlib import nullcontext
+from torch.nn import functional as F
+from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
+
+torch_compile_options = {
+    "epilogue_fusion"   : True,
+    "max_autotune"      : False,
+    "shape_padding"     : True,
+    "trace.enabled"     : False,
+    "triton.cudagraphs" : False,
+}
+
+@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
+def selective_log_softmax(logits, index):
+    logits = logits.to(torch.float32)
+    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
+    # loop to reduce peak mem consumption
+    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
+    logsumexp_values = torch.logsumexp(logits, dim = -1)
+    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
+    return per_token_logps
+@dataclass
+class UnslothPPOConfig(PPOConfig):
+    """
+    
+    Configuration class for the [`PPOTrainer`].
+
+    Using [`~transformers.HfArgumentParser`] we can turn this class into
+    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
+    command line.
+
+    Parameters:
+        exp_name (`str`, *optional*, defaults to `os.path.basename(__file__)[:-3]`):
+            Name of this experiment.
+        reward_model_path (`str`, *optional*, defaults to `"EleutherAI/pythia-160m"`):
+            Path to the reward model.
+        model_adapter_name (`str` or `None`, *optional*, defaults to `None`):
+            Name of the train target PEFT adapter, when using LoRA with multiple adapters.
+        ref_adapter_name (`str` or `None`, *optional*, defaults to `None`):
+            Name of the reference PEFT adapter, when using LoRA with multiple adapters.
+        num_ppo_epochs (`int`, *optional*, defaults to `4`):
+            Number of epochs to train.
+        whiten_rewards (`bool`, *optional*, defaults to `False`):
+            Whether to whiten the rewards.
+        kl_coef (`float`, *optional*, defaults to `0.05`):
+            KL coefficient.
+        cliprange (`float`, *optional*, defaults to `0.2`):
+            Clip range.
+        vf_coef (`float`, *optional*, defaults to `0.1`):
+            Value function coefficient.
+        cliprange_value (`float`, *optional*, defaults to `0.2`):
+            Clip range for the value function.
+        gamma (`float`, *optional*, defaults to `1.0`):
+            Discount factor.
+        lam (`float`, *optional*, defaults to `0.95`):
+            Lambda value for GAE.
+        ds3_gather_for_generation (`bool`, *optional*, defaults to `True`):
+            This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation,
+            improving generation speed. However, disabling this option allows training models that exceed the VRAM
+            capacity of a single GPU, albeit at the cost of slower generation.
+    
+    """
+    vllm_sampling_params: Optional[Any] = field(
+        default = None,
+        metadata = {'help': 'vLLM SamplingParams'},
+    )
+    unsloth_num_chunks : Optional[int] = field(
+        default = -1,
+        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
+    )
+    def __init__(
+        self,
+        output_dir = None,
+        overwrite_output_dir = None,
+        do_train = False,
+        do_eval = False,
+        do_predict = False,
+        eval_strategy = 'no',
+        prediction_loss_only = False,
+        per_device_train_batch_size = 4,
+        per_device_eval_batch_size = 4,
+        per_gpu_train_batch_size = None,
+        per_gpu_eval_batch_size = None,
+        gradient_accumulation_steps = 2,
+        eval_accumulation_steps = 2,
+        eval_delay = 0,
+        torch_empty_cache_steps = 250,
+        learning_rate = 5e-05,
+        weight_decay = 0.01,
+        adam_beta1 = 0.9,
+        adam_beta2 = 0.999,
+        adam_epsilon = 1e-08,
+        max_grad_norm = 1.0,
+        num_train_epochs = 3.0,
+        max_steps = -1,
+        lr_scheduler_type = 'linear',
+        warmup_ratio = 0.1,
+        warmup_steps = 0,
+        log_level = 'passive',
+        log_level_replica = 'warning',
+        log_on_each_node = True,
+        logging_dir = None,
+        logging_strategy = 'steps',
+        logging_first_step = False,
+        logging_steps = 1,
+        logging_nan_inf_filter = False,
+        save_strategy = 'steps',
+        save_steps = 500,
+        save_total_limit = None,
+        save_safetensors = True,
+        save_on_each_node = False,
+        save_only_model = False,
+        restore_callback_states_from_checkpoint = False,
+        no_cuda = False,
+        use_cpu = False,
+        use_mps_device = False,
+        seed = 3407,
+        data_seed = 3407,
+        jit_mode_eval = False,
+        use_ipex = False,
+        bf16 = False,
+        fp16 = False,
+        fp16_opt_level = 'O1',
+        half_precision_backend = 'auto',
+        bf16_full_eval = False,
+        fp16_full_eval = False,
+        tf32 = None,
+        local_rank = -1,
+        ddp_backend = None,
+        tpu_num_cores = None,
+        tpu_metrics_debug = False,
+        debug = '',
+        dataloader_drop_last = False,
+        eval_steps = None,
+        dataloader_num_workers = 0,
+        dataloader_prefetch_factor = None,
+        past_index = -1,
+        run_name = None,
+        disable_tqdm = None,
+        remove_unused_columns = True,
+        label_names = None,
+        load_best_model_at_end = False,
+        metric_for_best_model = None,
+        greater_is_better = None,
+        ignore_data_skip = False,
+        fsdp = '',
+        fsdp_min_num_params = 0,
+        fsdp_config = None,
+        tp_size = 0,
+        fsdp_transformer_layer_cls_to_wrap = None,
+        accelerator_config = None,
+        deepspeed = None,
+        label_smoothing_factor = 0.0,
+        optim = 'adamw_8bit',
+        optim_args = None,
+        adafactor = False,
+        group_by_length = False,
+        length_column_name = 'length',
+        report_to = None,
+        ddp_find_unused_parameters = None,
+        ddp_bucket_cap_mb = None,
+        ddp_broadcast_buffers = None,
+        dataloader_pin_memory = True,
+        dataloader_persistent_workers = False,
+        skip_memory_metrics = True,
+        use_legacy_prediction_loop = False,
+        push_to_hub = False,
+        resume_from_checkpoint = None,
+        hub_model_id = None,
+        hub_strategy = 'every_save',
+        hub_token = None,
+        hub_private_repo = None,
+        hub_always_push = False,
+        gradient_checkpointing = False,
+        gradient_checkpointing_kwargs = None,
+        include_inputs_for_metrics = False,
+        eval_do_concat_batches = True,
+        fp16_backend = 'auto',
+        push_to_hub_model_id = None,
+        push_to_hub_organization = None,
+        push_to_hub_token = None,
+        mp_parameters = '',
+        auto_find_batch_size = False,
+        full_determinism = False,
+        torchdynamo = None,
+        ray_scope = 'last',
+        ddp_timeout = 1800,
+        torch_compile = False,
+        torch_compile_backend = None,
+        torch_compile_mode = None,
+        include_tokens_per_second = False,
+        include_num_input_tokens_seen = False,
+        neftune_noise_alpha = None,
+        optim_target_modules = None,
+        batch_eval_metrics = False,
+        eval_on_start = False,
+        use_liger_kernel = False,
+        eval_use_gather_object = False,
+        average_tokens_across_devices = False,
+        dataset_num_proc = None,
+        num_mini_batches = 1,
+        total_episodes = None,
+        local_rollout_forward_batch_size = 64,
+        num_sample_generations = 10,
+        response_length = 53,
+        stop_token = None,
+        stop_token_id = None,
+        temperature = 0.7,
+        missing_eos_penalty = None,
+        sft_model_path = 'EleutherAI/pythia-160m',
+        world_size = None,
+        num_total_batches = None,
+        micro_batch_size = None,
+        local_batch_size = None,
+        batch_size = None,
+        local_mini_batch_size = None,
+        mini_batch_size = None,
+        exp_name = 'ppo_config',
+        reward_model_path = 'EleutherAI/pythia-160m',
+        model_adapter_name = None,
+        ref_adapter_name = None,
+        num_ppo_epochs = 4,
+        whiten_rewards = False,
+        kl_coef = 0.05,
+        cliprange = 0.2,
+        vf_coef = 0.1,
+        cliprange_value = 0.2,
+        gamma = 1.0,
+        lam = 0.95,
+        ds3_gather_for_generation = True,
+        vllm_sampling_params = None,
+        unsloth_num_chunks = -1,
+        **kwargs,
+    ):
+        if learning_rate < 1e-7: raise FloatingPointError(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
+        if learning_rate > 1: raise OverflowError(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
+        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
+            output_dir = 'unsloth_training_checkpoints'
+            save_strategy = 'no'
+        if dataset_num_proc is None:
+            from multiprocessing import cpu_count
+            dataset_num_proc = cpu_count()
+        
+        super().__init__(
+            output_dir = output_dir,
+            overwrite_output_dir = overwrite_output_dir,
+            do_train = do_train,
+            do_eval = do_eval,
+            do_predict = do_predict,
+            eval_strategy = eval_strategy,
+            prediction_loss_only = prediction_loss_only,
+            per_device_train_batch_size = per_device_train_batch_size,
+            per_device_eval_batch_size = per_device_eval_batch_size,
+            per_gpu_train_batch_size = per_gpu_train_batch_size,
+            per_gpu_eval_batch_size = per_gpu_eval_batch_size,
+            gradient_accumulation_steps = gradient_accumulation_steps,
+            eval_accumulation_steps = eval_accumulation_steps,
+            eval_delay = eval_delay,
+            torch_empty_cache_steps = torch_empty_cache_steps,
+            learning_rate = learning_rate,
+            weight_decay = weight_decay,
+            adam_beta1 = adam_beta1,
+            adam_beta2 = adam_beta2,
+            adam_epsilon = adam_epsilon,
+            max_grad_norm = max_grad_norm,
+            num_train_epochs = num_train_epochs,
+            max_steps = max_steps,
+            lr_scheduler_type = lr_scheduler_type,
+            warmup_ratio = warmup_ratio,
+            warmup_steps = warmup_steps,
+            log_level = log_level,
+            log_level_replica = log_level_replica,
+            log_on_each_node = log_on_each_node,
+            logging_dir = logging_dir,
+            logging_strategy = logging_strategy,
+            logging_first_step = logging_first_step,
+            logging_steps = logging_steps,
+            logging_nan_inf_filter = logging_nan_inf_filter,
+            save_strategy = save_strategy,
+            save_steps = save_steps,
+            save_total_limit = save_total_limit,
+            save_safetensors = save_safetensors,
+            save_on_each_node = save_on_each_node,
+            save_only_model = save_only_model,
+            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
+            no_cuda = no_cuda,
+            use_cpu = use_cpu,
+            use_mps_device = use_mps_device,
+            seed = seed,
+            data_seed = data_seed,
+            jit_mode_eval = jit_mode_eval,
+            use_ipex = use_ipex,
+            bf16 = bf16,
+            fp16 = fp16,
+            fp16_opt_level = fp16_opt_level,
+            half_precision_backend = half_precision_backend,
+            bf16_full_eval = bf16_full_eval,
+            fp16_full_eval = fp16_full_eval,
+            tf32 = tf32,
+            local_rank = local_rank,
+            ddp_backend = ddp_backend,
+            tpu_num_cores = tpu_num_cores,
+            tpu_metrics_debug = tpu_metrics_debug,
+            debug = debug,
+            dataloader_drop_last = dataloader_drop_last,
+            eval_steps = eval_steps,
+            dataloader_num_workers = dataloader_num_workers,
+            dataloader_prefetch_factor = dataloader_prefetch_factor,
+            past_index = past_index,
+            run_name = run_name,
+            disable_tqdm = disable_tqdm,
+            remove_unused_columns = remove_unused_columns,
+            label_names = label_names,
+            load_best_model_at_end = load_best_model_at_end,
+            metric_for_best_model = metric_for_best_model,
+            greater_is_better = greater_is_better,
+            ignore_data_skip = ignore_data_skip,
+            fsdp = fsdp,
+            fsdp_min_num_params = fsdp_min_num_params,
+            fsdp_config = fsdp_config,
+            tp_size = tp_size,
+            fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
+            accelerator_config = accelerator_config,
+            deepspeed = deepspeed,
+            label_smoothing_factor = label_smoothing_factor,
+            optim = optim,
+            optim_args = optim_args,
+            adafactor = adafactor,
+            group_by_length = group_by_length,
+            length_column_name = length_column_name,
+            report_to = report_to,
+            ddp_find_unused_parameters = ddp_find_unused_parameters,
+            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
+            ddp_broadcast_buffers = ddp_broadcast_buffers,
+            dataloader_pin_memory = dataloader_pin_memory,
+            dataloader_persistent_workers = dataloader_persistent_workers,
+            skip_memory_metrics = skip_memory_metrics,
+            use_legacy_prediction_loop = use_legacy_prediction_loop,
+            push_to_hub = push_to_hub,
+            resume_from_checkpoint = resume_from_checkpoint,
+            hub_model_id = hub_model_id,
+            hub_strategy = hub_strategy,
+            hub_token = hub_token,
+            hub_private_repo = hub_private_repo,
+            hub_always_push = hub_always_push,
+            gradient_checkpointing = gradient_checkpointing,
+            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
+            include_inputs_for_metrics = include_inputs_for_metrics,
+            eval_do_concat_batches = eval_do_concat_batches,
+            fp16_backend = fp16_backend,
+            push_to_hub_model_id = push_to_hub_model_id,
+            push_to_hub_organization = push_to_hub_organization,
+            push_to_hub_token = push_to_hub_token,
+            mp_parameters = mp_parameters,
+            auto_find_batch_size = auto_find_batch_size,
+            full_determinism = full_determinism,
+            torchdynamo = torchdynamo,
+            ray_scope = ray_scope,
+            ddp_timeout = ddp_timeout,
+            torch_compile = torch_compile,
+            torch_compile_backend = torch_compile_backend,
+            torch_compile_mode = torch_compile_mode,
+            include_tokens_per_second = include_tokens_per_second,
+            include_num_input_tokens_seen = include_num_input_tokens_seen,
+            neftune_noise_alpha = neftune_noise_alpha,
+            optim_target_modules = optim_target_modules,
+            batch_eval_metrics = batch_eval_metrics,
+            eval_on_start = eval_on_start,
+            use_liger_kernel = use_liger_kernel,
+            eval_use_gather_object = eval_use_gather_object,
+            average_tokens_across_devices = average_tokens_across_devices,
+            dataset_num_proc = dataset_num_proc,
+            num_mini_batches = num_mini_batches,
+            total_episodes = total_episodes,
+            local_rollout_forward_batch_size = local_rollout_forward_batch_size,
+            num_sample_generations = num_sample_generations,
+            response_length = response_length,
+            stop_token = stop_token,
+            stop_token_id = stop_token_id,
+            temperature = temperature,
+            missing_eos_penalty = missing_eos_penalty,
+            sft_model_path = sft_model_path,
+            world_size = world_size,
+            num_total_batches = num_total_batches,
+            micro_batch_size = micro_batch_size,
+            local_batch_size = local_batch_size,
+            batch_size = batch_size,
+            local_mini_batch_size = local_mini_batch_size,
+            mini_batch_size = mini_batch_size,
+            exp_name = exp_name,
+            reward_model_path = reward_model_path,
+            model_adapter_name = model_adapter_name,
+            ref_adapter_name = ref_adapter_name,
+            num_ppo_epochs = num_ppo_epochs,
+            whiten_rewards = whiten_rewards,
+            kl_coef = kl_coef,
+            cliprange = cliprange,
+            vf_coef = vf_coef,
+            cliprange_value = cliprange_value,
+            gamma = gamma,
+            lam = lam,
+            ds3_gather_for_generation = ds3_gather_for_generation,**kwargs)
+        self.vllm_sampling_params = vllm_sampling_params
+        self.unsloth_num_chunks = unsloth_num_chunks
+pass
+
+class _UnslothPPOTrainer(Trainer):
+    _tag_names = ["trl", "ppo"]
+
+    def __init__(
+        self,
+        args: PPOConfig,
+        processing_class: Optional[
+            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
+        ],
+        model: nn.Module,
+        ref_model: Optional[nn.Module],
+        reward_model: nn.Module,
+        train_dataset: Dataset,
+        value_model: Optional[nn.Module] = None,
+        data_collator: Optional[DataCollatorWithPadding] = None,
+        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
+        # less commonly used
+        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
+        callbacks: Optional[list[TrainerCallback]] = None,
+        peft_config: Optional["PeftConfig"] = None,
+    ) -> None:
+        if ref_model is model:
+            raise ValueError(
+                "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the "
+                "same as `model`, you must make a copy of it, or `None` if you use peft."
+            )
+
+        self.args = args
+        self.processing_class = processing_class
+        self.policy_model = model
+
+        # Define the collator if not provided
+        if data_collator is None:
+            data_collator = DataCollatorWithPadding(self.processing_class)
+
+        # Handle stop token settings: update policy model's generation_config to use provided stop token
+        if args.stop_token and args.stop_token_id:
+            raise ValueError("You cannot set both `stop_token` and `stop_token_id`.")
+        elif args.stop_token:
+            if args.stop_token == "eos":
+                self.policy_model.generation_config.eos_token_id = self.stop_token_id = processing_class.eos_token_id
+            else:
+                raise ValueError(
+                    f"Unknown `stop_token` {args.stop_token}. Allowed values are: `'eos'` and `None` (no stop token)."
+                )
+        else:
+            self.policy_model.generation_config.eos_token_id = self.stop_token_id = args.stop_token_id  # None or int
+
+        # peft support
+        if not is_peft_available() and peft_config is not None:
+            raise ImportError(
+                "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models"
+            )
+        elif is_peft_available() and peft_config is not None:
+            # if model is a peft model and we have a peft_confg, we merge and unload it first
+            if isinstance(self.policy_model, PeftModel):
+                self.policy_model = self.policy_model.merge_and_unload()
+
+            # get peft model with the given config
+            self.policy_model = get_peft_model(self.policy_model, peft_config)
+            if args.bf16 and getattr(self.policy_model, "is_loaded_in_4bit", False):
+                peft_module_casting_to_bf16(self.policy_model)
+
+        self.is_peft_model = is_peft_available() and isinstance(self.policy_model, PeftModel)
+        self.model_adapter_name = args.model_adapter_name
+        self.ref_adapter_name = args.ref_adapter_name
+
+        if ref_model:
+            self.ref_model = ref_model
+        elif self.is_peft_model:
+            self.ref_model = None
+        else:
+            self.ref_model = create_reference_model(self.policy_model)
+
+        self.reward_model = reward_model
+        self.train_dataset = train_dataset
+        self.train_dataset_len = len(train_dataset)
+        self.value_model = value_model
+        self.data_collator = data_collator
+        self.eval_dataset = eval_dataset
+        self.optimizer, self.lr_scheduler = optimizers
+        self.optimizer_cls_and_kwargs = None  # needed for transformers >= 4.47
+
+        #########
+        # calculate various batch sizes
+        #########
+        if args.total_episodes is None:  # allow the users to define episodes in terms of epochs.
+            args.total_episodes = int(args.num_train_epochs * self.train_dataset_len)
+        accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps)
+        self.accelerator = accelerator
+        args.world_size = accelerator.num_processes
+        args.local_batch_size = (
+            args.per_device_train_batch_size * args.gradient_accumulation_steps * args.num_mini_batches
+        )
+        args.micro_batch_size = int(args.per_device_train_batch_size * args.world_size)
+        args.batch_size = int(args.local_batch_size * args.world_size)
+        args.mini_batch_size = exact_div(
+            args.batch_size, args.num_mini_batches, "`batch_size` must be a multiple of `num_mini_batches`"
+        )
+        args.local_mini_batch_size = exact_div(
+            args.local_batch_size, args.num_mini_batches, "`local_batch_size` must be a multiple of `num_mini_batches`"
+        )
+        if args.whiten_rewards:
+            assert (
+                args.local_mini_batch_size >= 8
+            ), f"Per-rank minibatch size {args.local_mini_batch_size} is insufficient for whitening"
+        # `per_rank_rollout_batch_size` is our `args.local_batch_size`
+        # `per_rank_minibatch_size` is our `args.local_mini_batch_size`
+        args.num_total_batches = math.ceil(
+            args.total_episodes / args.batch_size
+        )  # we may train for more than `total_episodes`
+        time_tensor = torch.tensor(int(time.time()), device=accelerator.device)
+        time_int = broadcast(time_tensor, 0).item()  # avoid different timestamps across processes
+        args.run_name = f"{args.exp_name}__{args.seed}__{time_int}"
+        self.local_seed = args.seed + accelerator.process_index * 100003  # Prime
+        if args.num_sample_generations > 0:
+            self.sample_generations_freq = max(1, args.num_total_batches // args.num_sample_generations)
+        self.local_dataloader_batch_size = args.local_batch_size
+
+        #########
+        # setup model, optimizer, and others
+        #########
+        for module in [self.policy_model, self.ref_model, self.value_model, self.reward_model]:
+            if module is not None:
+                disable_dropout_in_model(module)
+        self.model = PolicyAndValueWrapper(self.policy_model, self.value_model)
+        self.model.config = self.policy_model.config  # needed for pushing to hub
+        self.create_optimizer_and_scheduler(
+            num_training_steps=args.num_total_batches
+        )  # note that we are calling `self.lr_scheduler.step()` manually only at the batch level
+
+        #########
+        ### trainer specifics
+        #########
+        default_callbacks = DEFAULT_CALLBACKS + get_reporting_integration_callbacks(self.args.report_to)
+        self.callbacks = default_callbacks if callbacks is None else default_callbacks + callbacks
+        self.callback_handler = CallbackHandler(
+            self.callbacks, self.model, self.processing_class, self.optimizer, self.lr_scheduler
+        )
+        self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK)
+        self.control = TrainerControl()
+        self.state = OnlineTrainerState(
+            is_local_process_zero=self.is_local_process_zero(),
+            is_world_process_zero=self.is_world_process_zero(),
+            stateful_callbacks=[
+                cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState)
+            ],
+        )
+        self.current_flos = 0
+        self.hp_search_backend = None
+        self.is_deepspeed_enabled = getattr(self.accelerator.state, "deepspeed_plugin", None) is not None
+        self.is_fsdp_enabled = getattr(self.accelerator.state, "fsdp_plugin", None) is not None
+        # Create distant repo and output directory if needed
+        self.hub_model_id = None
+        if self.args.push_to_hub:
+            self.init_hf_repo()
+        if self.args.should_save:
+            os.makedirs(self.args.output_dir, exist_ok=True)
+
+        # Add tags for models that have been loaded with the correct transformers version
+        if hasattr(self.model, "add_model_tags"):
+            self.model.add_model_tags(self._tag_names)
+
+        #########
+        ### setup dataloader
+        #########
+        self.dataloader = DataLoader(
+            self.train_dataset,
+            batch_size=self.local_dataloader_batch_size,
+            shuffle=True,
+            collate_fn=self.data_collator,
+            drop_last=True,  # needed; otherwise the last batch will be of ragged shape
+        )
+        # sync random states for DataLoader(shuffle=True) before `accelerator.prepare`
+        # see https://gist.github.com/vwxyzjn/2581bff1e48e185e0b85b6dfe1def79c
+        torch.manual_seed(args.seed)
+        self.model, self.optimizer, self.dataloader = accelerator.prepare(self.model, self.optimizer, self.dataloader)
+        torch.manual_seed(self.local_seed)  # reset the local seed again
+
+        self.eval_dataloader = DataLoader(
+            self.eval_dataset,
+            batch_size=args.per_device_eval_batch_size,
+            collate_fn=self.data_collator,
+            drop_last=True,
+        )  # no need to shuffle eval dataset
+        self.eval_dataloader = accelerator.prepare(self.eval_dataloader)
+
+        if self.is_deepspeed_enabled:
+            self.reward_model = prepare_deepspeed(
+                self.reward_model, args.per_device_train_batch_size, args.fp16, args.bf16
+            )
+
+            if self.ref_model is None:
+                if not self.is_peft_model:
+                    raise ValueError("No reference model and model is not a Peft model.")
+            else:
+                self.ref_model = prepare_deepspeed(
+                    self.ref_model, args.per_device_train_batch_size, args.fp16, args.bf16
+                )
+        else:
+            if self.ref_model is None:
+                if not self.is_peft_model:
+                    raise ValueError("No reference model and model is not a Peft model.")
+            else:
+                self.ref_model = self.ref_model.to(self.accelerator.device)
+            self.reward_model = self.reward_model.to(self.accelerator.device)
+
+    def get_train_dataloader(self) -> DataLoader:
+        return self.dataloader
+
+    def get_eval_dataloader(self) -> DataLoader:
+        return self.eval_dataloader
+
+    @contextmanager
+    def null_ref_context(self):
+        """Context manager for handling null reference model (that is, peft adapter manipulation)."""
+        with (
+            self.accelerator.unwrap_model(self.model.policy).disable_adapter()
+            if self.is_peft_model and not self.ref_adapter_name
+            else nullcontext()
+        ):
+            if self.ref_adapter_name:
+                self.model.policy.set_adapter(self.ref_adapter_name)
+            yield
+            if self.ref_adapter_name:
+                self.model.policy.set_adapter(self.model_adapter_name or "default")
+
+    def save_model(self, output_dir: Optional[str] = None, _internal_call: bool = False):
+        backup_model = self.model
+        self.model = self.model.policy  # save only the policy
+
+        if self.is_deepspeed_enabled:
+            backup_deepspeed = self.deepspeed
+            self.deepspeed = self.model
+
+        super().save_model(output_dir, _internal_call)
+
+        self.model = backup_model
+
+        if self.is_deepspeed_enabled:
+            self.deepspeed = backup_deepspeed
+
+    def train(self):
+        args = self.args
+        accelerator = self.accelerator
+        optimizer = self.optimizer
+        model = self.model
+        ref_policy = self.ref_model
+        reward_model = self.reward_model
+        processing_class = self.processing_class
+        dataloader = self.dataloader
+        device = accelerator.device
+
+        def repeat_generator():
+            while True:
+                yield from dataloader
+
+        iter_dataloader = iter(repeat_generator())
+        generation_config = GenerationConfig(
+            max_new_tokens=args.response_length,
+            temperature=(args.temperature + 1e-7),
+            top_k=0.0,
+            top_p=1.0,
+            do_sample=True,
+        )
+
+        accelerator.print("===training policy===")
+        start_time = time.time()
+        stats_shape = (args.num_ppo_epochs, args.num_mini_batches, args.gradient_accumulation_steps)
+        approxkl_stats = torch.zeros(stats_shape, device=device)
+        pg_clipfrac_stats = torch.zeros(stats_shape, device=device)
+        pg_loss_stats = torch.zeros(stats_shape, device=device)
+        vf_loss_stats = torch.zeros(stats_shape, device=device)
+        vf_clipfrac_stats = torch.zeros(stats_shape, device=device)
+        entropy_stats = torch.zeros(stats_shape, device=device)
+        ratio_stats = torch.zeros(stats_shape, device=device)
+        model.train()
+
+        # trainer state initialization
+        self.state.global_step = 0
+        self.state.episode = 0
+        self.state.max_steps = args.num_total_batches * args.num_mini_batches
+        self.state.num_train_epochs = args.total_episodes / self.train_dataset_len
+        # Compute absolute values for logging, eval, and save if given as ratio
+        if args.logging_steps is not None:
+            if args.logging_steps < 1:
+                self.state.logging_steps = math.ceil(self.state.max_steps * args.logging_steps)
+            else:
+                self.state.logging_steps = args.logging_steps
+        if args.eval_steps is not None:
+            if args.eval_steps < 1:
+                self.state.eval_steps = math.ceil(self.state.max_steps * args.eval_steps)
+            else:
+                self.state.eval_steps = args.eval_steps
+        if args.save_steps is not None:
+            if args.save_steps < 1:
+                self.state.save_steps = math.ceil(self.state.max_steps * args.save_steps)
+            else:
+                self.state.save_steps = args.save_steps
+        self.control = self.callback_handler.on_train_begin(args, self.state, self.control)
+
+        # backward compatibility
+        if self.is_deepspeed_enabled:
+            self.deepspeed = self.model
+            self.model_wrapped = self.model
+
+        for update in range(1, args.num_total_batches + 1):
+            self.state.episode += 1 * args.batch_size
+            data = next(iter_dataloader)
+            with torch.no_grad():
+                queries = data["input_ids"].to(device)
+                context_length = queries.shape[1]
+                responses = []
+                postprocessed_responses = []
+                logprobs = []
+                ref_logprobs = []
+                scores = []
+                sequence_lengths = []
+                values = []
+                with unwrap_model_for_generation(
+                    self.model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
+                ) as unwrapped_model:
+                    query_responses, logitss = batch_generation(
+                        unwrapped_model.policy,
+                        queries,
+                        args.local_rollout_forward_batch_size,
+                        processing_class.pad_token_id,
+                        generation_config,
+                    )
+
+                for i in range(0, queries.shape[0], args.local_rollout_forward_batch_size):
+                    query = queries[i : i + args.local_rollout_forward_batch_size]
+                    query_response = query_responses[i : i + args.local_rollout_forward_batch_size]
+                    response = query_response[:, context_length:]
+                    logits = logitss[i : i + args.local_rollout_forward_batch_size]
+                    logprob = selective_log_softmax(logits, response)
+                    del logits
+                    torch.cuda.empty_cache()
+
+                    if ref_policy is None:
+                        with self.null_ref_context():
+                            ref_output = forward(model.policy, query_response, processing_class.pad_token_id)
+                    else:
+                        ref_output = forward(ref_policy, query_response, processing_class.pad_token_id)
+                    ref_logits = ref_output.logits[:, context_length - 1 : -1]
+                    ref_logits /= args.temperature + 1e-7
+                    ref_logprob = selective_log_softmax(ref_logits, response)
+                    del ref_output, ref_logits
+                    torch.cuda.empty_cache()
+
+                    # Response Processing 1. truncate response after the first occurrence of `stop_token_id`
+                    postprocessed_response = response
+                    if self.stop_token_id is not None:  # handle the edge case when stop_token_id exists but is 0
+                        postprocessed_response = truncate_response(
+                            self.stop_token_id, processing_class.pad_token_id, response
+                        )
+
+                    # Response Processing 2. run reward model on the truncated responses
+                    postprocessed_query_response = torch.cat((query, postprocessed_response), 1)
+                    sequence_length = first_true_indices(postprocessed_response == processing_class.pad_token_id) - 1
+                    unwrapped_value_model = accelerator.unwrap_model(model).value_model
+                    full_value, _, _ = get_reward(
+                        unwrapped_value_model, query_response, processing_class.pad_token_id, context_length
+                    )
+                    value = full_value[:, context_length - 1 : -1].squeeze(-1)
+                    _, score, _ = get_reward(
+                        reward_model, postprocessed_query_response, processing_class.pad_token_id, context_length
+                    )
+
+                    responses.append(response)
+                    postprocessed_responses.append(postprocessed_response)
+                    logprobs.append(logprob)
+                    ref_logprobs.append(ref_logprob)
+                    sequence_lengths.append(sequence_length)
+                    scores.append(score)
+                    values.append(value)
+                responses = torch.cat(responses, 0)
+                postprocessed_responses = torch.cat(postprocessed_responses, 0)
+                logprobs = torch.cat(logprobs, 0)
+                ref_logprobs = torch.cat(ref_logprobs, 0)
+                sequence_lengths = torch.cat(sequence_lengths, 0)
+                scores = torch.cat(scores, 0)
+                values = torch.cat(values, 0)
+                del (logprob, ref_logprob, full_value, value, score, unwrapped_model)
+                torch.cuda.empty_cache()
+                gc.collect()
+
+                # Response Processing 3. Filter completion. Ensure that the sample contains stop_token_id
+                # Completions not passing that filter will receive a lower score.
+                contain_eos_token = torch.any(postprocessed_responses == self.processing_class.eos_token_id, dim=-1)
+                if self.args.missing_eos_penalty is not None:
+                    scores[~contain_eos_token] -= self.args.missing_eos_penalty
+                # accelerator.print(f"{scores=}, {(contain_eos_token.sum() / len(contain_eos_token))=}")
+
+                # be very careful with `padding_mask_p1`; see https://excalidraw.com/#json=LWnzG4w2k5DjF_EOL_xPt,e2w3a-hFJ_gX5vOfeyXGTw
+                response_idxs = torch.arange(responses.shape[1], device=responses.device).repeat(responses.shape[0], 1)
+                padding_mask = response_idxs > sequence_lengths.unsqueeze(1)
+                logprobs = torch.masked_fill(logprobs, padding_mask, INVALID_LOGPROB)
+                ref_logprobs = torch.masked_fill(ref_logprobs, padding_mask, INVALID_LOGPROB)
+                sequence_lengths_p1 = sequence_lengths + 1
+                padding_mask_p1 = response_idxs > (sequence_lengths_p1.unsqueeze(1))
+                values = torch.masked_fill(values, padding_mask_p1, 0)
+
+                # 4. compute rewards
+                kl = logprobs - ref_logprobs
+                non_score_reward = -args.kl_coef * kl
+                rewards = non_score_reward.clone()
+                actual_start = torch.arange(rewards.size(0), device=rewards.device)
+                actual_end = torch.where(sequence_lengths_p1 < rewards.size(1), sequence_lengths_p1, sequence_lengths)
+                rewards[[actual_start, actual_end]] += scores
+
+                # 5. whiten rewards
+                if args.whiten_rewards:
+                    rewards = masked_whiten(rewards, mask=~padding_mask_p1, shift_mean=False)
+                    rewards = torch.masked_fill(rewards, padding_mask_p1, 0)
+
+                # 6. compute advantages and returns
+                lastgaelam = 0
+                advantages_reversed = []
+                gen_length = responses.shape[1]
+                for t in reversed(range(gen_length)):
+                    nextvalues = values[:, t + 1] if t < gen_length - 1 else 0.0
+                    delta = rewards[:, t] + args.gamma * nextvalues - values[:, t]
+                    lastgaelam = delta + args.gamma * args.lam * lastgaelam
+                    advantages_reversed.append(lastgaelam)
+                advantages = torch.stack(advantages_reversed[::-1], axis=1)
+                returns = advantages + values
+                advantages = masked_whiten(advantages, ~padding_mask)
+                advantages = torch.masked_fill(advantages, padding_mask, 0)
+                torch.cuda.empty_cache()
+
+            # Do multiple epochs of PPO training, with a fresh random shuffle in each epoch
+            for ppo_epoch_idx in range(args.num_ppo_epochs):
+                b_inds = np.random.permutation(args.local_batch_size)
+                minibatch_idx = 0
+                for mini_batch_start in range(0, args.local_batch_size, args.local_mini_batch_size):
+                    mini_batch_end = mini_batch_start + args.local_mini_batch_size
+                    mini_batch_inds = b_inds[mini_batch_start:mini_batch_end]
+                    gradient_accumulation_idx = 0
+                    for micro_batch_start in range(0, args.local_mini_batch_size, args.per_device_train_batch_size):
+                        with accelerator.accumulate(model):
+                            micro_batch_end = micro_batch_start + args.per_device_train_batch_size
+                            micro_batch_inds = mini_batch_inds[micro_batch_start:micro_batch_end]
+                            mb_advantage = advantages[micro_batch_inds]
+                            mb_responses = responses[micro_batch_inds]
+                            mb_query_responses = query_responses[micro_batch_inds]
+                            mb_logprobs = logprobs[micro_batch_inds]
+                            mb_return = returns[micro_batch_inds]
+                            mb_values = values[micro_batch_inds]
+
+                            output, vpred_temp = forward(model, mb_query_responses, processing_class.pad_token_id)
+                            logits = output.logits[:, context_length - 1 : -1]
+                            logits /= args.temperature + 1e-7
+                            new_logprobs = selective_log_softmax(logits, mb_responses)
+                            new_logprobs = torch.masked_fill(
+                                new_logprobs, padding_mask[micro_batch_inds], INVALID_LOGPROB
+                            )
+                            vpred = vpred_temp[:, context_length - 1 : -1].squeeze(-1)
+                            vpred = torch.masked_fill(vpred, padding_mask_p1[micro_batch_inds], 0)
+                            vpredclipped = torch.clamp(
+                                vpred,
+                                mb_values - args.cliprange_value,
+                                mb_values + args.cliprange_value,
+                            )
+                            vf_losses1 = torch.square(vpred - mb_return)
+                            vf_losses2 = torch.square(vpredclipped - mb_return)
+                            vf_loss_max = torch.max(vf_losses1, vf_losses2)
+                            vf_loss = 0.5 * masked_mean(vf_loss_max, ~padding_mask_p1[micro_batch_inds])
+                            vf_clipfrac = masked_mean(
+                                (vf_losses2 > vf_losses1).float(), ~padding_mask_p1[micro_batch_inds]
+                            )
+                            logprobs_diff = new_logprobs - mb_logprobs
+                            ratio = torch.exp(logprobs_diff)
+                            pg_losses = -mb_advantage * ratio
+                            pg_losses2 = -mb_advantage * torch.clamp(ratio, 1.0 - args.cliprange, 1.0 + args.cliprange)
+                            pg_loss_max = torch.max(pg_losses, pg_losses2)
+                            pg_loss = masked_mean(pg_loss_max, ~padding_mask[micro_batch_inds])
+                            loss = pg_loss + args.vf_coef * vf_loss
+                            accelerator.backward(loss)
+                            optimizer.step()
+                            optimizer.zero_grad()
+                            with torch.no_grad():
+                                pg_clipfrac = masked_mean(
+                                    (pg_losses2 > pg_losses).float(), ~padding_mask[micro_batch_inds]
+                                )
+                                prob_dist = torch.nn.functional.softmax(logits, dim=-1)
+                                entropy = torch.logsumexp(logits, dim=-1) - torch.sum(prob_dist * logits, dim=-1)
+                                approxkl = 0.5 * (logprobs_diff**2).mean()
+                                approxkl_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = approxkl
+                                pg_clipfrac_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = (
+                                    pg_clipfrac
+                                )
+                                pg_loss_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = pg_loss
+                                vf_loss_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = vf_loss
+                                vf_clipfrac_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = (
+                                    vf_clipfrac
+                                )
+                                entropy_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = entropy.mean()
+                                ratio_stats[ppo_epoch_idx, minibatch_idx, gradient_accumulation_idx] = ratio.mean()
+                        gradient_accumulation_idx += 1
+                    minibatch_idx += 1
+                    # del everything and empty cache
+                    # fmt: off
+                    del (
+                        output, vpred_temp, logits, new_logprobs, vpred, vpredclipped,
+                        vf_losses1, vf_losses2, vf_loss, vf_clipfrac, logprobs_diff, ratio, pg_losses, pg_losses2, pg_loss_max,
+                        pg_loss, loss, pg_clipfrac, prob_dist, entropy, approxkl, mb_return,
+                        mb_advantage, mb_values, mb_responses, mb_query_responses, mb_logprobs,
+                    )
+                    # fmt: on
+                    torch.cuda.empty_cache()
+            with torch.no_grad():
+                mean_kl = kl.sum(1).mean()
+                mean_entropy = (-logprobs).sum(1).mean()
+                mean_non_score_reward = non_score_reward.sum(1).mean()
+                rlhf_reward = mean_non_score_reward + scores.mean()
+                eps = int(self.state.episode / (time.time() - start_time))
+                metrics = {}
+                metrics["eps"] = eps
+                metrics["objective/kl"] = self.accelerator.gather_for_metrics(mean_kl).mean().item()
+                metrics["objective/entropy"] = self.accelerator.gather_for_metrics(mean_entropy).mean().item()
+                metrics["objective/non_score_reward"] = (
+                    self.accelerator.gather_for_metrics(mean_non_score_reward).mean().item()
+                )
+                metrics["objective/rlhf_reward"] = self.accelerator.gather_for_metrics(rlhf_reward).mean().item()
+                metrics["objective/scores"] = self.accelerator.gather_for_metrics(scores.mean()).mean().item()
+                metrics["policy/approxkl_avg"] = self.accelerator.gather_for_metrics(approxkl_stats).mean().item()
+                metrics["policy/clipfrac_avg"] = self.accelerator.gather_for_metrics(pg_clipfrac_stats).mean().item()
+                metrics["loss/policy_avg"] = self.accelerator.gather_for_metrics(pg_loss_stats).mean().item()
+                metrics["loss/value_avg"] = self.accelerator.gather_for_metrics(vf_loss_stats).mean().item()
+                metrics["val/clipfrac_avg"] = self.accelerator.gather_for_metrics(vf_clipfrac_stats).mean().item()
+                metrics["policy/entropy_avg"] = self.accelerator.gather_for_metrics(entropy_stats).mean().item()
+                metrics["val/ratio"] = self.accelerator.gather_for_metrics(ratio_stats).mean().item()
+                metrics["val/ratio_var"] = self.accelerator.gather_for_metrics(ratio_stats).var().item()
+                metrics["val/num_eos_tokens"] = (responses == processing_class.eos_token_id).sum().item()
+                metrics["lr"] = self.lr_scheduler.get_last_lr()[0]
+                metrics["episode"] = self.state.episode
+                self.state.epoch = self.state.episode / self.train_dataset_len  # used by self.log
+                self.state.global_step += 1
+                self.log(metrics)
+
+            self.lr_scheduler.step()
+            self.control = self.callback_handler.on_step_end(args, self.state, self.control)
+            if self.control.should_save:
+                self._save_checkpoint(model, trial=None)
+                self.control = self.callback_handler.on_save(self.args, self.state, self.control)
+            del kl, mean_kl, mean_entropy, mean_non_score_reward, scores, metrics, non_score_reward
+            torch.cuda.empty_cache()
+            gc.collect()
+
+            if args.num_sample_generations > 0 and (update - 1) % self.sample_generations_freq == 0:
+                self.generate_completions(sampling=True)
+                torch.cuda.empty_cache()
+            del (
+                query_responses,
+                responses,
+                postprocessed_responses,
+                logprobs,
+                ref_logprobs,
+                values,
+                sequence_lengths,
+                contain_eos_token,
+                sequence_lengths_p1,
+                response_idxs,
+                padding_mask,
+                padding_mask_p1,
+                rewards,
+                actual_start,
+                actual_end,
+                advantages,
+                returns,
+            )
+            torch.cuda.empty_cache()
+
+        # HF trainer specifics
+        self.control = self.callback_handler.on_train_end(args, self.state, self.control)
+        if self.control.should_save:
+            self._save_checkpoint(model, trial=None, metrics=None)
+            self.control = self.callback_handler.on_save(self.args, self.state, self.control)
+
+    def generate_completions(self, sampling: bool = False):
+        args = self.args
+        processing_class = self.processing_class
+        generation_config = GenerationConfig(
+            max_new_tokens=self.args.response_length,
+            temperature=(0.01 + 1e-7),
+            top_k=0.0,
+            top_p=1.0,
+            do_sample=True,
+        )
+
+        table = defaultdict(list)
+        with unwrap_model_for_generation(
+            self.model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
+        ) as unwrapped_model:
+            for batch in self.eval_dataloader:
+                query = batch["input_ids"]
+                with torch.no_grad():
+                    context_length = query.shape[1]
+                    query_response, _ = batch_generation(
+                        unwrapped_model.policy,
+                        query,
+                        query.shape[0],
+                        processing_class.pad_token_id,
+                        generation_config,
+                    )
+                    response = query_response[:, context_length:]
+                    postprocessed_response = response
+                    if self.stop_token_id is not None:  # handle the edge case when stop_token_id exists but is 0
+                        postprocessed_response = truncate_response(
+                            self.stop_token_id, processing_class.pad_token_id, response
+                        )
+                    table["query"].extend(
+                        gather_object(processing_class.batch_decode(query, skip_special_tokens=True))
+                    )
+                    table["model response"].extend(
+                        gather_object(processing_class.batch_decode(postprocessed_response))
+                    )
+
+                    postprocessed_query_response = torch.cat((query, postprocessed_response), 1)
+                    _, score, _ = get_reward(
+                        self.reward_model, postprocessed_query_response, processing_class.pad_token_id, context_length
+                    )
+                    table["score"].extend(self.accelerator.gather_for_metrics(score).float().cpu().numpy())
+
+                if sampling:
+                    break
+        df = pd.DataFrame(table)
+
+        if self.accelerator.is_main_process:
+            print_rich_table(df.iloc[0 : 0 + 5])
+            if "wandb" in args.report_to:
+                import wandb
+
+                if wandb.run is not None:
+                    wandb.log({"completions": wandb.Table(dataframe=df)})
+
+            if "comet_ml" in args.report_to:
+                log_table_to_comet_experiment(
+                    name="completions.csv",
+                    table=df,
+                )
+
+    def create_model_card(
+        self,
+        model_name: Optional[str] = None,
+        dataset_name: Optional[str] = None,
+        tags: Union[str, list[str], None] = None,
+    ):
+        """
+        Creates a draft of a model card using the information available to the `Trainer`.
+
+        Args:
+            model_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the model.
+            dataset_name (`str` or `None`, *optional*, defaults to `None`):
+                Name of the dataset used for training.
+            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
+                Tags to be associated with the model card.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
+            base_model = self.model.config._name_or_path
+        else:
+            base_model = None
+
+        tags = tags or []
+        if isinstance(tags, str):
+            tags = [tags]
+
+        if hasattr(self.model.config, "unsloth_version"):
+            tags.append("unsloth")
+
+        citation = textwrap.dedent("""\
+        @article{mziegler2019fine-tuning,
+            title        = {{Fine-Tuning Language Models from Human Preferences}},
+            author       = {Daniel M. Ziegler and Nisan Stiennon and Jeffrey Wu and Tom B. Brown and Alec Radford and Dario Amodei and Paul F. Christiano and Geoffrey Irving},
+            year         = 2019,
+            eprint       = {arXiv:1909.08593}
+        }""")
+
+        model_card = generate_model_card(
+            base_model=base_model,
+            model_name=model_name,
+            hub_model_id=self.hub_model_id,
+            dataset_name=dataset_name,
+            tags=tags,
+            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
+            comet_url=get_comet_experiment_url(),
+            trainer_name="PPO",
+            trainer_citation=citation,
+            paper_title="Fine-Tuning Language Models from Human Preferences",
+            paper_id="1909.08593",
+        )
+
+        model_card.save(os.path.join(self.args.output_dir, "README.md"))
+class UnslothPPOTrainer(_UnslothPPOTrainer):
+    """
+    
+    """
+    def __init__(
+        self,
+        args,
+        processing_class,
+        model,
+        ref_model,
+        reward_model,
+        train_dataset,
+        value_model = None,
+        data_collator = None,
+        eval_dataset = None,
+        callbacks = None,
+        peft_config = None,
+        **kwargs
+    ):
+        if args is None: args = UnslothPPOConfig()
+        use_bf16 = getattr(args, 'bf16', False)
+        use_fp16 = getattr(args, 'fp16', False)
+        force_float32 = False
+        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':
+            print('Unsloth: Switching to float32 training since model cannot work with float16')
+            force_float32 = True
+        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
+        dtype = getattr(model.config, 'torch_dtype', None)
+        if dtype is None: dtype = model.get_input_embeddings().dtype
+        from unsloth_zoo.utils import _get_dtype
+        dtype = _get_dtype(dtype)
+        float16 = dtype == torch.float16
+        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
+        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
+        if force_float32:
+            args.fp16 = False
+            args.bf16 = False
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
+        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
+            args.fp16 = float16
+            args.bf16 = not float16
+            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
+        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
+            args.eval_strategy = 'steps'
+            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
+        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
+        if ga_steps is not None and ga_steps > 1:
+            from transformers import __version__ as transformers_version
+            if Version(transformers_version) <= Version('4.45.2'):
+                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
+                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
+        if getattr(args, 'eval_strategy', 'no') != 'no':
+            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
+            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
+            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
+        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
+        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
+        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
+        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
+        if force_float32:
+            args.bf16_full_eval = False
+            args.fp16_full_eval = False
+        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
+            args.bf16_full_eval = True
+            args.fp16_full_eval = False
+        elif not bf16_full_eval and not fp16_full_eval:
+            args.bf16_full_eval = args.bf16
+            args.fp16_full_eval = args.fp16
+        _output_logits = False
+        if locals().get('compute_metrics', None) is not None: _output_logits = True
+        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
+        if _output_logits:
+            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
+        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
+            pass
+        else:
+            model_max_seq_length = getattr(model, 'max_seq_length', None)
+            args_max_seq_length  = getattr(args,  'max_seq_length', None)
+            if args_max_seq_length is None and model_max_seq_length is not None:
+                max_seq_length = model.max_seq_length
+                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
+        if model is not None and hasattr(model, 'for_training'):
+            model.for_training()
+        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
+        if 'processing_class' in locals():
+            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
+            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
+        __tokenizer = processing_class if 'processing_class' in locals() else tokenizer
+        from unsloth_zoo.vision_utils import UnslothVisionDataCollator
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
+                data_collator = DataCollatorForLanguageModeling(__tokenizer, mlm = False)
+            elif isinstance(data_collator, DataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
+                data_collator = DataCollatorForSeq2Seq(__tokenizer)
+        else:
+            if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
+            if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
+            if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
+        if not isinstance(data_collator, UnslothVisionDataCollator):
+            if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
+                if isinstance(data_collator, DataCollatorForSeq2Seq):
+                    data_collator = DataCollatorForSeq2Seq(__tokenizer.tokenizer)
+                else:
+                    data_collator = DataCollatorForLanguageModeling(__tokenizer.tokenizer, mlm = False)
+        other_metrics = []
+        
+        from unsloth_zoo.logging_utils import PatchRLStatistics
+        PatchRLStatistics('ppo_trainer', other_metrics)
+        
+        super().__init__(
+            args = args,
+            processing_class = processing_class,
+            model = model,
+            ref_model = ref_model,
+            reward_model = reward_model,
+            train_dataset = train_dataset,
+            value_model = value_model,
+            data_collator = data_collator,
+            eval_dataset = eval_dataset,
+            callbacks = callbacks,
+            peft_config = peft_config,**kwargs)
+        if hasattr(self, 'neftune_hook_handle'):
+            self.neftune_hook_handle.remove()
+            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
+        if getattr(args, 'neftune_noise_alpha', None) is not None:
+            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
+        pass
+        
+pass