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Qwen2.5-1.5B-Distillation-r-32

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Qwen2.5-1.5B-Distillation-r-32
File size: 3,293 Bytes 
94d23fe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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{
 "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
}