finetune.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "GvJKNC2yZJX8"
      },
      "outputs": [],
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 加载 google drive 或者你喜欢的其他网络存储，用于保存数据集和微调模型"
      ],
      "metadata": {
        "id": "KGX72EQjEF8v"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hTWhWAocFED",
        "outputId": "a6766a38-978e-47b8-9085-b7c7d878629b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/', force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-GRoZFoHxXW",
        "outputId": "8b2d14df-37b9-4363-8886-d3dfa2e4a849"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 克隆代码"
      ],
      "metadata": {
        "id": "FTM-nm8KEdiR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jU9urkAdJld",
        "outputId": "da8589e6-2c59-4646-b949-0969b4dcd0d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'seven8wen'...\n",
            "remote: Enumerating objects: 59, done.\u001b[K\n",
            "remote: Counting objects: 100% (59/59), done.\u001b[K\n",
            "remote: Compressing objects: 100% (39/39), done.\u001b[K\n",
            "remote: Total 59 (delta 32), reused 43 (delta 18), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (59/59), 9.60 KiB | 756.00 KiB/s, done.\n"
          ]
        }
      ],
      "source": [
        "!rm -fr seven8wen/ && git clone https://github.com/gameofdimension/seven8wen.git"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 安装依赖"
      ],
      "metadata": {
        "id": "Ehp0JNccEh3w"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6lR3rmbUSRC5",
        "outputId": "0ab30551-8389-404a-a960-5c7a556ba6d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/huggingface/peft.git (from -r seven8wen/requirements.txt (line 15))\n",
            "  Cloning https://github.com/huggingface/peft.git to /tmp/pip-req-build-m3zx3_81\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft.git /tmp/pip-req-build-m3zx3_81\n",
            "  Resolved https://github.com/huggingface/peft.git to commit fd10faedfa986157ae1912607638ac655a690e45\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting frozenlist>=1.1.1 (from aiohttp->datasets==2.10.1->-r seven8wen/requirements.txt (line 14))\n",
            "  Downloading frozenlist-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (149 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting aiosignal>=1.1.2 (from aiohttp->datasets==2.10.1->-r seven8wen/requirements.txt (line 14))\n",
            "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard->-r seven8wen/requirements.txt (line 11)) (5.3.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard->-r seven8wen/requirements.txt (line 11)) (0.3.0)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard->-r seven8wen/requirements.txt (line 11)) (1.16.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard->-r seven8wen/requirements.txt (line 11)) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard->-r seven8wen/requirements.txt (line 11)) (1.3.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.27.1->-r seven8wen/requirements.txt (line 7)) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.27.1->-r seven8wen/requirements.txt (line 7)) (2022.12.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.27.1->-r seven8wen/requirements.txt (line 7)) (3.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard->-r seven8wen/requirements.txt (line 11)) (2.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets==2.10.1->-r seven8wen/requirements.txt (line 14)) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets==2.10.1->-r seven8wen/requirements.txt (line 14)) (2022.7.1)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.1->-r seven8wen/requirements.txt (line 10)) (1.3.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->icetk->-r seven8wen/requirements.txt (line 8)) (8.4.0)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->-r seven8wen/requirements.txt (line 11)) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard->-r seven8wen/requirements.txt (line 11)) (3.2.2)\n",
            "Building wheels for collected packages: peft\n",
            "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for peft: filename=peft-0.4.0.dev0-py3-none-any.whl size=61569 sha256=0ec6f337cc8e08e72aeadc04d81c0982d194fec283324898a018e301963ad353\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-fz370siv/wheels/d7/c7/de/1368fac8590e1b103ddc2ec2a28ad51d83aded1a3830e8a087\n",
            "Successfully built peft\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, cpm_kernels, bitsandbytes, xxhash, protobuf, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, huggingface-hub, aiosignal, transformers, aiohttp, datasets, accelerate, peft, icetk\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.20.3\n",
            "    Uninstalling protobuf-3.20.3:\n",
            "      Successfully uninstalled protobuf-3.20.3\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",
            "google-api-core 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-bigquery 3.9.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.19.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-datastore 2.15.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-firestore 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-language 2.9.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-translate 3.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "googleapis-common-protos 1.59.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.0 which is incompatible.\n",
            "tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 3.20.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed accelerate-0.17.1 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 bitsandbytes-0.37.1 cpm_kernels-1.0.11 datasets-2.10.1 dill-0.3.6 frozenlist-1.3.3 huggingface-hub-0.15.1 icetk-0.0.4 multidict-6.0.4 multiprocess-0.70.14 peft-0.4.0.dev0 protobuf-3.20.0 responses-0.18.0 safetensors-0.3.1 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.27.1 xxhash-3.2.0 yarl-1.9.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install -r seven8wen/requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQ0nr1fQTT3W",
        "outputId": "fec26b5b-714a-4435-c6fd-29df933996cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/seven8wen/code\n"
          ]
        }
      ],
      "source": [
        "%cd seven8wen/code"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "# warnings.filterwarnings(action='once')\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "MSc-Dr2bLyOk"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 将语料处理成 token id 列表。如果 mount 了稳定存储，则只需运行一次，后续再跑这个 notebook 时就直接加载第一次的结果就好了"
      ],
      "metadata": {
        "id": "MtHgRjyZErAb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "_iAgy2yWSXXX"
      },
      "outputs": [],
      "source": [
        "from data_prep import make_data\n",
        "# make_data(model_name=\"THUDM/chatglm-6b\", base_dir='/content/drive/MyDrive/corpus/')\n",
        "# make_data(model_name=\"baichuan-inc/baichuan-7B\", base_dir='/content/drive/MyDrive/corpus/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RwKJy3h0UuN4",
        "outputId": "c90ee52c-e671-41b2-ece9-50315b66f9b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        }
      ],
      "source": [
        "from lora import finetune"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 运行微调"
      ],
      "metadata": {
        "id": "i27Nt7fHFiAM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "QmxMpPgBXWpt",
        "outputId": "3234c0ac-7fbe-42f5-a596-cb66e3f79fb6"
      },
      "outputs": [
        {
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.\n"
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          "name": "stderr",
          "text": [
            "Explicitly passing a `revision` is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision.\n"
          ]
        },
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.\n"
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          "name": "stderr",
          "text": [
            "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "以下是微调前输出：\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers_modules.THUDM.chatglm-6b.1d240ba371910e9282298d4592532d7f0f3e9f3e.modeling_chatglm:The dtype of attention mask (torch.int64) is not bool\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prompt->\n",
            " 类型#裙*材质#牛仔布*颜色#黑色*风格#潮*裙型#a字*裙型#牛仔裙*裙款式#口袋\n",
            "prediction->\n",
            "  好的,以下是您提供的裙款信息:\n",
            "\n",
            "类型:牛仔裙\n",
            "材质:牛仔布\n",
            "颜色:黑色\n",
            "风格:潮流\n",
            "裙型:A字裙\n",
            "款式:牛仔裙\n",
            "口袋:无口袋\n",
            "gold truth->\n",
            " 俏皮的a字廓形搭配挺括硬朗的洗水牛仔，<UNK>穿又实力减龄，同时露出美好的腿部肌肤，让时尚潮人们钟爱不已。黑色毛球装饰在口袋与裙摆上，丰富其层次感之余格外吸睛夺目。舒适的一字腰头，巧妙勾勒出纤细腰肢。\n",
            "\n",
            "\n",
            "prompt->\n",
            " 类型#裙*风格#通勤*风格#淑女*风格#文艺*风格#简约*图案#格子*裙长#连衣裙*裙衣长#常规*裙袖长#长袖*裙领型#v领*裙领型#翻领*裙衣门襟#系带*裙款式#拼接*裙款式#不规则\n",
            "prediction->\n",
            "  类型#裙*风格#通勤*风格#淑女*风格#文艺*风格#简约*图案#格子*裙长#连衣裙*裙衣长#常规*裙袖长#长袖*裙领型#v领*裙领型#翻领*裙衣门襟#系带*裙款式#拼接*裙款式#不规则\n",
            "gold truth->\n",
            " 这款长袖系带连衣裙，可以说是细节之处都是小亮点了。领口部分运用了常规通勤的翻领样式，形成了一个小v领，可以很好地展现锁骨的曲线。值得一提的是，腰部与袖口处相互呼应的格纹拼接设计，很有文艺淑女的气质，简约中透露着女性的优雅。还有不规则的碎褶裙摆，浪漫又随性不拖沓。\n",
            "\n",
            "\n",
            "prompt->\n",
            " 类型#上衣*版型#宽松*版型#显瘦*风格#简约*衣样式#外套*衣领型#圆领*衣袖长#无袖\n",
            "prediction->\n",
            "  类型#上衣*版型#宽松#显瘦*风格#简约*衣样式#外套*衣领型#圆领*衣袖长#无袖\n",
            "\n",
            "宽松版型的上衣是近年来非常流行的款式,不仅能够很好地展现身材曲线,还能够让人看起来更加舒适自在。这种款式的上衣通常采用简约的设计风格,搭配圆领或V领的衣领,能够突出个人的气质。\n",
            "\n",
            "在衣袖方面,宽松版型的上衣可以选择无袖或半袖,这两种款式都能够很好地展现女性的柔美感。而衣领方面,可以选择V领\n",
            "gold truth->\n",
            " 圆领设计，时尚大方，优雅舒适有型。简约无袖的设计，尽显干练利落，简洁圆领设计，剪裁简洁大气，衬托出一丝禁欲气息，轻松优雅，宽松自在，遮肉显瘦，舒适还保暖，单穿或者搭配大衣外套都是经典，优质<UNK>面料，舒适透气保暖，不起球不易变形，耐穿手感舒适顺畅。\n",
            "\n",
            "\n",
            "prompt->\n",
            " 类型#裙*版型#显瘦*颜色#深色*图案#印花*裙下摆#花边*裙长#连衣裙*裙款式#不对称\n",
            "prediction->\n",
            "  裙下摆和花边可以增强裙子的层次感和质感,而裙长则可以根据个人喜好和场合来选择。深色的颜色可以使人看起来更加苗条和高挑,而图案和印花则可以为裙子增添色彩和趣味性。不对称的设计可以创造出独特的视觉效果,从而使裙子更加独特和时尚。连衣裙是最常见的裙子款式之一,可以根据个人喜好和场合来选择不同的裙长和颜色。\n",
            "gold truth->\n",
            " 此款连衣裙选用优质面料打造，上身穿着舒适度爆棚。独特印花，立体饱满，工艺精湛。不对称的肩部设计，打破传统设计的单调感，更显时尚与活力。花边点缀，为整体注入甜美气息，少女感十足。搭配上深色腰封，有效拔高腰线，轻松优化身材比例，显高显瘦。\n",
            "\n",
            "\n",
            "prompt->\n",
            " 类型#裙*版型#宽松*风格#简约*裙型#直筒裙*裙长#连衣裙*裙领型#圆领*裙衣门襟#套头*裙款式#拼接\n",
            "prediction->\n",
            "  裙款式#宽松连衣裙#直筒裙#拼接连衣裙#圆领连衣裙#裙衣门襟连衣裙#裙长连衣裙#简约风格连衣裙#时尚连衣裙#休闲连衣裙#夏季连衣裙#秋季连衣裙#冬季连衣裙#\n",
            "gold truth->\n",
            " 连衣裙看似简约，实际上有着很强的设计感。宽松直筒版型包容性强，不管是什么身材的女性穿在身上都十分有型。圆领套头设计简约实用，裙身上的拼接设计给人亮眼时尚的感觉，优质面料柔软舒适，保暖性透气性极佳又十分亲肤。\n",
            "\n",
            "\n"
          ]
        },
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1500' max='1500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1500/1500 36:15, Epoch 1/1]\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>4.775400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>4.234300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>4.115900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>4.115100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>4.162800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>4.122100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>4.098300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>4.022200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>4.066900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>3.956100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>4.098800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>3.996800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>4.119700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>3.941000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>4.064000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>4.066600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>3.961500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>3.963900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>4.012000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>3.855900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1050</td>\n",
              "      <td>3.849100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>4.010200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1150</td>\n",
              "      <td>3.899700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>3.815700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1250</td>\n",
              "      <td>3.898100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>3.864500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1350</td>\n",
              "      <td>3.840800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>3.942600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1450</td>\n",
              "      <td>3.916300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>3.821800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
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          "text": [
            "以下是微调后结果：\n",
            "prompt->\n",
            " 类型#裙*材质#牛仔布*颜色#黑色*风格#潮*裙型#a字*裙型#牛仔裙*裙款式#口袋\n",
            "prediction->\n",
            "  黑色牛仔裙是经典的时尚单品,经典的黑色色调,让这款牛仔裙更显高级感,搭配a字版型,更显腰身纤细,搭配口袋设计,更显潮酷感。\n",
            "gold truth->\n",
            " 俏皮的a字廓形搭配挺括硬朗的洗水牛仔，<UNK>穿又实力减龄，同时露出美好的腿部肌肤，让时尚潮人们钟爱不已。黑色毛球装饰在口袋与裙摆上，丰富其层次感之余格外吸睛夺目。舒适的一字腰头，巧妙勾勒出纤细腰肢。\n",
            "\n",
            "\n",
            "prompt->\n",
            " 类型#裙*风格#通勤*风格#淑女*风格#文艺*风格#简约*图案#格子*裙长#连衣裙*裙衣长#常规*裙袖长#长袖*裙领型#v领*裙领型#翻领*裙衣门襟#系带*裙款式#拼接*裙款式#不规则\n",
            "prediction->\n",
            "  一款简约的连衣裙,经典的翻领设计,带来优雅的气质,搭配v领的领口,展现女性柔美的曲线,搭配系带的领口,带来甜美的气息,搭配不规则的裙摆,带来独特的风格,搭配长袖的袖口,带来优雅的气质,搭配翻领的领口,带来干练的干练,搭配格子的拼接,带来文艺的气息。\n",
            "gold truth->\n",
            " 这款长袖系带连衣裙，可以说是细节之处都是小亮点了。领口部分运用了常规通勤的翻领样式，形成了一个小v领，可以很好地展现锁骨的曲线。值得一提的是，腰部与袖口处相互呼应的格纹拼接设计，很有文艺淑女的气质，简约中透露着女性的优雅。还有不规则的碎褶裙摆，浪漫又随性不拖沓。\n",
            "\n",
            "\n",
            "prompt->\n",
            " 类型#上衣*版型#宽松*版型#显瘦*风格#简约*衣样式#外套*衣领型#圆领*衣袖长#无袖\n",
            "prediction->\n",
            "  无袖的外套,简约的圆领设计,更显优雅大方。宽松的版型设计,穿着舒适不紧绷,显瘦更显高挑。衣身的下摆设计,修饰腿型,更显高挑。\n",
            "gold truth->\n",
            " 圆领设计，时尚大方，优雅舒适有型。简约无袖的设计，尽显干练利落，简洁圆领设计，剪裁简洁大气，衬托出一丝禁欲气息，轻松优雅，宽松自在，遮肉显瘦，舒适还保暖，单穿或者搭配大衣外套都是经典，优质<UNK>面料，舒适透气保暖，不起球不易变形，耐穿手感舒适顺畅。\n",
            "\n",
            "\n",
            "prompt->\n",
            " 类型#裙*版型#显瘦*颜色#深色*图案#印花*裙下摆#花边*裙长#连衣裙*裙款式#不对称\n",
            "prediction->\n",
            "  连衣裙的下摆采用了不对称的花边设计,让整件连衣裙看起来更加的别致,同时,也增加了整件连衣裙的层次感。深色的印花图案,让整件连衣裙看起来更加的优雅大方,同时,也增加了整件连衣裙的时尚感。\n",
            "gold truth->\n",
            " 此款连衣裙选用优质面料打造，上身穿着舒适度爆棚。独特印花，立体饱满，工艺精湛。不对称的肩部设计，打破传统设计的单调感，更显时尚与活力。花边点缀，为整体注入甜美气息，少女感十足。搭配上深色腰封，有效拔高腰线，轻松优化身材比例，显高显瘦。\n",
            "\n",
            "\n",
            "prompt->\n",
            " 类型#裙*版型#宽松*风格#简约*裙型#直筒裙*裙长#连衣裙*裙领型#圆领*裙衣门襟#套头*裙款式#拼接\n",
            "prediction->\n",
            "  简约的圆领设计,让这款连衣裙更显优雅大方,套头的设计,让这款连衣裙更显简约大方,直筒的宽松版型,让这款连衣裙更显宽松舒适,拼接的裙摆设计,让这款连衣裙更显时尚大方。\n",
            "gold truth->\n",
            " 连衣裙看似简约，实际上有着很强的设计感。宽松直筒版型包容性强，不管是什么身材的女性穿在身上都十分有型。圆领套头设计简约实用，裙身上的拼接设计给人亮眼时尚的感觉，优质面料柔软舒适，保暖性透气性极佳又十分亲肤。\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "finetune(model_name=\"THUDM/chatglm-6b\", base_dir='/content/drive/MyDrive/corpus/', v100=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
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      "source": [
        "# finetune(model_name=\"baichuan-inc/baichuan-7B\", base_dir='/content/drive/MyDrive/corpus/', v100=False)"
      ]
    }
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