MiniCPM/finetune/lora_finetune.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MiniCPM-2B 参数高效微调（LoRA）A100 80G 单卡示例\n",
    "\n",
    "显存更小的显卡可用 batch size 和 grad_accum 间时间换空间\n",
    "\n",
    "本 notebook 是一个使用 `AdvertiseGen` 数据集对 MiniCPM-2B 进行 LoRA 微调，使其具备专业的广告生成能力的代码示例。\n",
    "\n",
    "## 最低硬件需求\n",
    "- 显存：12GB\n",
    "- 显卡架构：安培架构（推荐）\n",
    "- 内存：16GB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 准备数据集\n",
    "\n",
    "将数据集转换为更通用的格式\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转换为 ChatML 格式\n",
    "import os\n",
    "import shutil\n",
    "import json\n",
    "\n",
    "input_dir = \"data/AdvertiseGen\"\n",
    "output_dir = \"data/AdvertiseGenChatML\"\n",
    "if os.path.exists(output_dir):\n",
    "    shutil.rmtree(output_dir)\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "for fn in [\"train.json\", \"dev.json\"]:\n",
    "    data_out_list = []\n",
    "    with open(os.path.join(input_dir, fn), \"r\") as f, open(os.path.join(output_dir, fn), \"w\") as fo:\n",
    "        for line in f:\n",
    "            if len(line.strip()) > 0:\n",
    "                data = json.loads(line)\n",
    "                data_out = {\n",
    "                    \"messages\": [\n",
    "                        {\n",
    "                            \"role\": \"user\",\n",
    "                            \"content\": data[\"content\"],\n",
    "                        },\n",
    "                        {\n",
    "                            \"role\": \"assistant\",\n",
    "                            \"content\": data[\"summary\"],\n",
    "                        },\n",
    "                    ]\n",
    "                }\n",
    "                data_out_list.append(data_out)\n",
    "        json.dump(data_out_list, fo, ensure_ascii=False, indent=4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 使用 LoRA 进行微调\n",
    "\n",
    "命令行一键运行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!bash lora_finetune.sh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 推理验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from tqdm import tqdm\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"output/AdvertiseGenLoRA/20240315224356/checkpoint-3000\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(path)\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    path, torch_dtype=torch.bfloat16, device_map=\"cuda\", trust_remote_code=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res, history = model.chat(tokenizer, query=\"<用户>类型#上衣*材质#牛仔布*颜色#白色*风格#简约*图案#刺绣*衣样式#外套*衣款式#破洞<AI>\", max_length=80, top_p=0.5)\n",
    "res, history"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}