4.medical_chat_Breeze_7B_Instruct_v1_0.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "de53995b-32ed-4722-8cac-ba104c8efacb",
      "metadata": {
        "id": "de53995b-32ed-4722-8cac-ba104c8efacb"
      },
      "source": [
        "# 導入環境"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "d6e9hRKMWhvB",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6e9hRKMWhvB",
        "outputId": "bc0708de-ccd8-47a3-808b-bcb36f375a32"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "wget is already the newest version (1.21.2-2ubuntu1.1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n",
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          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "!pip install accelerate -U\n",
        "!pip install transformers[torch]\n",
        "!pip install peft\n",
        "!pip install pandas pyarrow\n",
        "!pip install -U bitsandbytes\n",
        "!pip install transformers datasets\n",
        "!apt-get install wget\n",
        "!pip install transformers gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "GGxhT9jNgHIA",
      "metadata": {
        "id": "GGxhT9jNgHIA"
      },
      "outputs": [],
      "source": [
        "# 載入必要套件\n",
        "import bitsandbytes\n",
        "import gc\n",
        "import os\n",
        "import pandas as pd\n",
        "import torch\n",
        "from datasets import Dataset\n",
        "from google.colab import drive, output\n",
        "from huggingface_hub import login, HfApi\n",
        "from peft import LoraConfig, TaskType, get_peft_model, PeftModel\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, Trainer, GenerationConfig, AutoModelForSequenceClassification, AdamW,  get_linear_schedule_with_warmup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "APQ7ZGmIW8Bo",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APQ7ZGmIW8Bo",
        "outputId": "e03bea7e-f929-43e3-e548-6ae6d29f9c16"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2024-08-13 01:02:27--  https://huggingface.co/datasets/ChenWeiLi/Medtext_zhtw/raw/main/MedText_zhtw.json\n",
            "Resolving huggingface.co (huggingface.co)... 18.238.109.92, 18.238.109.52, 18.238.109.121, ...\n",
            "Connecting to huggingface.co (huggingface.co)|18.238.109.92|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 983900 (961K) [text/plain]\n",
            "Saving to: ‘MedText_zhtw.json.1’\n",
            "\n",
            "MedText_zhtw.json.1 100%[===================>] 960.84K  --.-KB/s    in 0.1s    \n",
            "\n",
            "2024-08-13 01:02:27 (7.71 MB/s) - ‘MedText_zhtw.json.1’ saved [983900/983900]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#下載資料集\n",
        "!wget https://huggingface.co/datasets/ChenWeiLi/Medtext_zhtw/raw/main/MedText_zhtw.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "47dzym70yR0Y",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47dzym70yR0Y",
        "outputId": "4603282a-edf0-4b11-adcf-e0afc5842649"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "#連結到 google drive(可選)\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "WAX41berae4k",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAX41berae4k",
        "outputId": "603c7f2e-79bd-418f-8fb0-35bc767faf9d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "# 替換為你的Hugging Face API Token\n",
        "my_read_token = \"請更新成你的 read token\"\n",
        "os.environ[\"HUGGINGFACE_TOKEN\"] = my_read_token\n",
        "# 登錄Hugging Face\n",
        "login(token=os.environ[\"HUGGINGFACE_TOKEN\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "51d05e5d-d14e-4f03-92be-9a9677d41918",
      "metadata": {
        "id": "51d05e5d-d14e-4f03-92be-9a9677d41918"
      },
      "source": [
        "# 處理資料集\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "e098d9eb",
      "metadata": {
        "id": "e098d9eb"
      },
      "outputs": [],
      "source": [
        "# 將 JSON 文件轉換為 CSV 文件。\n",
        "df = pd.read_json('/content/MedText_zhtw.json' )\n",
        "ds = Dataset.from_pandas(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "74ee5a67-2e55-4974-b90e-cbf492de500a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74ee5a67-2e55-4974-b90e-cbf492de500a",
        "outputId": "f6439bbd-4bbe-44b5-8078-8b548a227238"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "model_name = \"MediaTek-Research/Breeze-7B-Instruct-v1_0\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, token=my_read_token)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "1pAT0rkBeD1J",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243,
          "referenced_widgets": [
            "3452d17557cc421796ce1ffc41402766",
            "e8fd447eef094227b683790f89700a90",
            "3b56c9fa4a244ce6af62f6020cfa88dd",
            "50dbcf56b18a406cb4a714ab59466f93",
            "a8d546dfbf124df19230cf85f57de73e",
            "f92297fa2d95451f9a2c36363e36edd7",
            "b27f85d364f34d2b97447f580e6af532",
            "0d77811ceb1545a890605fb2122c6988",
            "faf63760c5f941609ad21c7fb020ef22",
            "a8ecf9a2cdf44193bff35b0add64629d",
            "814ab9db16ac4e668c552a2a3d3f213a"
          ]
        },
        "id": "1pAT0rkBeD1J",
        "outputId": "3833013e-b339-4cbb-d56e-b2b7fad691fd"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3452d17557cc421796ce1ffc41402766",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1412 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset({\n",
            "    features: ['input_ids', 'attention_mask', 'labels'],\n",
            "    num_rows: 1412\n",
            "})\n",
            "user\n",
            "\n",
            "你是一位專業的醫療人員,請用心且專業的回答問題。一名 50 歲男性有復發性腎結石和骨質減少病史。由於先前診斷出維生素 D 缺乏症，他一直在服用大劑量的維生素 D 補充劑。實驗室結果顯示高血鈣症和高鈣尿症。可能的診斷是什麼，治療方法是什麼？assistant\n",
            "\n",
            " 該患者有復發性腎結石、骨質減少和大劑量維生素 D 補充劑病史，以及高鈣血症和高鈣尿症的實驗室檢查結果，暗示維生素 D 中毒的可能性。過量攝取維生素 D 會造成腸道對鈣的吸收增加，導致高鈣血症和高鈣尿症，及腎結石和骨質流失。治療包括停止補充維生素 D，並可能提供靜脈輸液和袢利尿劑以促進鈣的排泄。\n",
            "該患者有復發性腎結石、骨質減少和大劑量維生素 D 補充劑病史，以及高鈣血症和高鈣尿症的實驗室檢查結果，暗示維生素 D 中毒的可能性。過量攝取維生素 D 會造成腸道對鈣的吸收增加，導致高鈣血症和高鈣尿症，及腎結石和骨質流失。治療包括停止補充維生素 D，並可能提供靜脈輸液和袢利尿劑以促進鈣的排泄。\n"
          ]
        }
      ],
      "source": [
        "def process_func(example):\n",
        "    MAX_LENGTH = 384  # Llama 分詞器會將一個中文字切割成多個 token，因此需要放開一些最大長度，確保資料的完整性\n",
        "    input_ids, attention_mask, labels = [], [], []\n",
        "\n",
        "    # 構建 instruction 和 input 字符串，並進行分詞\n",
        "    instruction = tokenizer(f\"user\\n\\n{example['instruction'] + example['input']}assistant\\n\\n\", add_special_tokens=False)\n",
        "    response = tokenizer(f\"{example['output']}\", add_special_tokens=False)\n",
        "\n",
        "    # 合併 input 和 response 的 token ID 和注意力掩碼\n",
        "    input_ids = instruction[\"input_ids\"] + response[\"input_ids\"]\n",
        "    attention_mask = instruction[\"attention_mask\"] + response[\"attention_mask\"]\n",
        "    labels = [-100] * len(instruction[\"input_ids\"]) + response[\"input_ids\"]\n",
        "\n",
        "    # 如果超過最大長度，進行截斷\n",
        "    if len(input_ids) > MAX_LENGTH:\n",
        "        input_ids = input_ids[:MAX_LENGTH]\n",
        "        attention_mask = attention_mask[:MAX_LENGTH]\n",
        "        labels = labels[:MAX_LENGTH]\n",
        "\n",
        "    # 確保 attention_mask 和 labels 與 input_ids 一致\n",
        "    attention_mask = attention_mask[:len(input_ids)]\n",
        "    labels = labels[:len(input_ids)]\n",
        "\n",
        "    return {\n",
        "        \"input_ids\": input_ids,\n",
        "        \"attention_mask\": attention_mask,\n",
        "        \"labels\": labels\n",
        "    }\n",
        "\n",
        "# 應用到整個數據集\n",
        "tokenized_ds = ds.map(process_func, remove_columns=ds.column_names)\n",
        "\n",
        "# 檢查是否正確\n",
        "print(tokenized_ds)\n",
        "\n",
        "print(tokenizer.decode(tokenized_ds[0]['input_ids']))\n",
        "print(tokenizer.decode([token for token in tokenized_ds[0][\"labels\"] if token != -100]))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "424823a8-ed0d-4309-83c8-3f6b1cdf274c",
      "metadata": {
        "id": "424823a8-ed0d-4309-83c8-3f6b1cdf274c"
      },
      "source": [
        "# 創建模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "170764e5-d899-4ef4-8c53-36f6dec0d198",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500,
          "referenced_widgets": [
            "8a2f5f39302d443dac79ce840ad3541e",
            "bfd4918620814b51bc2fbe5e34050005",
            "f087082ffaeb4e95ac80f8506772266e",
            "4423502ecc3a4bcbb359ce7ea64615ee",
            "0f968cdab3fb47af9222fc6de0c8862e",
            "b706076fe59e406ab082f2a0f58f70d4",
            "3d6e317391fd4ab0979dbbdede16023d",
            "9064f351d307498dbb2721fdd7edfb84",
            "0249bb4ca2f04a8a94efbd0ebabe4967",
            "4da49a20ed514269982d023060c54dab",
            "59fb89c136234aa4b7b9b564b4f5e850"
          ]
        },
        "id": "170764e5-d899-4ef4-8c53-36f6dec0d198",
        "outputId": "218096df-c96a-4e98-b675-52de122b5d98"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a2f5f39302d443dac79ce840ad3541e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "MistralForCausalLM(\n",
              "  (model): MistralModel(\n",
              "    (embed_tokens): Embedding(61952, 4096)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x MistralDecoderLayer(\n",
              "        (self_attn): MistralSdpaAttention(\n",
              "          (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (k_proj): Linear(in_features=4096, out_features=1024, bias=False)\n",
              "          (v_proj): Linear(in_features=4096, out_features=1024, bias=False)\n",
              "          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (rotary_emb): MistralRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): MistralMLP(\n",
              "          (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
              "          (up_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
              "          (down_proj): Linear(in_features=14336, out_features=4096, bias=False)\n",
              "          (act_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): MistralRMSNorm()\n",
              "        (post_attention_layernorm): MistralRMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (norm): MistralRMSNorm()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=4096, out_features=61952, bias=False)\n",
              ")"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 重新開始加載模型\n",
        "model = AutoModelForCausalLM.from_pretrained(\"MediaTek-Research/Breeze-7B-Instruct-v1_0\", device_map=\"auto\",torch_dtype=torch.bfloat16)\n",
        "# 從檢查點載入模型- 可參考的寫法\n",
        "#model_checkpoint = '/content/drive/MyDrive/colab_results/checkpoint-38500'  # 修改為最新的檢查點路徑\n",
        "#model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
        "\n",
        "\n",
        "# 設定 pad_token_id\n",
        "model.config.pad_token_id = tokenizer.pad_token_id\n",
        "# 設定 eos_token_id\n",
        "model.config.eos_token_id = tokenizer.eos_token_id\n",
        "# 檢查是否正確\n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "2323eac7-37d5-4288-8bc5-79fac7113402",
      "metadata": {
        "id": "2323eac7-37d5-4288-8bc5-79fac7113402"
      },
      "outputs": [],
      "source": [
        "model.enable_input_require_grads() # 開啟梯度檢查點時，要執行方法"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "13d71257-3c1c-4303-8ff8-af161ebc2cf1",
      "metadata": {
        "id": "13d71257-3c1c-4303-8ff8-af161ebc2cf1"
      },
      "source": [
        "# lora"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "2d304ae2-ab60-4080-a80d-19cac2e3ade3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2d304ae2-ab60-4080-a80d-19cac2e3ade3",
        "outputId": "d79250d0-c986-4782-ab46-c58f2233aaa9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "LoraConfig(peft_type=<PeftType.LORA: 'LORA'>, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type=<TaskType.CAUSAL_LM: 'CAUSAL_LM'>, inference_mode=False, r=8, target_modules={'up_proj', 'o_proj', 'down_proj', 'gate_proj', 'v_proj', 'q_proj', 'k_proj'}, lora_alpha=32, lora_dropout=0.1, fan_in_fan_out=False, bias='none', use_rslora=False, modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={}, use_dora=False, layer_replication=None, runtime_config=LoraRuntimeConfig(ephemeral_gpu_offload=False))"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "config = LoraConfig(\n",
        "    task_type=TaskType.CAUSAL_LM,\n",
        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
        "    inference_mode=False, # 訓練模式\n",
        "    r=8, # Lora 秩\n",
        "    lora_alpha=32, # Lora alapa，具體作用參見 Lora 原理\n",
        "    lora_dropout=0.1# Dropout 比例\n",
        ")\n",
        "config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "2c2489c5-eaab-4e1f-b06a-c3f914b4bf8e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2c2489c5-eaab-4e1f-b06a-c3f914b4bf8e",
        "outputId": "176f2cd4-dc38-4f41-a2e2-e2324b69c410"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "LoraConfig(peft_type=<PeftType.LORA: 'LORA'>, auto_mapping=None, base_model_name_or_path='MediaTek-Research/Breeze-7B-Instruct-v1_0', revision=None, task_type=<TaskType.CAUSAL_LM: 'CAUSAL_LM'>, inference_mode=False, r=8, target_modules={'up_proj', 'o_proj', 'down_proj', 'gate_proj', 'v_proj', 'q_proj', 'k_proj'}, lora_alpha=32, lora_dropout=0.1, fan_in_fan_out=False, bias='none', use_rslora=False, modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={}, use_dora=False, layer_replication=None, runtime_config=LoraRuntimeConfig(ephemeral_gpu_offload=False))"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = get_peft_model(model, config)\n",
        "# 檢查是否正確\n",
        "config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "ebf5482b-fab9-4eb3-ad88-c116def4be12",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebf5482b-fab9-4eb3-ad88-c116def4be12",
        "outputId": "eff7d2e4-6b99-4734-d1d0-52ad104eb41e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 20,971,520 || all params: 7,508,070,400 || trainable%: 0.2793\n"
          ]
        }
      ],
      "source": [
        "model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ca055683-837f-4865-9c57-9164ba60c00f",
      "metadata": {
        "id": "ca055683-837f-4865-9c57-9164ba60c00f"
      },
      "source": [
        "# 配置訓練參數"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "7e76bbff-15fd-4995-a61d-8364dc5e9ea0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7e76bbff-15fd-4995-a61d-8364dc5e9ea0",
        "outputId": "097e262b-f245-454e-d54b-e19fa0bc0bd7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "torch.utils.checkpoint.use_reentrant = False\n",
        "# TODO: 之後改成使用optuna自動調整超參\n",
        "# 設定訓練參數\n",
        "args = TrainingArguments(\n",
        "    output_dir=\"/content/drive/MyDrive/colab_results\",\n",
        "    save_steps=50,  # 每50步保存一次檢查點\n",
        "    logging_dir='/content/drive/MyDrive/colab_logs',\n",
        "    per_device_train_batch_size=64,  # 增加批次大小\n",
        "    gradient_accumulation_steps=4,  # 調整累積梯度步數，使得實際批次大小達到 260K tokens\n",
        "    logging_steps=10,\n",
        "    num_train_epochs=40,\n",
        "    learning_rate=5e-5,\n",
        "    save_strategy=\"steps\",  # 每 steps 次保存一次\n",
        "    save_total_limit=5,  # 保留最多5個檢查點\n",
        "    gradient_checkpointing=True,\n",
        "    weight_decay=0.01,\n",
        ")\n",
        "\n",
        "# 設定優化器\n",
        "optimizer = AdamW(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)\n",
        "\n",
        "# 設定學習率調度器\n",
        "num_training_steps = len(tokenized_ds) // (args.per_device_train_batch_size * args.gradient_accumulation_steps) * args.num_train_epochs\n",
        "lr_scheduler = get_linear_schedule_with_warmup(\n",
        "    optimizer,\n",
        "    num_warmup_steps=int(0.1 * num_training_steps),  # 預熱步數，這裡設定為總步數的10%\n",
        "    num_training_steps=num_training_steps\n",
        ")\n",
        "\n",
        "# 創建 Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=tokenized_ds,\n",
        "    optimizers=(optimizer, lr_scheduler),  # 設定優化器和學習率調度器\n",
        "    data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),\n",
        "    tokenizer =tokenizer\n",
        ")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "aec9bc36-b297-45af-99e1-d4c4d82be081",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "aec9bc36-b297-45af-99e1-d4c4d82be081",
        "outputId": "30ca149e-c3a6-47eb-f897-7cb6c0b7c154"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='200' max='200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [200/200 1:10:39, Epoch 34/40]\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>10</td>\n",
              "      <td>2.402900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>2.031100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.795300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.585800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.373300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.187800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.998300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.816600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.655400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.523000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>0.411900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.315000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>0.237400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>0.183400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.144000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>0.109800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>0.092600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>0.075800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>0.067600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.060100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=200, training_loss=0.7533482852578163, metrics={'train_runtime': 4263.2006, 'train_samples_per_second': 13.248, 'train_steps_per_second': 0.047, 'total_flos': 4.506742038029599e+17, 'train_loss': 0.7533482852578163, 'epoch': 34.78260869565217})"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()\n",
        "\n",
        "# 繼續訓練\n",
        "#def forward_with_checkpoint(*args, **kwargs):\n",
        "#    return torch.utils.checkpoint.checkpoint(trainer.training_step, *args, use_reentrant=False, **kwargs)\n",
        "\n",
        "#trainer.training_step = forward_with_checkpoint\n",
        "#trainer.train(resume_from_checkpoint=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9823e3c7",
      "metadata": {
        "id": "9823e3c7"
      },
      "source": [
        "# 推理"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "-ZJ5L4Fh2Wrs",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ZJ5L4Fh2Wrs",
        "outputId": "e483e4f7-2971-4f42-919d-f1e1d50274e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tokenizer.pad_token = </s>\n",
            "tokenizer.eos_token = </s>\n"
          ]
        }
      ],
      "source": [
        "print(\"tokenizer.pad_token = \"+tokenizer.pad_token)\n",
        "\n",
        "print(\"tokenizer.eos_token = \"+tokenizer.eos_token)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "12dad881",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12dad881",
        "outputId": "94d98835-120d-4751-c47c-1f5aa7952cdc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Formatted text: [SYSTEM] 你是一位專業的醫療人員，請用心且專業的以三到五句話回答問題。\n",
            "[USER] 每天肚子痛是啥狀況？\n",
            "\n",
            "Generated text with chat template: <s> 你是一位專業的醫療人員，請用心且專業的以三到五句話回答問題。  [INST] 每天肚子痛是啥狀況？ [/INST] \n",
            "Model inputs: {'input_ids': tensor([[    1,     1,   259, 44845, 42695, 42364, 28914, 42594, 42407, 28924,\n",
            "         30539, 44546, 29958, 42364, 28914, 29074, 29492, 29036, 30108, 50175,\n",
            "         43188, 42191, 28944, 28705,   733, 16289, 28793, 28705, 42647, 45453,\n",
            "         34001, 28971, 36165, 42613, 29771,   733, 28748, 16289, 28793, 28705]],\n",
            "       device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], device='cuda:0')}\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:91: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generated IDs: tensor([[    1,     1,   259, 44845, 42695, 42364, 28914, 42594, 42407, 28924,\n",
            "         30539, 44546, 29958, 42364, 28914, 29074, 29492, 29036, 30108, 50175,\n",
            "         43188, 42191, 28944, 28705,   733, 16289, 28793, 28705, 42647, 45453,\n",
            "         34001, 28971, 36165, 42613, 29771,   733, 28748, 16289, 28793, 28705,\n",
            "         47932, 44403, 29355, 57707, 42223, 43429, 43981, 42672, 43181, 28914,\n",
            "         28924, 42234, 34558, 35170, 33990,   325,  6147, 28735, 28731, 29041,\n",
            "         48328, 47674, 33755, 29041, 34585, 54153, 52754, 29440, 56925, 29432,\n",
            "         42953, 28944, 42201, 42348, 42995, 28924, 42598, 43544, 29131, 43710,\n",
            "         42865, 28924, 42397, 29250, 46010, 42710, 42577, 31439, 42185, 42324,\n",
            "         42179, 30801, 44997, 42731, 28944, 31508, 43244, 42286, 44338, 42176,\n",
            "          9782, 28705, 43419, 29689, 42275, 46268, 43497, 56499, 31503, 28944,\n",
            "         50470, 43233, 44360, 42170, 12789, 30367, 30332, 42530, 43107, 52440,\n",
            "         30137, 42747, 42543, 45284, 43878, 29195, 42490, 28944,     2]],\n",
            "       device='cuda:0')\n",
            "腹部疼痛或腹痛可能是由多種原因引起的，包括腸躁症 (IBS)、急性腸胃炎、膽結石或其他胃腸道疾病。需要更多信息，包含症狀和危險因素，才能更準確評估情況並提供建議進行進一步檢查。應諮詢健康護理服務 provider 以為您個人化的指導和服務。切勿自行診斷自己——這會導致錯誤的主治醫生或者不當的大量治療。\n",
            "腹部疼痛或腹痛可能是由多種原因引起的，包括腸躁症 (IBS)、急性腸胃炎、膽結石或其他胃腸道疾病。需要更多信息，包含症狀和危險因素，才能更準確評估情況並提供建議進行進一步檢查。應諮詢健康護理服務 provider 以為您個人化的指導和服務。切勿自行診斷自己——這會導致錯誤的主治醫生或者不當的大量治療。\n"
          ]
        }
      ],
      "source": [
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "prompt = \"每天肚子痛是啥狀況？\"\n",
        "messages = [\n",
        "    {\"role\": \"system\", \"content\": \"你是一位專業的醫療人員，請用心且專業的以三到五句話回答問題。\"},\n",
        "    {\"role\": \"user\", \"content\": prompt}\n",
        "]\n",
        "\n",
        "# 將消息合併成一個文本輸入\n",
        "def format_messages(messages):\n",
        "    formatted_messages = \"\"\n",
        "    for message in messages:\n",
        "        if message['role'] == 'system':\n",
        "            formatted_messages += f\"[SYSTEM] {message['content']}\\n\"\n",
        "        elif message['role'] == 'user':\n",
        "            formatted_messages += f\"[USER] {message['content']}\\n\"\n",
        "    return formatted_messages\n",
        "\n",
        "# 格式化消息\n",
        "formatted_text = format_messages(messages)\n",
        "print(\"Formatted text:\", formatted_text)\n",
        "\n",
        "text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)\n",
        "print(\"Generated text with chat template:\", text)\n",
        "\n",
        "\n",
        "model_inputs = tokenizer([text], return_tensors=\"pt\").to('cuda')\n",
        "\n",
        "# 印出model_inputs進行檢查\n",
        "print(\"Model inputs:\", model_inputs)\n",
        "\n",
        "# 產生文本\n",
        "generated_ids = model.generate(\n",
        "    model_inputs.input_ids,\n",
        "    max_new_tokens=90,\n",
        "    #eos_token_id=tokenizer.encode('<|eot_id|>')[0],\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "    pad_token_id=tokenizer.pad_token_id,\n",
        "    attention_mask=model_inputs.attention_mask,\n",
        "repetition_penalty=1.6,  # 增加 repetition_penalty\n",
        "    top_k=50,  # 設定最高k個概率詞\n",
        "    # stop_token=tokenizer.eos_token,  # 確保模型在遇到停止標記時中斷\n",
        "    do_sample=True,\n",
        "    top_p=0.15,  # 新增 top_p 參數\n",
        "    temperature=0.15,  # 設定 temperature 參數\n",
        "    #forced_eos_token_id=tokenizer.encode('</s>')[0]\n",
        ")\n",
        "\n",
        "# 打印generated_ids進行檢查\n",
        "print(\"Generated IDs:\", generated_ids)\n",
        "# 去掉輸入部分，僅保留生成的文本\n",
        "generated_ids = generated_ids[:, model_inputs.input_ids.shape[-1]:]\n",
        "\n",
        "\n",
        "response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
        "\n",
        "print(response)\n",
        "\n",
        "\n",
        "# 清理生成的文本\n",
        "import re\n",
        "response = re.sub(r'</s>.*', '', response, flags=re.DOTALL)\n",
        "response = re.sub(r'</s>.*', '</s>', response)\n",
        "response = re.sub(r'\\[.*?\\]', '', response)  # 去除方括號內的內容\n",
        "response = re.sub(r'</?[^>]+>', '', response)  # 去除HTML標籤\n",
        "response = re.sub(r'dress|dressing', '', response, flags=re.IGNORECASE)  # 去除 \"dress\" 和 \"dressing\"\n",
        "response = re.sub(r'<<.*?>>', '', response)  # 去除 `<<SYS>>` 標籤\n",
        "response = re.sub(r'\\[.*?\\]', '', response)  # 去除 `[/EMBB]]` 標籤\n",
        "response = response.strip()  # 去除前後多餘的空格\n",
        "# 從最後開始查找句號並去除句號之後的語句\n",
        "def remove_after_last_period(text):\n",
        "    last_period_index = max(text.rfind('。'), text.rfind('!'))\n",
        "    if last_period_index != -1:\n",
        "        return text[:last_period_index + 1]\n",
        "    return text\n",
        "\n",
        "# 清理生成的文本\n",
        "response = remove_after_last_period(response).strip()\n",
        "\n",
        "print(response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "W7BpBOd0gAbQ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "W7BpBOd0gAbQ",
        "outputId": "6442b02f-bafb-464e-9b9a-1242494795a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://ad6181bc44d884f75b.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://ad6181bc44d884f75b.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": []
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import gradio as gr\n",
        "import re\n",
        "# 定义一个生成文本的函数\n",
        "def chat_with_model(input_text):\n",
        "    inputs = tokenizer(input_text, return_tensors=\"pt\").to(\"cuda\")\n",
        "    # outputs = model.generate(inputs.input_ids, max_new_tokens=150, pad_token_id=tokenizer.pad_token_id)\n",
        "    # prompt = \"每天肚子痛是啥狀況？\"\n",
        "    messages = [\n",
        "        {\"role\": \"system\", \"content\": \"你是一位專業的醫療人員，請用心且專業的以三到五句話回答問題。\"},\n",
        "        {\"role\": \"user\", \"content\": input_text}\n",
        "    ]\n",
        "\n",
        "    # 將消息合併成一個文本輸入\n",
        "    def format_messages(messages):\n",
        "        formatted_messages = \"\"\n",
        "        for message in messages:\n",
        "            if message['role'] == 'system':\n",
        "                formatted_messages += f\"[SYSTEM] {message['content']}\\n\"\n",
        "            elif message['role'] == 'user':\n",
        "                formatted_messages += f\"[USER] {message['content']}\\n\"\n",
        "        return formatted_messages\n",
        "\n",
        "    # 格式化消息\n",
        "    formatted_text = format_messages(messages)\n",
        "    print(\"Formatted text:\", formatted_text)\n",
        "\n",
        "    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)\n",
        "    print(\"Generated text with chat template:\", text)\n",
        "\n",
        "\n",
        "    model_inputs = tokenizer([text], return_tensors=\"pt\").to('cuda')\n",
        "\n",
        "    # 印出model_inputs進行檢查\n",
        "    print(\"Model inputs:\", model_inputs)\n",
        "\n",
        "    # 產生文本\n",
        "    generated_ids = model.generate(\n",
        "        model_inputs.input_ids,\n",
        "        max_new_tokens=90,\n",
        "        #eos_token_id=tokenizer.encode('<|eot_id|>')[0],\n",
        "        eos_token_id=tokenizer.eos_token_id,\n",
        "        pad_token_id=tokenizer.pad_token_id,\n",
        "        attention_mask=model_inputs.attention_mask,\n",
        "    repetition_penalty=1.6,  # 增加 repetition_penalty\n",
        "        top_k=50,  # 設定最高k個概率詞\n",
        "        # stop_token=tokenizer.eos_token,  # 確保模型在遇到停止標記時中斷\n",
        "        do_sample=True,\n",
        "        top_p=0.15,  # 新增 top_p 參數\n",
        "        temperature=0.15,  # 設定 temperature 參數\n",
        "        #forced_eos_token_id=tokenizer.encode('</s>')[0]\n",
        "    )\n",
        "    generated_ids = generated_ids[:, model_inputs.input_ids.shape[-1]:]\n",
        "\n",
        "\n",
        "    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
        "    # 清理生成的文本\n",
        "\n",
        "    response = re.sub(r'</s>.*', '', response, flags=re.DOTALL)\n",
        "    response = re.sub(r'</s>.*', '</s>', response)\n",
        "    response = re.sub(r'\\[.*?\\]', '', response)  # 去除方括號內的內容\n",
        "    response = re.sub(r'</?[^>]+>', '', response)  # 去除HTML標籤\n",
        "    response = re.sub(r'dress|dressing', '', response, flags=re.IGNORECASE)  # 去除 \"dress\" 和 \"dressing\"\n",
        "    response = re.sub(r'<<.*?>>', '', response)  # 去除 `<<SYS>>` 標籤\n",
        "    response = re.sub(r'\\[.*?\\]', '', response)  # 去除 `[/EMBB]]` 標籤\n",
        "    response = response.strip()  # 去除前後多餘的空格\n",
        "    # 從最後開始查找句號並去除句號之後的語句\n",
        "    def remove_after_last_period(text):\n",
        "        last_period_index = max(text.rfind('。'), text.rfind('!'))\n",
        "        if last_period_index != -1:\n",
        "            return text[:last_period_index + 1]\n",
        "        return text\n",
        "\n",
        "    # 清理生成的文本\n",
        "    response = remove_after_last_period(response).strip()\n",
        "    return response\n",
        "\n",
        "# 创建 Gradio 界面\n",
        "iface = gr.Interface(fn=chat_with_model, inputs=\"text\", outputs=\"text\", title=\"Medical Chatbot\", description=\"使用TAIDE-LX-7B-Chat-Medical-Fintune模型的医疗聊天机器人\")\n",
        "\n",
        "# 启动接口\n",
        "iface.launch()\n"
      ]
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        "peft_model_id=\"./uploadfild2\"\n",
        "#trainer.model.save_pretrained(peft_model_id)\n",
        "tokenizer.save_pretrained(peft_model_id)\n",
        "#model.save_pretrained(peft_model_id)\n",
        "model.save_pretrained(peft_model_id, safe_serialization=False)\n"
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            "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
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              "adapter_model.bin:   0%|          | 0.00/84.0M [00:00<?, ?B/s]"
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        "os.environ[\"HUGGINGFACE_UPLOAD_TOKEN\"] = \"請更新成你的token\"\n",
        "# 登錄Hugging Face\n",
        "login(token=os.environ[\"HUGGINGFACE_UPLOAD_TOKEN\"])\n",
        "\n",
        "api = HfApi()\n",
        "api.upload_folder(\n",
        "    folder_path=peft_model_id,  # 本地保存模型的目錄\n",
        "    path_in_repo=\"\",  # 默認值會將文件上傳到根目錄\n",
        "    repo_id=\"mark1098/TAIDE-LX-7B-Chat-Medical-Fintune\",  # Hugging Face 上的模型名稱\n",
        "    repo_type=\"model\"  # 上傳的是模型\n",
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