Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,14 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+lora_model/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+outputs/checkpoint-100/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+outputs/checkpoint-200/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+outputs/checkpoint-300/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+outputs/checkpoint-400/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+outputs/checkpoint-500/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+outputs/checkpoint-600/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+outputs/checkpoint-700/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+outputs/checkpoint-800/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+outputs/checkpoint-900/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+outputs/checkpoint-936/tokenizer.json filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/Untitled1-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.venv-backups/18897064/backup.log

@@ -0,0 +1,60 @@
+[2025-03-18 05:24:42] Processing virtual environment: /venv/main
+[2025-03-18 05:24:43] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-0524.txt
+[2025-03-18 05:24:43] Backup process completed
+[2025-03-18 05:30:01] Processing virtual environment: /venv/main
+[2025-03-18 05:30:01] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-0530.txt
+[2025-03-18 05:30:01] Backup process completed
+[2025-03-18 06:00:01] Processing virtual environment: /venv/main
+[2025-03-18 06:00:02] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-0600.txt
+[2025-03-18 06:00:02] Backup process completed
+[2025-03-18 06:30:01] Processing virtual environment: /venv/main
+[2025-03-18 06:30:02] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-0630.txt
+[2025-03-18 06:30:02] Backup process completed
+[2025-03-18 07:00:01] Processing virtual environment: /venv/main
+[2025-03-18 07:00:02] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-0700.txt
+[2025-03-18 07:00:02] Backup process completed
+[2025-03-18 07:30:01] Processing virtual environment: /venv/main
+[2025-03-18 07:30:02] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-0730.txt
+[2025-03-18 07:30:02] Backup process completed
+[2025-03-18 08:00:01] Processing virtual environment: /venv/main
+[2025-03-18 08:00:01] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-0800.txt
+[2025-03-18 08:00:01] Backup process completed
+[2025-03-18 08:30:01] Processing virtual environment: /venv/main
+[2025-03-18 08:30:02] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-0830.txt
+[2025-03-18 08:30:02] Backup process completed
+[2025-03-18 09:00:01] Processing virtual environment: /venv/main
+[2025-03-18 09:00:01] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-0900.txt
+[2025-03-18 09:00:02] Backup process completed
+[2025-03-18 09:30:01] Processing virtual environment: /venv/main
+[2025-03-18 09:30:01] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-0930.txt
+[2025-03-18 09:30:01] Backup process completed
+[2025-03-18 10:00:01] Processing virtual environment: /venv/main
+[2025-03-18 10:00:02] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-1000.txt
+[2025-03-18 10:00:02] Backup process completed
+[2025-03-18 10:30:01] Processing virtual environment: /venv/main
+[2025-03-18 10:30:01] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-1030.txt
+[2025-03-18 10:30:01] Backup process completed
+[2025-03-18 11:00:01] Processing virtual environment: /venv/main
+[2025-03-18 11:00:02] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-1100.txt
+[2025-03-18 11:00:02] Backup process completed
+[2025-03-18 11:30:01] Processing virtual environment: /venv/main
+[2025-03-18 11:30:02] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-1130.txt
+[2025-03-18 11:30:02] Backup process completed
+[2025-03-18 12:00:01] Processing virtual environment: /venv/main
+[2025-03-18 12:00:01] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-1200.txt
+[2025-03-18 12:00:01] Backup process completed
+[2025-03-18 12:30:01] Processing virtual environment: /venv/main
+[2025-03-18 12:30:02] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-1230.txt
+[2025-03-18 12:30:02] Backup process completed
+[2025-03-18 13:00:01] Processing virtual environment: /venv/main
+[2025-03-18 13:00:02] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-1300.txt
+[2025-03-18 13:00:02] Backup process completed
+[2025-03-18 13:30:01] Processing virtual environment: /venv/main
+[2025-03-18 13:30:01] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-1330.txt
+[2025-03-18 13:30:01] Backup process completed
+[2025-03-18 14:00:01] Processing virtual environment: /venv/main
+[2025-03-18 14:00:02] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-1400.txt
+[2025-03-18 14:00:02] Backup process completed
+[2025-03-18 14:30:01] Processing virtual environment: /venv/main
+[2025-03-18 14:30:01] SUCCESS: Created backup at /workspace/.venv-backups/18897064/venv-main-2025-03-18-1430.txt
+[2025-03-18 14:30:01] Backup process completed

.venv-backups/18897064/venv-main-2025-03-18-0524.txt

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.venv-backups/18897064/venv-main-2025-03-18-0530.txt

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.venv-backups/18897064/venv-main-2025-03-18-0600.txt

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.venv-backups/18897064/venv-main-2025-03-18-0630.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-0700.txt

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+unsloth @ git+https://github.com/unslothai/unsloth.git@6f7c8c6d0a63caaa129cc0bc6b845d5d8b9c81e8
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.venv-backups/18897064/venv-main-2025-03-18-0730.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-0800.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-0830.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-0900.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-0930.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-1000.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-1030.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-1100.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-1130.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-1200.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-1230.txt

@@ -0,0 +1,110 @@
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+unsloth @ git+https://github.com/unslothai/unsloth.git@6f7c8c6d0a63caaa129cc0bc6b845d5d8b9c81e8
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.venv-backups/18897064/venv-main-2025-03-18-1300.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-1330.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-1400.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-2025-03-18-1430.txt

@@ -0,0 +1,110 @@
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.venv-backups/18897064/venv-main-latest.txt

@@ -0,0 +1,110 @@
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+tzdata==2025.1
+unsloth @ git+https://github.com/unslothai/unsloth.git@6f7c8c6d0a63caaa129cc0bc6b845d5d8b9c81e8
+unsloth_zoo==2025.3.12
+urllib3==2.3.0
+wcwidth==0.2.13
+widgetsnbextension==4.0.13
+xformers==0.0.29.post1
+xxhash==3.5.0
+yarl==1.18.3
+zipp==3.21.0

Untitled.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "de38bf8f-3186-4014-a29e-eca49cb72aea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
+      "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
+      "==((====))==  Unsloth 2025.3.14: Fast Qwen2 patching. Transformers: 4.49.0.\n",
+      "   \\\\   /|    NVIDIA A100-PCIE-40GB. Num GPUs = 1. Max memory: 39.394 GB. Platform: Linux.\n",
+      "O^O/ \\_/ \\    Torch: 2.5.1+cu121. CUDA: 8.0. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
+      "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = False]\n",
+      " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
+      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38898fae87c94e53ba161b9a7d1c38fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/3.09G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/270 [00:00<?, ?B/s]"
+      ]
+     },
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+     "output_type": "display_data"
+    },
+    {
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+       "tokenizer_config.json:   0%|          | 0.00/7.36k [00:00<?, ?B/s]"
+      ]
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+    },
+    {
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+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d2750b8cb154c9d956bed8d63e3d53b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_major": 2,
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+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from unsloth import FastLanguageModel\n",
+    "import torch\n",
+    "max_seq_length = 8192 # Choose any! We auto support RoPE Scaling internally!\n",
+    "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
+    "load_in_4bit = False # Use 4bit quantization to reduce memory usage. Can be False.\n",
+    "\n",
+    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
+    "    # Can select any from the below:\n",
+    "    # \"unsloth/Qwen2.5-0.5B\", \"unsloth/Qwen2.5-1.5B\", \"unsloth/Qwen2.5-3B\"\n",
+    "    # \"unsloth/Qwen2.5-14B\",  \"unsloth/Qwen2.5-32B\",  \"unsloth/Qwen2.5-72B\",\n",
+    "    # And also all Instruct versions and Math. Coding verisons!\n",
+    "    model_name = \"unsloth/Qwen2.5-1.5B-Instruct\",\n",
+    "    max_seq_length = max_seq_length,\n",
+    "    dtype = dtype,\n",
+    "    load_in_4bit = load_in_4bit,\n",
+    "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5f94455a-e496-46d8-8e81-30b1ac788b62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Unsloth 2025.3.14 patched 28 layers with 28 QKV layers, 28 O layers and 28 MLP layers.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = FastLanguageModel.get_peft_model(\n",
+    "    model,\n",
+    "    r = 32, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
+    "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
+    "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
+    "    lora_alpha = 16,\n",
+    "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
+    "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
+    "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
+    "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
+    "    random_state = 3407,\n",
+    "    use_rslora = False,  # We support rank stabilized LoRA\n",
+    "    loftq_config = None, # And LoftQ\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3b8cd54e-88de-4247-8788-e068b7239555",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      },
+      "text/plain": [
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+      ]
+     },
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+    },
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+    {
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90be502cdd05496b9a3b6e04bd63e1f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "train-039-of-040.parquet:   0%|          | 0.00/30.3M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79ca9b10f64d44c6af9810930555deda",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split:   0%|          | 0/394995 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Processing split: train\n",
+      "Number of examples in train: 394995\n",
+      "Processed 0 examples\n",
+      "Processed 1000 examples\n",
+      "Processed 2000 examples\n",
+      "Processed 3000 examples\n",
+      "Processed 4000 examples\n",
+      "Processed 5000 examples\n",
+      "Processed 6000 examples\n",
+      "Processed 7000 examples\n",
+      "Processed 8000 examples\n",
+      "Processed 9000 examples\n",
+      "Conversion complete for train, processed 10000 examples\n",
+      "\n",
+      "Example of accessing converted data:\n",
+      "Dataset[0]['text']:\n",
+      "<|im_start|>system\n",
+      "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n",
+      "<|im_start|>user\n",
+      "The cost of five pencils and one pen is $\\$2.50$, and the cost of one pencil and two pens is $\\$1.85$. What is the cost of two pencils and one pen?<|im_end|>\n",
+      "<|im_start|>assistant\n",
+      "<think>\n",
+      "Alright, let's try to solve this problem step by step. So, we have two equations here:\n",
+      "\n",
+      "1. The cost of five pencils and one pen is $2.50.\n",
+      "2. The cost of one pencil and two pens is $1.85.\n",
+      "\n",
+      "We need t...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c4bfda39749415a9c1b3a330cf5365b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Saving the dataset (0/1 shards):   0%|          | 0/10000 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved train dataset to disk as 'converted_train'\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset, Dataset\n",
+    "import json\n",
+    "\n",
+    "def convert_format(messages):\n",
+    "    \"\"\"\n",
+    "    Convert a single entry from the 'messages' column to the target format.\n",
+    "    \"\"\"\n",
+    "    # Create the target format with system prompt\n",
+    "    target_format = '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n'\n",
+    "\n",
+    "    # Add each message\n",
+    "    for message in messages:\n",
+    "        role = message.get('role')\n",
+    "        content = message.get('content')\n",
+    "\n",
+    "        if role and content:\n",
+    "            # Add formatted message\n",
+    "            target_format += f'<|im_start|>{role}\\n{content}<|im_end|>\\n'\n",
+    "\n",
+    "    return target_format\n",
+    "\n",
+    "def process_dataset():\n",
+    "    # Load the dataset from Hugging Face\n",
+    "    dataset = load_dataset(\"oumi-ai/MetaMathQA-R1\")\n",
+    "\n",
+    "    # Create new datasets dict to store the converted data\n",
+    "    converted_datasets = {}\n",
+    "\n",
+    "    # Process each split in the dataset\n",
+    "    for split_name in dataset.keys():\n",
+    "        print(f\"\\nProcessing split: {split_name}\")\n",
+    "        split = dataset[split_name]\n",
+    "\n",
+    "        # Check if 'messages' column exists in this split\n",
+    "        if 'messages' in split.column_names:\n",
+    "            messages_list = split['messages']\n",
+    "            print(f\"Number of examples in {split_name}: {len(messages_list)}\")\n",
+    "\n",
+    "            # Get the first 10000 examples (or all if less than 10000)\n",
+    "            num_examples = min(10000, len(messages_list))\n",
+    "\n",
+    "            # Convert each messages entry and store in a list\n",
+    "            converted_texts = []\n",
+    "            for i in range(num_examples):\n",
+    "                # Convert this entry to the target format\n",
+    "                converted = convert_format(messages_list[i])\n",
+    "                converted_texts.append(converted)\n",
+    "\n",
+    "                # Print progress\n",
+    "                if i % 1000 == 0:\n",
+    "                    print(f\"Processed {i} examples\")\n",
+    "\n",
+    "            # Create a new dataset with a 'text' column containing the converted data\n",
+    "            converted_dataset = Dataset.from_dict({\"text\": converted_texts})\n",
+    "            converted_datasets[split_name] = converted_dataset\n",
+    "\n",
+    "            print(f\"Conversion complete for {split_name}, processed {num_examples} examples\")\n",
+    "        else:\n",
+    "            print(f\"'messages' column not found in {split_name} split\")\n",
+    "\n",
+    "    return converted_datasets\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    # Process the dataset and get the converted datasets\n",
+    "    converted_data = process_dataset()\n",
+    "\n",
+    "    # Example of accessing an element (if 'train' split exists)\n",
+    "    if 'train' in converted_data:\n",
+    "        print(\"\\nExample of accessing converted data:\")\n",
+    "        print(\"Dataset[0]['text']:\")\n",
+    "        print(converted_data['train'][0]['text'][:500] + \"...\")  # Show first 500 chars\n",
+    "\n",
+    "    # You can save the datasets if needed\n",
+    "    for split_name, dataset in converted_data.items():\n",
+    "        dataset.save_to_disk(f\"converted_{split_name}\")\n",
+    "        print(f\"Saved {split_name} dataset to disk as 'converted_{split_name}'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7117b4c2-1dde-4b46-bd51-16bba68b4c44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c71365ef63df431fbf20b5838703cad9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Unsloth: Tokenizing [\"text\"] (num_proc=2):   0%|          | 0/10000 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from trl import SFTTrainer\n",
+    "from transformers import TrainingArguments, DataCollatorForSeq2Seq\n",
+    "from unsloth import is_bfloat16_supported\n",
+    "\n",
+    "trainer = SFTTrainer(\n",
+    "    model = model,\n",
+    "    tokenizer = tokenizer,\n",
+    "    train_dataset = dataset,\n",
+    "    dataset_text_field = \"text\",\n",
+    "    max_seq_length = max_seq_length,\n",
+    "    data_collator = DataCollatorForSeq2Seq(tokenizer = tokenizer),\n",
+    "    dataset_num_proc = 2,\n",
+    "    packing = False, # Can make training 5x faster for short sequences.\n",
+    "    args = TrainingArguments(\n",
+    "        per_device_train_batch_size = 16,\n",
+    "        gradient_accumulation_steps = 4,\n",
+    "        warmup_steps = 100,\n",
+    "        num_train_epochs = 6, # Set this for 1 full training run.\n",
+    "        #max_steps = 60,\n",
+    "        learning_rate = 2e-4,\n",
+    "        fp16 = not is_bfloat16_supported(),\n",
+    "        bf16 = is_bfloat16_supported(),\n",
+    "        logging_steps = 50,\n",
+    "        save_steps = 100,  # Added checkpoint saving\n",
+    "        optim = \"adamw_8bit\",\n",
+    "        weight_decay = 0.01,\n",
+    "        lr_scheduler_type = \"linear\",\n",
+    "        seed = 3407,\n",
+    "        output_dir = \"outputs\",\n",
+    "        report_to = \"none\", # Use this for WandB etc\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "20ed86b5-5945-487e-a538-b0adc6a52999",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b31f5620d6a54b3eb6b5933c66834c4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map (num_proc=128):   0%|          | 0/10000 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from unsloth.chat_templates import train_on_responses_only\n",
+    "trainer = train_on_responses_only(\n",
+    "    trainer,\n",
+    "    instruction_part = \"<|im_start|>user\\n\",\n",
+    "    response_part = \"<|im_start|>assistant\\n\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "93d21670-3de4-473f-872f-810e77f80d21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n<|im_start|>user\\nThe cost of five pencils and one pen is $\\\\$2.50$, and the cost of one pencil and two pens is $\\\\$1.85$. What is the cost of two pencils and one pen?<|im_end|>\\n<|im_start|>assistant\\n<think>\\nAlright, let\\'s try to solve this problem step by step. So, we have two equations here:\\n\\n1. The cost of five pencils and one pen is $2.50.\\n2. The cost of one pencil and two pens is $1.85.\\n\\nWe need to find the cost of two pencils and one pen. Hmm, okay. So first off, let me assign variables to these things to make it easier. Let\\'s say the cost of one pencil is \"p\" dollars, and the cost of one pen is \"n\" dollars. Then we can translate the given information into equations.\\n\\nFirst equation: Five pencils and one pen cost $2.50. That would be 5 times p plus 1 times n equals 2.50. So, mathematically, that\\'s:\\n\\n5p + n = 2.50\\n\\nSecond equation: One pencil and two pens cost $1.85. That translates to 1 times p plus 2 times n equals 1.85. So:\\n\\np + 2n = 1.85\\n\\nOkay, so now we have a system of two equations with two variables. We need to solve for p and n, and once we have those, we can figure out what 2 pencils and 1 pen would cost by calculating 2p + n.\\n\\nSo, let me think about how to solve these equations. There are a couple of methods: substitution and elimination. Which one would be easier here?\\n\\nLooking at the two equations:\\n\\n1) 5p + n = 2.50  \\n2) p + 2n = 1.85\\n\\nMaybe substitution would work. Let\\'s see. Maybe if we solve one of the equations for one variable and substitute into the other. Let\\'s take the first equation, maybe solve for n.\\n\\nFrom equation 1:  \\n5p + n = 2.50  \\nSo, subtract 5p from both sides:  \\nn = 2.50 - 5p\\n\\nNow, substitute this expression for n into equation 2. Equation 2 is p + 2n = 1.85. Substituting:\\n\\np + 2*(2.50 - 5p) = 1.85\\n\\nLet me compute that. Multiply out the 2:\\n\\np + 5.00 - 10p = 1.85\\n\\nCombine like terms (p - 10p):  \\n-9p + 5.00 = 1.85\\n\\nNow, subtract 5.00 from both sides:\\n\\n-9p = 1.85 - 5.00  \\nWhich is:\\n\\n-9p = -3.15\\n\\nDivide both sides by -9:\\n\\np = (-3.15)/(-9) = 3.15 / 9\\n\\nHmm, let me do that division. 3.15 divided by 9. Let\\'s see, 9 goes into 3.15 how many times?\\n\\nWell, 9 x 0.35 is 3.15. Because 9 x 0.30 = 2.70, and 9 x 0.05 = 0.45, so 2.70 + 0.45 = 3.15. Yup. So, p = 0.35. So each pencil is 35 cents.\\n\\nNow, substitute p = 0.35 back into our expression for n. Earlier, we had n = 2.50 - 5p. Let\\'s plug that in:\\n\\nn = 2.50 - 5*(0.35)  \\nCalculate 5*0.35: that\\'s 1.75  \\nSo:\\n\\nn = 2.50 - 1.75  \\nWhich is 0.75. So, a pen is 75 cents.\\n\\nLet me check to make sure these values satisfy both equations.\\n\\nFirst equation: 5 pencils and 1 pen. 5*0.35 is 1.75, plus 0.75 is 2.50. That checks out. Second equation: 1 pencil is 0.35, 2 pens is 0.75*2=1.50. Together, 0.35 + 1.50 = 1.85, which matches the second equation. So, p = $0.35 and n = $0.75 are correct.\\n\\nNow, the question asks for the cost of two pencils and one pen. So, 2p + n.\\n\\nLet\\'s compute that: 2*0.35 + 0.75. That is 0.70 + 0.75 = 1.45. So, the answer should be $1.45.\\n\\nWait, let\\'s make sure we didn\\'t make any arithmetic mistakes. Let\\'s retrace:\\n\\n1. Assigned variables: p = pencil, n = pen.\\n\\n2. Equations: 5p + n = 2.50 and p + 2n = 1.85.\\n\\n3. Solved first equation for n: n = 2.50 -5p.\\n\\n4. Substituted into second equation: p + 2*(2.50 -5p) = 1.85.\\n\\n5. Calculated that as p + 5.00 -10p =1.85, simplifies to -9p +5.00 =1.85.\\n\\n6. Subtract 5: -9p = -3.15, divide by -9: p=0.35.\\n\\n7. Then n = 2.50 -5*0.35=2.50 -1.75=0.75.\\n\\n8. Cost for two pencils and one pen: 2*0.35 +0.75=0.70+0.75=1.45.\\n\\nYes, that all seems correct.\\n\\nAlternatively, we can test using elimination to see if we get the same result.\\n\\nLet me try the elimination method as a check. Maybe multiply the second equation by five:\\n\\nEquation 1: 5p + n = 2.50  \\nEquation 2: p + 2n = 1.85\\n\\nMultiply equation 2 by 5:\\n\\n5p + 10n = 9.25\\n\\nNow subtract equation 1 from this new equation:\\n\\n(5p + 10n) - (5p +n) = 9.25 -2.50  \\nWhich is 5p +10n -5p -n = 6.75  \\nSimplifies to 9n =6.75  \\nThus, n=6.75 /9=0.75.\\n\\nSo, n=0.75. Then substitute back into equation2: p +2*0.75=1.85  \\np +1.50=1.85  \\np=1.85 -1.50=0.35.\\n\\nSame results. So, that reaffirms p=0.35 and n=0.75. So, 2p +n=0.70 +0.75=1.45. Therefore, the answer is indeed $1.45. So written in dollars, that\\'s \\\\$1.45. Hence, the cost of two pencils and one pen is \\\\$1.45.\\n\\nI can see that both substitution and elimination methods give the same answer. So, unless there\\'s a miscalculation that I\\'m missing, the answer should be \\\\$1.45. Given that cross-verification hasn\\'t shown any problems, I think that is correct.\\n\\n**Final Answer**\\n\\\\boxed{1.45}\\n</think>\\n\\nLet \\\\( p \\\\) be the cost of one pencil in dollars and \\\\( n \\\\) be the cost of one pen in dollars. We are given the following system of equations:\\n\\n1. \\\\( 5p + n = 2.50 \\\\)\\n2. \\\\( p + 2n = 1.85 \\\\)\\n\\nFirst, solve the first equation for \\\\( n \\\\):\\n\\\\[\\nn = 2.50 - 5p\\n\\\\]\\n\\nSubstitute this expression for \\\\( n \\\\) into the second equation:\\n\\\\[\\np + 2(2.50 - 5p) = 1.85\\n\\\\]\\nSimplify and solve for \\\\( p \\\\):\\n\\\\[\\np + 5.00 - 10p = 1.85 \\\\\\\\\\n-9p + 5.00 = 1.85 \\\\\\\\\\n-9p = 1.85 - 5.00 \\\\\\\\\\n-9p = -3.15 \\\\\\\\\\np = \\\\frac{-3.15}{-9} = 0.35\\n\\\\]\\n\\nNow substitute \\\\( p = 0.35 \\\\) back into the expression for \\\\( n \\\\):\\n\\\\[\\nn = 2.50 - 5(0.35) \\\\\\\\\\nn = 2.50 - 1.75 \\\\\\\\\\nn = 0.75\\n\\\\]\\n\\nWe need to find the cost of two pencils and one pen, which is \\\\( 2p + n \\\\):\\n\\\\[\\n2(0.35) + 0.75 = 0.70 + 0.75 = 1.45\\n\\\\]\\n\\nThus, the cost of two pencils and one pen is \\\\(\\\\boxed{1.45}\\\\).<|im_end|>\\n'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer.decode(trainer.train_dataset[0][\"input_ids\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0625d8df-5034-4597-8650-c06530aabc22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'                                                                         <think>\\nAlright, let\\'s try to solve this problem step by step. So, we have two equations here:\\n\\n1. The cost of five pencils and one pen is $2.50.\\n2. The cost of one pencil and two pens is $1.85.\\n\\nWe need to find the cost of two pencils and one pen. Hmm, okay. So first off, let me assign variables to these things to make it easier. Let\\'s say the cost of one pencil is \"p\" dollars, and the cost of one pen is \"n\" dollars. Then we can translate the given information into equations.\\n\\nFirst equation: Five pencils and one pen cost $2.50. That would be 5 times p plus 1 times n equals 2.50. So, mathematically, that\\'s:\\n\\n5p + n = 2.50\\n\\nSecond equation: One pencil and two pens cost $1.85. That translates to 1 times p plus 2 times n equals 1.85. So:\\n\\np + 2n = 1.85\\n\\nOkay, so now we have a system of two equations with two variables. We need to solve for p and n, and once we have those, we can figure out what 2 pencils and 1 pen would cost by calculating 2p + n.\\n\\nSo, let me think about how to solve these equations. There are a couple of methods: substitution and elimination. Which one would be easier here?\\n\\nLooking at the two equations:\\n\\n1) 5p + n = 2.50  \\n2) p + 2n = 1.85\\n\\nMaybe substitution would work. Let\\'s see. Maybe if we solve one of the equations for one variable and substitute into the other. Let\\'s take the first equation, maybe solve for n.\\n\\nFrom equation 1:  \\n5p + n = 2.50  \\nSo, subtract 5p from both sides:  \\nn = 2.50 - 5p\\n\\nNow, substitute this expression for n into equation 2. Equation 2 is p + 2n = 1.85. Substituting:\\n\\np + 2*(2.50 - 5p) = 1.85\\n\\nLet me compute that. Multiply out the 2:\\n\\np + 5.00 - 10p = 1.85\\n\\nCombine like terms (p - 10p):  \\n-9p + 5.00 = 1.85\\n\\nNow, subtract 5.00 from both sides:\\n\\n-9p = 1.85 - 5.00  \\nWhich is:\\n\\n-9p = -3.15\\n\\nDivide both sides by -9:\\n\\np = (-3.15)/(-9) = 3.15 / 9\\n\\nHmm, let me do that division. 3.15 divided by 9. Let\\'s see, 9 goes into 3.15 how many times?\\n\\nWell, 9 x 0.35 is 3.15. Because 9 x 0.30 = 2.70, and 9 x 0.05 = 0.45, so 2.70 + 0.45 = 3.15. Yup. So, p = 0.35. So each pencil is 35 cents.\\n\\nNow, substitute p = 0.35 back into our expression for n. Earlier, we had n = 2.50 - 5p. Let\\'s plug that in:\\n\\nn = 2.50 - 5*(0.35)  \\nCalculate 5*0.35: that\\'s 1.75  \\nSo:\\n\\nn = 2.50 - 1.75  \\nWhich is 0.75. So, a pen is 75 cents.\\n\\nLet me check to make sure these values satisfy both equations.\\n\\nFirst equation: 5 pencils and 1 pen. 5*0.35 is 1.75, plus 0.75 is 2.50. That checks out. Second equation: 1 pencil is 0.35, 2 pens is 0.75*2=1.50. Together, 0.35 + 1.50 = 1.85, which matches the second equation. So, p = $0.35 and n = $0.75 are correct.\\n\\nNow, the question asks for the cost of two pencils and one pen. So, 2p + n.\\n\\nLet\\'s compute that: 2*0.35 + 0.75. That is 0.70 + 0.75 = 1.45. So, the answer should be $1.45.\\n\\nWait, let\\'s make sure we didn\\'t make any arithmetic mistakes. Let\\'s retrace:\\n\\n1. Assigned variables: p = pencil, n = pen.\\n\\n2. Equations: 5p + n = 2.50 and p + 2n = 1.85.\\n\\n3. Solved first equation for n: n = 2.50 -5p.\\n\\n4. Substituted into second equation: p + 2*(2.50 -5p) = 1.85.\\n\\n5. Calculated that as p + 5.00 -10p =1.85, simplifies to -9p +5.00 =1.85.\\n\\n6. Subtract 5: -9p = -3.15, divide by -9: p=0.35.\\n\\n7. Then n = 2.50 -5*0.35=2.50 -1.75=0.75.\\n\\n8. Cost for two pencils and one pen: 2*0.35 +0.75=0.70+0.75=1.45.\\n\\nYes, that all seems correct.\\n\\nAlternatively, we can test using elimination to see if we get the same result.\\n\\nLet me try the elimination method as a check. Maybe multiply the second equation by five:\\n\\nEquation 1: 5p + n = 2.50  \\nEquation 2: p + 2n = 1.85\\n\\nMultiply equation 2 by 5:\\n\\n5p + 10n = 9.25\\n\\nNow subtract equation 1 from this new equation:\\n\\n(5p + 10n) - (5p +n) = 9.25 -2.50  \\nWhich is 5p +10n -5p -n = 6.75  \\nSimplifies to 9n =6.75  \\nThus, n=6.75 /9=0.75.\\n\\nSo, n=0.75. Then substitute back into equation2: p +2*0.75=1.85  \\np +1.50=1.85  \\np=1.85 -1.50=0.35.\\n\\nSame results. So, that reaffirms p=0.35 and n=0.75. So, 2p +n=0.70 +0.75=1.45. Therefore, the answer is indeed $1.45. So written in dollars, that\\'s \\\\$1.45. Hence, the cost of two pencils and one pen is \\\\$1.45.\\n\\nI can see that both substitution and elimination methods give the same answer. So, unless there\\'s a miscalculation that I\\'m missing, the answer should be \\\\$1.45. Given that cross-verification hasn\\'t shown any problems, I think that is correct.\\n\\n**Final Answer**\\n\\\\boxed{1.45}\\n</think>\\n\\nLet \\\\( p \\\\) be the cost of one pencil in dollars and \\\\( n \\\\) be the cost of one pen in dollars. We are given the following system of equations:\\n\\n1. \\\\( 5p + n = 2.50 \\\\)\\n2. \\\\( p + 2n = 1.85 \\\\)\\n\\nFirst, solve the first equation for \\\\( n \\\\):\\n\\\\[\\nn = 2.50 - 5p\\n\\\\]\\n\\nSubstitute this expression for \\\\( n \\\\) into the second equation:\\n\\\\[\\np + 2(2.50 - 5p) = 1.85\\n\\\\]\\nSimplify and solve for \\\\( p \\\\):\\n\\\\[\\np + 5.00 - 10p = 1.85 \\\\\\\\\\n-9p + 5.00 = 1.85 \\\\\\\\\\n-9p = 1.85 - 5.00 \\\\\\\\\\n-9p = -3.15 \\\\\\\\\\np = \\\\frac{-3.15}{-9} = 0.35\\n\\\\]\\n\\nNow substitute \\\\( p = 0.35 \\\\) back into the expression for \\\\( n \\\\):\\n\\\\[\\nn = 2.50 - 5(0.35) \\\\\\\\\\nn = 2.50 - 1.75 \\\\\\\\\\nn = 0.75\\n\\\\]\\n\\nWe need to find the cost of two pencils and one pen, which is \\\\( 2p + n \\\\):\\n\\\\[\\n2(0.35) + 0.75 = 0.70 + 0.75 = 1.45\\n\\\\]\\n\\nThus, the cost of two pencils and one pen is \\\\(\\\\boxed{1.45}\\\\).<|im_end|>\\n'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "space = tokenizer(\" \", add_special_tokens = False).input_ids[0]\n",
+    "tokenizer.decode([space if x == -100 else x for x in trainer.train_dataset[0][\"labels\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0e8c8bb6-0907-4106-a3cb-94c8b3c6da1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
+      "   \\\\   /|    Num examples = 10,000 | Num Epochs = 6 | Total steps = 936\n",
+      "O^O/ \\_/ \\    Batch size per device = 16 | Gradient accumulation steps = 4\n",
+      "\\        /    Data Parallel GPUs = 1 | Total batch size (16 x 4 x 1) = 64\n",
+      " \"-____-\"     Trainable parameters = 36,929,536/1,580,643,840 (2.34% trained)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unsloth: Will smartly offload gradients to save VRAM!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='936' max='936' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [936/936 7:52:53, Epoch 5/6]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Step</th>\n",
+       "      <th>Training Loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>50</td>\n",
+       "      <td>1.074000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>100</td>\n",
+       "      <td>0.914600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>150</td>\n",
+       "      <td>0.853400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>200</td>\n",
+       "      <td>0.823500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>250</td>\n",
+       "      <td>0.826000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>300</td>\n",
+       "      <td>0.831000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>350</td>\n",
+       "      <td>0.811000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>400</td>\n",
+       "      <td>0.805300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>450</td>\n",
+       "      <td>0.803300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>500</td>\n",
+       "      <td>0.790300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>550</td>\n",
+       "      <td>0.783200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>600</td>\n",
+       "      <td>0.784600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>650</td>\n",
+       "      <td>0.784000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>700</td>\n",
+       "      <td>0.771100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>750</td>\n",
+       "      <td>0.767800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>800</td>\n",
+       "      <td>0.773000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>850</td>\n",
+       "      <td>0.768000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>900</td>\n",
+       "      <td>0.756900</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer_stats = trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5fb9b55-1819-44da-ba3a-1a6bc976b80b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import login\n",
+    "login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "51fe7a4e-a22f-4809-883c-811d2799bb42",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('lora_model/tokenizer_config.json',\n",
+       " 'lora_model/special_tokens_map.json',\n",
+       " 'lora_model/vocab.json',\n",
+       " 'lora_model/merges.txt',\n",
+       " 'lora_model/added_tokens.json',\n",
+       " 'lora_model/tokenizer.json')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.save_pretrained(\"lora_model\") # Local saving\n",
+    "tokenizer.save_pretrained(\"lora_model\")\n",
+    "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
+    "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8a1585a-5acd-4571-a291-3db9a04d826d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python3 (ipykernel)",
+   "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
+}

Untitled1.ipynb

@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c3c1c0c3-95ce-40fd-aaaf-56427df1b82c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3aad272b2d9843d3b374d501e3f86504",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from huggingface_hub import login\n",
+    "login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5dcb236-b4d3-4d59-8eb3-9f7e17abaf91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import HfApi\n",
+    "api = HfApi()\n",
+    "\n",
+    "api.upload_folder(\n",
+    "    folder_path=\"\",\n",
+    "    repo_id=\"kevinwang676/Qwen2.5-1.5B-Distillation\",\n",
+    "    repo_type=\"model\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a085135f-2459-4cd9-b1a4-7d37c01e64c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'model' 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[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241m.\u001b[39msave_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlora_model\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;66;03m# Local saving\u001b[39;00m\n\u001b[1;32m      2\u001b[0m tokenizer\u001b[38;5;241m.\u001b[39msave_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlora_model\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\u001b[39;00m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "model.save_pretrained(\"lora_model\") # Local saving\n",
+    "tokenizer.save_pretrained(\"lora_model\")\n",
+    "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
+    "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fac7f828-b1cd-466a-9e1a-251a74d6847c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python3 (ipykernel)",
+   "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
+}

converted_train/data-00000-of-00001.arrow

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:334d9238a8db475c5357f433808c6a65bb791c18300aba7c6a8a98e16fcaa735
+size 64396832

converted_train/dataset_info.json

@@ -0,0 +1,12 @@
+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "text": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

converted_train/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "fdc222bf7b68dfbe",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}

lora_model/README.md

@@ -0,0 +1,202 @@
+---
+base_model: unsloth/Qwen2.5-1.5B-Instruct
+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 0.14.0

lora_model/adapter_config.json

@@ -0,0 +1,37 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/Qwen2.5-1.5B-Instruct",
+  "bias": "none",
+  "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": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "down_proj",
+    "up_proj",
+    "o_proj",
+    "gate_proj",
+    "v_proj",
+    "k_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

lora_model/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a9066b4abf68c6ba235ecb2860df0e9e77d30e17503b0d0f794f6b431c8390f3
+size 147770496

lora_model/added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
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+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
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+  "<|im_start|>": 151644,
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+  "<|object_ref_end|>": 151647,
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+  "<|quad_end|>": 151651,
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+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

lora_model/special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
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+    "single_word": false
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+    "content": "<|vision_pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

lora_model/tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

lora_model/tokenizer_config.json

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+    "151660": {
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+    "151661": {
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+    "151663": {
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+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 32768,
+  "pad_token": "<|vision_pad|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

onstart.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+# This file is run on instance start. Output in /var/log/onstart.log
+

outputs/checkpoint-100/README.md

@@ -0,0 +1,202 @@
+---
+base_model: unsloth/Qwen2.5-1.5B-Instruct
+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 0.14.0

outputs/checkpoint-100/adapter_config.json

@@ -0,0 +1,37 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/Qwen2.5-1.5B-Instruct",
+  "bias": "none",
+  "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": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "down_proj",
+    "up_proj",
+    "o_proj",
+    "gate_proj",
+    "v_proj",
+    "k_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

outputs/checkpoint-100/adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6350b2115aca57a874418662d7e86c1f87b153aad2e3171c836f6fafbf711fa
+size 147770496

outputs/checkpoint-100/added_tokens.json

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+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

outputs/checkpoint-100/optimizer.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0b42a68b41d97f1fe9c3c3ac09331e137539e00f10e03a831ee497add9d126eb
+size 75471860

outputs/checkpoint-100/rng_state.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:608fccb6c056ce88cdfd5355e6be2046f4d107a24a87c6b0d2c3b200ce6bb4ea
+size 14244

outputs/checkpoint-100/scheduler.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2e0bfbbe2be6a1399a4c175bf33e1a680e875704102055b2bbfcb74670a4c677
+size 1064

outputs/checkpoint-100/special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|vision_pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

outputs/checkpoint-100/tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896