test2.7b.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/thesby/anaconda3/envs/qwen_2.7b_vl/lib/python3.10/site-packages/transformers/modeling_utils.py:4256: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m device \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# the device to load the model onto\u001b[39;00m\n\u001b[1;32m      4\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/home/thesby/projects/pretrained_models/LLM/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 5\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mAutoModelForCausalLM\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtorch_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mauto\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mauto\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m      9\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(path)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mchat\u001b[39m(prompt):\n",
      "File \u001b[0;32m~/anaconda3/envs/qwen_2.7b_vl/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py:563\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m    561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(config) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    562\u001b[0m     model_class \u001b[38;5;241m=\u001b[39m _get_model_class(config, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping)\n\u001b[0;32m--> 563\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    564\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    565\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    566\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    567\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    568\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(c\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    569\u001b[0m )\n",
      "File \u001b[0;32m~/anaconda3/envs/qwen_2.7b_vl/lib/python3.10/site-packages/transformers/modeling_utils.py:3494\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   3489\u001b[0m     logger\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m   3490\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis model has some weights that should be kept in higher precision, you need to upgrade \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3491\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`accelerate` to properly deal with them (`pip install --upgrade accelerate`).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3492\u001b[0m     )\n\u001b[1;32m   3493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m device_map \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msequential\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 3494\u001b[0m     max_memory \u001b[38;5;241m=\u001b[39m \u001b[43mget_balanced_memory\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3496\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlow_zero\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbalanced_low_0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3498\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_memory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_memory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3499\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdevice_map_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3500\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3501\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   3502\u001b[0m     max_memory \u001b[38;5;241m=\u001b[39m get_max_memory(max_memory)\n",
      "File \u001b[0;32m~/anaconda3/envs/qwen_2.7b_vl/lib/python3.10/site-packages/accelerate/utils/modeling.py:1004\u001b[0m, in \u001b[0;36mget_balanced_memory\u001b[0;34m(model, max_memory, no_split_module_classes, dtype, special_dtypes, low_zero)\u001b[0m\n\u001b[1;32m   1001\u001b[0m     buffer \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1003\u001b[0m \u001b[38;5;66;03m# Compute mean of final modules. In the first dict of module sizes, leaves are the parameters\u001b[39;00m\n\u001b[0;32m-> 1004\u001b[0m leaves \u001b[38;5;241m=\u001b[39m [n \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m module_sizes \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m([p \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m module_sizes \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m p\u001b[38;5;241m.\u001b[39mstartswith(n \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)]) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1005\u001b[0m module_sizes \u001b[38;5;241m=\u001b[39m {n: v \u001b[38;5;28;01mfor\u001b[39;00m n, v \u001b[38;5;129;01min\u001b[39;00m module_sizes\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m leaves}\n\u001b[1;32m   1006\u001b[0m \u001b[38;5;66;03m# Once removed, leaves are the final modules.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/qwen_2.7b_vl/lib/python3.10/site-packages/accelerate/utils/modeling.py:1004\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1001\u001b[0m     buffer \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1003\u001b[0m \u001b[38;5;66;03m# Compute mean of final modules. In the first dict of module sizes, leaves are the parameters\u001b[39;00m\n\u001b[0;32m-> 1004\u001b[0m leaves \u001b[38;5;241m=\u001b[39m [n \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m module_sizes \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m([p \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m module_sizes \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m p\u001b[38;5;241m.\u001b[39mstartswith(n \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)]) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1005\u001b[0m module_sizes \u001b[38;5;241m=\u001b[39m {n: v \u001b[38;5;28;01mfor\u001b[39;00m n, v \u001b[38;5;129;01min\u001b[39;00m module_sizes\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m leaves}\n\u001b[1;32m   1006\u001b[0m \u001b[38;5;66;03m# Once removed, leaves are the final modules.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/qwen_2.7b_vl/lib/python3.10/site-packages/accelerate/utils/modeling.py:1004\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1001\u001b[0m     buffer \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1003\u001b[0m \u001b[38;5;66;03m# Compute mean of final modules. In the first dict of module sizes, leaves are the parameters\u001b[39;00m\n\u001b[0;32m-> 1004\u001b[0m leaves \u001b[38;5;241m=\u001b[39m [n \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m module_sizes \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m([p \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m module_sizes \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstartswith\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m]) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1005\u001b[0m module_sizes \u001b[38;5;241m=\u001b[39m {n: v \u001b[38;5;28;01mfor\u001b[39;00m n, v \u001b[38;5;129;01min\u001b[39;00m module_sizes\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m leaves}\n\u001b[1;32m   1006\u001b[0m \u001b[38;5;66;03m# Once removed, leaves are the final modules.\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "device = \"cuda\" # the device to load the model onto\n",
    "\n",
    "path = '/home/thesby/projects/pretrained_models/LLM/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4'\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    path,\n",
    "    torch_dtype=\"auto\",\n",
    "    device_map=\"auto\"\n",
    ")\n",
    "tokenizer = AutoTokenizer.from_pretrained(path)\n",
    "\n",
    "\n",
    "def chat(prompt):\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
    "        {\"role\": \"user\", \"content\": prompt}\n",
    "    ]\n",
    "    text = tokenizer.apply_chat_template(\n",
    "        messages,\n",
    "        tokenize=False,\n",
    "        add_generation_prompt=True\n",
    "    )\n",
    "    model_inputs = tokenizer([text], return_tensors=\"pt\").to(device)\n",
    "\n",
    "    generated_ids = model.generate(\n",
    "        model_inputs.input_ids,\n",
    "        max_new_tokens=512\n",
    "    )\n",
    "    generated_ids = [\n",
    "        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)\n",
    "    ]\n",
    "\n",
    "    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
    "    return response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'我是来自阿里云的超大规模语言模型，我叫通义千问。作为一个AI助手，我的目标是帮助用户获得准确、有用的信息，解决他们的问题和困惑。我可以回答各种问题，提供知识性、咨询性的信息，还可以进行多轮对话，并且能够根据上下文进行推理和理解。我会不断学习和进步，不断提升自己的能力，为用户提供更好的服务。如果您有任何问题或需要帮助，请随时告诉我，我会尽力回答您的问题。'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt = \"介绍一下你自己\"\n",
    "chat(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "根据您的描述，这款产品的品牌是海尔，型号是KT039，功率为800瓦，材质为不锈钢，适合在宿舍使用。关于颜色和尺码信息没有明确说明，所以空白。\n"
     ]
    }
   ],
   "source": [
    "prompt = '''从下列标题中提取信息，包括但不限于：品牌名、型号、功率、颜色、尺码、材质。如果没有相关信息，就置空\n",
    "海尔KT039不锈钢电热水壶800W宿舍可用'''\n",
    "print(chat(prompt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Qwen2TokenizerFast(name_or_path='/home/thesby/projects/pretrained_models/LLM/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4', vocab_size=151643, model_max_length=32768, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'eos_token': '<|im_end|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>']}, clean_up_tokenization_spaces=False),  added_tokens_decoder={\n",
       "\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [105492, 104773], 'attention_mask': [1, 1]}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer('中华人民共和国')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer2 = AutoTokenizer.from_pretrained('/home/thesby/projects/pretrained_models/LLM/Qwen-VL-Chat-Int4', trust_remote_code=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QWenTokenizer(name_or_path='/home/thesby/projects/pretrained_models/LLM/Qwen-VL-Chat-Int4', vocab_size=151860, model_max_length=8192, is_fast=False, padding_side='right', truncation_side='right', special_tokens={}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
       "\t\n",
       "}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [105492, 104773], 'token_type_ids': [0, 0], 'attention_mask': [1, 1]}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer2('中华人民共和国')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\n",
      "You are a helpful assistant<|im_end|>\n",
      "' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n",
      "' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\n",
      "'}}{% endif %}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\n",
      "' }}{% endif %}\n"
     ]
    }
   ],
   "source": [
    "print(tokenizer.chat_template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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