5.使用_LLaMA_Factory_微调」.ipynb

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      "provenance": [],
      "gpuType": "A100",
      "machine_shape": "hm"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 使用 LLaMA Factory 微调 Llama-3 中文对话模型\n",
        "\n",
        "请申请一个免费 T4 GPU 来运行该脚本\n",
        "\n",
        "项目主页: https://github.com/hiyouga/LLaMA-Factory\n"
      ],
      "metadata": {
        "id": "gf60HoT633NY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 安装 LLaMA Factory 依赖"
      ],
      "metadata": {
        "id": "rMp4rTWk4TKZ"
      }
    },
    {
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7201c384-5d12-4247-d438-6195da00bf72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Building editable for llamafactory (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/d6/6d/5d/5b73fa0f46d01a793713f8859201361e9e581ced8c75e5c6a3\n",
            "Successfully built llamafactory fire\n",
            "Installing collected packages: pydub, xxhash, websockets, tomlkit, shtab, semantic-version, ruff, python-multipart, pyarrow, orjson, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, h11, fsspec, fire, ffmpy, dill, aiofiles, uvicorn, tiktoken, starlette, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, httpcore, tyro, sse-starlette, nvidia-cusolver-cu12, httpx, fastapi, gradio-client, datasets, gradio, bitsandbytes, accelerate, trl, peft, llamafactory\n",
            "  Attempting uninstall: tomlkit\n",
            "    Found existing installation: tomlkit 0.13.0\n",
            "    Uninstalling tomlkit-0.13.0:\n",
            "      Successfully uninstalled tomlkit-0.13.0\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.6.1\n",
            "    Uninstalling fsspec-2024.6.1:\n",
            "      Successfully uninstalled fsspec-2024.6.1\n",
            "  Attempting uninstall: accelerate\n",
            "    Found existing installation: accelerate 0.32.1\n",
            "    Uninstalling accelerate-0.32.1:\n",
            "      Successfully uninstalled accelerate-0.32.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.5.0 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed accelerate-0.32.0 aiofiles-23.2.1 bitsandbytes-0.43.3 datasets-2.20.0 dill-0.3.8 fastapi-0.112.0 ffmpy-0.4.0 fire-0.6.0 fsspec-2024.5.0 gradio-4.41.0 gradio-client-1.3.0 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 llamafactory-0.8.4.dev0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105 orjson-3.10.7 peft-0.12.0 pyarrow-17.0.0 pydub-0.25.1 python-multipart-0.0.9 ruff-0.5.7 semantic-version-2.10.0 shtab-1.7.1 sse-starlette-2.1.3 starlette-0.37.2 tiktoken-0.7.0 tomlkit-0.12.0 trl-0.9.6 tyro-0.8.6 uvicorn-0.30.5 websockets-12.0 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "%cd /content/\n",
        "%rm -rf LLaMA-Factory\n",
        "!git clone https://github.com/hiyouga/LLaMA-Factory.git\n",
        "%cd LLaMA-Factory\n",
        "%ls\n",
        "!pip install -e .[torch,bitsandbytes]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 检查 GPU 环境\n",
        "\n",
        "免费 T4 申请教程：https://zhuanlan.zhihu.com/p/642542618"
      ],
      "metadata": {
        "id": "Gs68aSwm5MFi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "try:\n",
        "  assert torch.cuda.is_available() is True\n",
        "except AssertionError:\n",
        "  print(\"需要 GPU 环境，申请教程：https://zhuanlan.zhihu.com/p/642542618\")"
      ],
      "metadata": {
        "id": "P6rwbyFa5LkF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 更新自我认知数据集\n",
        "\n",
        "可以自由修改 NAME 和 AUTHOR 变量的内容。"
      ],
      "metadata": {
        "id": "gYEF3SGI6XdO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "%cd /content/LLaMA-Factory/\n",
        "\n",
        "NAME = \"Llama-Chinese\"\n",
        "AUTHOR = \"LLaMA Factory\"\n",
        "\n",
        "with open(\"data/identity.json\", \"r\", encoding=\"utf-8\") as f:\n",
        "  dataset = json.load(f)\n",
        "\n",
        "for sample in dataset:\n",
        "  sample[\"output\"] = sample[\"output\"].replace(\"{{\"+ \"name\" + \"}}\", NAME).replace(\"{{\"+ \"author\" + \"}}\", AUTHOR)\n",
        "\n",
        "with open(\"data/identity.json\", \"w\", encoding=\"utf-8\") as f:\n",
        "  json.dump(dataset, f, indent=2, ensure_ascii=False)"
      ],
      "metadata": {
        "id": "7gX4PskL6UJP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "655cfdf5-1c42-4de7-87e1-2903a4d6e8a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/LLaMA-Factory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 使用 LLaMA Board Web UI 微调模型"
      ],
      "metadata": {
        "id": "cA7vWZ6om3cR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/LLaMA-Factory/data/\n",
        "!wget https://huggingface.co/datasets/ChenWeiLi/Medtext_zhtw/raw/main/MedText_zhtw.json\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rzzVHPJlQDj7",
        "outputId": "2155b612-353a-4d5b-b10a-63f64051e579"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/LLaMA-Factory/data\n",
            "--2024-08-13 01:43:18--  https://huggingface.co/datasets/ChenWeiLi/Medtext_zhtw/raw/main/MedText_zhtw.json\n",
            "Resolving huggingface.co (huggingface.co)... 18.238.109.52, 18.238.109.121, 18.238.109.102, ...\n",
            "Connecting to huggingface.co (huggingface.co)|18.238.109.52|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 983900 (961K) [text/plain]\n",
            "Saving to: ‘MedText_zhtw.json’\n",
            "\n",
            "MedText_zhtw.json   100%[===================>] 960.84K  5.37MB/s    in 0.2s    \n",
            "\n",
            "2024-08-13 01:43:18 (5.37 MB/s) - ‘MedText_zhtw.json’ saved [983900/983900]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/LLaMA-Factory/data/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNNar6F3Urtj",
        "outputId": "fae5a5b9-3e1f-4d19-969d-adbdf48a118c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/LLaMA-Factory/data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# 讀取 dataset_info.json 文件\n",
        "with open('dataset_info.json', 'r') as file:\n",
        "    data = json.load(file)\n",
        "\n",
        "# 新增 \"Medtext\" 項目\n",
        "data[\"Medtext\"] = {\n",
        "    \"file_name\": \"MedText_zhtw.json\"\n",
        "}\n",
        "\n",
        "# 將修改後的內容保存回 dataset_info.json 文件\n",
        "with open('dataset_info.json', 'w') as file:\n",
        "    json.dump(data, file, indent=2, ensure_ascii=False)\n"
      ],
      "metadata": {
        "id": "QiaeApu_WjSu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/LLaMA-Factory/\n",
        "!GRADIO_SHARE=1 llamafactory-cli webui"
      ],
      "metadata": {
        "id": "YIfzFgLsm2kS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4776bc8-bc0f-4fc2-ff4c-6379fa30d03f"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Downloading shards: 100% 30/30 [32:05<00:00, 64.18s/it]\n",
            "[INFO|modeling_utils.py:1531] 2024-08-13 02:17:37,870 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.\n",
            "[INFO|configuration_utils.py:1000] 2024-08-13 02:17:37,871 >> Generate config GenerationConfig {\n",
            "  \"bos_token_id\": 128000,\n",
            "  \"eos_token_id\": 128009,\n",
            "  \"use_cache\": false\n",
            "}\n",
            "\n",
            "Loading checkpoint shards:  23% 7/30 [02:56<09:37, 25.09s/it]"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 使用命令行微调模型\n",
        "\n",
        "微调过程大约需要 30 分钟。"
      ],
      "metadata": {
        "id": "B6ap81295trx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "args = dict(\n",
        "  stage=\"sft\",                        # 进行指令监督微调\n",
        "  do_train=True,\n",
        "  model_name_or_path=\"unsloth/llama-3-8b-Instruct-bnb-4bit\", # 使用 4 比特量化版 Llama-3-8b-Instruct 模型\n",
        "  dataset=\"identity,alpaca_en_demo,alpaca_zh_demo\",      # 使用 alpaca 和自我认知数据集\n",
        "  template=\"llama3\",                     # 使用 llama3 提示词模板\n",
        "  finetuning_type=\"lora\",                   # 使用 LoRA 适配器来节省显存\n",
        "  lora_target=\"all\",                     # 添加 LoRA 适配器至全部线性层\n",
        "  output_dir=\"llama3_lora\",                  # 保存 LoRA 适配器的路径\n",
        "  per_device_train_batch_size=2,               # 批处理大小\n",
        "  gradient_accumulation_steps=4,               # 梯度累积步数\n",
        "  lr_scheduler_type=\"cosine\",                 # 使用余弦学习率退火算法\n",
        "  logging_steps=10,                      # 每 10 步输出一个记录\n",
        "  warmup_ratio=0.1,                      # 使用预热学习率\n",
        "  save_steps=1000,                      # 每 1000 步保存一个检查点\n",
        "  learning_rate=5e-5,                     # 学习率大小\n",
        "  num_train_epochs=3.0,                    # 训练轮数\n",
        "  max_samples=300,                      # 使用每个数据集中的 300 条样本\n",
        "  max_grad_norm=1.0,                     # 将梯度范数裁剪至 1.0\n",
        "  quantization_bit=4,                     # 使用 4 比特 QLoRA\n",
        "  loraplus_lr_ratio=16.0,                   # 使用 LoRA+ 算法并设置 lambda=16.0\n",
        "  fp16=True,                         # 使用 float16 混合精度训练\n",
        ")\n",
        "\n",
        "json.dump(args, open(\"train_llama3.json\", \"w\", encoding=\"utf-8\"), indent=2)\n",
        "\n",
        "%cd /content/LLaMA-Factory/\n",
        "\n",
        "!llamafactory-cli train train_llama3.json"
      ],
      "metadata": {
        "id": "psywJyo75vt6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 模型推理"
      ],
      "metadata": {
        "id": "otpDQuzaMBpm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llamafactory.chat import ChatModel\n",
        "from llamafactory.extras.misc import torch_gc\n",
        "\n",
        "%cd /content/LLaMA-Factory/\n",
        "\n",
        "args = dict(\n",
        "  model_name_or_path=\"unsloth/llama-3-8b-Instruct-bnb-4bit\", # 使用 4 比特量化版 Llama-3-8b-Instruct 模型\n",
        "  adapter_name_or_path=\"llama3_lora\",            # 加载之前保存的 LoRA 适配器\n",
        "  template=\"llama3\",                     # 和训练保持一致\n",
        "  finetuning_type=\"lora\",                  # 和训练保持一致\n",
        "  quantization_bit=4,                    # 加载 4 比特量化模型\n",
        ")\n",
        "chat_model = ChatModel(args)\n",
        "\n",
        "messages = []\n",
        "print(\"使用 `clear` 清除对话历史，使用 `exit` 退出程序。\")\n",
        "while True:\n",
        "  query = input(\"\\nUser: \")\n",
        "  if query.strip() == \"exit\":\n",
        "    break\n",
        "  if query.strip() == \"clear\":\n",
        "    messages = []\n",
        "    torch_gc()\n",
        "    print(\"对话历史已清除\")\n",
        "    continue\n",
        "\n",
        "  messages.append({\"role\": \"user\", \"content\": query})\n",
        "  print(\"Assistant: \", end=\"\", flush=True)\n",
        "\n",
        "  response = \"\"\n",
        "  for new_text in chat_model.stream_chat(messages):\n",
        "    print(new_text, end=\"\", flush=True)\n",
        "    response += new_text\n",
        "  print()\n",
        "  messages.append({\"role\": \"assistant\", \"content\": response})\n",
        "\n",
        "torch_gc()"
      ],
      "metadata": {
        "id": "kbFsAE-y5so4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 合并 LoRA 权重并上传模型\n",
        "\n",
        "注意：Colab 免费版仅提供了 12GB 系统内存，而合并 8B 模型的 LoRA 权重需要至少 18GB 系统内存，因此你 **无法** 在免费版运行此功能。"
      ],
      "metadata": {
        "id": "_7g7kprbhXo0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "id": "XA2kyAz-hXbp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "args = dict(\n",
        "  model_name_or_path=\"meta-llama/Meta-Llama-3-8B-Instruct\", # 使用非量化的官方 Llama-3-8B-Instruct 模型\n",
        "  adapter_name_or_path=\"llama3_lora\",            # 加载之前保存的 LoRA 适配器\n",
        "  template=\"llama3\",                     # 和训练保持一致\n",
        "  finetuning_type=\"lora\",                  # 和训练保持一致\n",
        "  export_dir=\"llama3_lora_merged\",              # 合并后模型的保存目录\n",
        "  export_size=2,                       # 合并后模型每个权重文件的大小（单位：GB）\n",
        "  export_device=\"cpu\",                    # 合并模型使用的设备：`cpu` 或 `cuda`\n",
        "  #export_hub_model_id=\"your_id/your_model\",         # 用于上传模型的 HuggingFace 模型 ID\n",
        ")\n",
        "\n",
        "json.dump(args, open(\"merge_llama3.json\", \"w\", encoding=\"utf-8\"), indent=2)\n",
        "\n",
        "%cd /content/LLaMA-Factory/\n",
        "\n",
        "!llamafactory-cli export merge_llama3.json"
      ],
      "metadata": {
        "id": "eERYoAOrhpcu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}