286 lines
8.9 KiB
Markdown
286 lines
8.9 KiB
Markdown
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# GLM4-9B-chat Lora 微调.
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介绍如何基于 llama-factory 框架，对 glm-4-9b-chat 模型进行 Lora 微调。Lora 是一种高效微调方法，深入了解其原理可参见博客：[知乎|深入浅出 Lora](https://zhuanlan.zhihu.com/p/650197598)。
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## 一、环境准备
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我们实践了两种平台进行选择
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* 在[autodl](https://www.autodl.com/)平台中租一个3090等24G显存的显卡机器，如下图所示镜像选择`PyTorch`-->`2.0.0`-->`3.8(ubuntu20.04)`-->`11.8`
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* 在 [InternStudio](https://studio.intern-ai.org.cn/) 平台中选择 A100(1/4) 的配置，如下图所示镜像选择 `Cuda11.7-conda`，如下图所示：
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在Terminal中，进行pip换源和安装依赖包
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## 环境配置
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在完成基本环境配置和本地模型部署的情况下，你还需要安装一些第三方库，可以使用以下命令：
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```bash
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python -m pip install --upgrade pip
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# 更换 pypi 源加速库的安装
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pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
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# 安装 LLaMA-Factory
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git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
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cd LLaMA-Factory
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pip install -e ".[torch,metrics]"
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#上面这步操作会完成torch、transformers、datasets等相关依赖包的安装
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```
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## 二、模型下载
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使用 `modelscope` 中的`snapshot_download`函数下载模型，第一个参数为模型名称，参数`cache_dir`为模型的下载路径。
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在 `/root/autodl-tmp` 路径下新建 `download.py` 文件并在其中输入以下内容，粘贴代码后记得保存文件，如下图所示。并运行 `python /root/autodl-tmp/download.py`执行下载，模型大小为 14 GB，下载模型大概需要 10~20 分钟
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```python
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import torch
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from modelscope import snapshot_download, AutoModel, AutoTokenizer
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import os
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model_dir = snapshot_download('ZhipuAI/glm-4-9b-chat', cache_dir='/root/autodl-tmp', revision='master')
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```
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## 三、指令集构建 —— Alpaca 格式
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LLaMA-Factory 支持 alpaca 格式和 sharegpt 格式的数据集,本次微调我们使用 alpaca 格式
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### 指令监督微调数据格式说明
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在指令监督微调时，`instruction` 列对应的内容会与 `input` 列对应的内容拼接后作为人类指令，即人类指令为 `instruction\ninput`。而 `output` 列对应的内容为模型回答。
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如果指定，`system` 列对应的内容将被作为系统提示词。
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`history` 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮对话的指令和回答。注意在指令监督微调时，历史消息中的回答内容**也会被用于模型学习**。
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```json
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[
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{
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"instruction": "人类指令（必填）",
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"input": "人类输入（选填）",
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"output": "模型回答（必填）",
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"system": "系统提示词（选填）",
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"history": [
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["第一轮指令（选填）", "第一轮回答（选填）"],
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["第二轮指令（选填）", "第二轮回答（选填）"]
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]
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}
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]
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```
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### 单轮对话数据的格式转换
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使用以下程序将[数据集](../datasets/)转换成 alpaca 格式
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```python
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import json
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import re
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# 选择要格式转换的数据集
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file_name = "single_turn_dataset_1.json"
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#file_name = "single_turn_dataset_2.json"
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system_prompt = "如果要添加系统提示词，请放在这里"
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with open(f'../{file_name}', 'rt', encoding='utf-8') as file:
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data = json.load(file)
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converted_data = [{"instruction": item["prompt"],
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"input": "",
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"output": item["completion"],
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"system": system_prompt
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} for item in data]
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for i in range(len(converted_data)):
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# 数据清洗-去掉特殊符号
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if "🐳" in converted_data[i]["output"]:
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converted_data[i]["output"] = converted_data[i]["output"].replace("🐳", "")
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# 数据清洗-去掉“你好，我是红烧肉”，会影响大模型的自我认知
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if '好，我是' in converted_data[i]["output"]:
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converted_data[i]["output"] = converted_data[i]["output"].strip()
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intro_pattern = r"^[^\n]+\n"
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converted_data[i]["output"] = re.sub(intro_pattern, "", converted_data[i]["output"]).strip()
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with open(f'./processed/{file_name}', 'w', encoding='utf-8') as f:
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json.dump(converted_data, f, ensure_ascii=False, indent=4)
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print(f'./processed/{file_name} Done')
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```
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### 多轮对话数据的格式转换
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使用以下程序将[数据集](../datasets/)转换成 alpaca 格式
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```python
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from tqdm import tqdm
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import json
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# 选择要格式转换的数据集
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file_name = "data.json"
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#file_name = "data_pro.json"
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#file_name = "multi_turn_dataset_1.json"
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#file_name = "multi_turn_dataset_2.json"
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#file_name = "aiwei.json"
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system_prompt = "如果要添加系统提示词，请放在这里"
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with open(f'../{file_name}', 'rt', encoding='utf-8') as file:
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data = json.load(file)
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# 遍历原始数据，进行格式转换
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# 转换后的数据格式
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converted_data = []
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for item in tqdm(data):
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conversation = item['conversation']
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history = [(c['input'], c['output']) for c in conversation[:-1]]
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last_item = conversation[-1]
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converted_data.append({
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"instruction": last_item['input'],
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"input": "",
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"output": last_item['output'],
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"system": system_prompt,
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"history": history
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})
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# 将转换后的数据转换为JSON格式
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converted_json = json.dumps(converted_data, ensure_ascii=False, indent=4)
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with open(f'./processed/{file_name}', 'w', encoding='utf-8') as f:
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json.dump(converted_data, f, ensure_ascii=False, indent=4)
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```
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### 角色扮演数据的格式转换
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代码同上，根据原数据集是单轮对话还是多轮对话来选择。注意设置各个角色的“system_prompt”。
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### 数据集合并
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为了方便处理（不想在LLaMA-Factory中添加太多的数据集），这里将所有已经处理好的 alpaca 格式的数据集（每一个数据集文件都是一个json字符串）合并成一个文件（一个大的json字符串），合并代码如下：
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```python
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import json
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# 初始化一个空列表来存储所有数据
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merged_data = []
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file_list = [
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"single_turn_dataset_1.json",
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"single_turn_dataset_2.json",
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"self_cognition_EmoLLM.json",
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"ruozhiba_raw.json",
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"data.json",
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"data_pro.json",
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"multi_turn_dataset_1.json",
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"multi_turn_dataset_2.json",
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"aiwei.json",
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"tiangou.json",
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"SoulStar_data.json",
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"mother_v2.json",
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"scientist.json"
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]
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# 遍历所有文件并读取数据
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for filename in file_list:
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with open(f"./processed/{filename}", 'r', encoding='utf-8') as file:
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data = json.load(file)
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merged_data.extend(data)
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# 将合并后的数据写入新的 JSON 文件
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with open('emo_glm4_merged_data.json', 'w', encoding='utf-8') as output_file:
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json.dump(merged_data, output_file, ensure_ascii=False, indent=4)
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print("合并完成，已保存到 emo_glm4_merged_data.json 文件中。")
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```
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### 将数据集配置到LLaMA-Factory 中
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修改 LLaMa-Factory 目录中的 data/dataset_info.json 文件，在其中添加：
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```json
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"emo_merged": {
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"file_name": "emo_glm4_merged_data.json文件的绝对路径",
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}
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}
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```
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## 四、微调模型
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在 LLaMA-Factory 目录中新建配置文件 emo_glm4_lora_sft.yaml ：
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```python
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### model
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model_name_or_path: glm-4-9b-chat模型地址的绝对路径
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### method
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stage: sft
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do_train: true
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finetuning_type: lora
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lora_target: all
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### dataset
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# dataset 要和 data/dataset_info.json 中添加的信息保持一致
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dataset: emo_merged
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template: glm4
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cutoff_len: 2048
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max_samples: 1000
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overwrite_cache: true
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preprocessing_num_workers: 16
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### output
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# output_dir是模型训练过程中的checkpoint，训练日志等的保存目录
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output_dir: saves/emo-glm4-epoch10/lora/sft
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logging_steps: 10
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#save_steps: 500
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plot_loss: true
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overwrite_output_dir: true
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save_strategy: epoch
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### train
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 8
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learning_rate: 1.0e-4
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num_train_epochs: 10.0
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lr_scheduler_type: cosine
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warmup_ratio: 0.1
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fp16: true
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### eval
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do_eval: false
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val_size: 0.1
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per_device_eval_batch_size: 1
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eval_strategy: steps
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eval_steps: 10
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```
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执行以下命令开始微调：
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```bash
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cd LLaMA-Factory
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llamafactory-cli train glm4_emo_lora_sft.yaml
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```
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训练完成后，在 LLaMA-Factory 目录中新建配置文件 emo_glm4_lora_sft_export.yaml:
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```python
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### model
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model_name_or_path: glm-4-9b-chat模型地址的绝对路径
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# 刚才emo_glm4_lora_sft.yaml文件中的 output_dir
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adapter_name_or_path: saves/emo-glm4-epoch10/lora/sft
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template: glm4
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finetuning_type: lora
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### export
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export_dir: models/EmoLLM-glm-4-9b-chat
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export_size: 2
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export_device: cpu
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export_legacy_format: false
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```
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## 五、合并模型
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执行以下命令开始合并模型：
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```bash
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cd LLaMA-Factory
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llamafactory-cli export emo_glm4_lora_sft_export.yaml
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```
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在 models/EmoLLM-glm-4-9b-chat 目录中就可以获得经过Lora微调后的完整模型。
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模型权重已开源：[ModelScope](https://www.modelscope.cn/models/wwewwt/EmoLLM-glm-4-9b-chat/summary)
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