76 lines
1.8 KiB
Markdown
76 lines
1.8 KiB
Markdown
# EmoLLM_Scientist微调指南
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## 数据
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微调数据共包含3800段对话，借助LLM自动生成，后续进行人工校验。数据路径：'datasets\scientist.json'
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## 基座
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基座模型采用InternLM2-Chat-7B，模型介绍请见[InternLM](https://github.com/InternLM/InternLM)
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## 训练方式
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基于xtuner的微调，使用xtuner的train命令行工具，使用命令如下：
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### 安装依赖
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```bash
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cd xtuner_config/
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pip3 install -r requirements.txt
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```
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---
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### 运行微调脚本
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```bash
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cd xtuner_config/
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xtuner train internlm2_7b_chat_qlora_e3_scienctist.py --deepspeed deepspeed_zero2
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```
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---
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### 模型转换
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将得到的 PTH 模型转换为 HuggingFace 模型，生成 Adapter 文件夹
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```bash
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cd xtuner_config/
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mkdir hf
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export MKL_SERVICE_FORCE_INTEL=1
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#这里假设训练了3个epoch
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xtuner convert pth_to_hf internlm2_7b_chat_qlora_e3_scienctist.py ./work_dirs/internlm2_7b_chat_qlora_e3_scienctist/epoch_3.pth ./hf
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```
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---
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### 将 HuggingFace adapter 合并到大语言模型
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```bash
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xtuner convert merge ./internlm2-chat-7b ./hf ./merged --max-shard-size 2GB
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# xtuner convert merge \
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# ${NAME_OR_PATH_TO_LLM} \
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# ${NAME_OR_PATH_TO_ADAPTER} \
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# ${SAVE_PATH} \
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# --max-shard-size 2GB
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```
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---
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### 测试
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```
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cd demo/
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python cli_internlm2_scientist.py
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```
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---
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## 模型上传
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完成测试后可将模型上传到ModelScope和Openxlab平台
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### ModelScope
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脚本：'scripts/upload_modelscope.py'
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[Openxlab模型上传](https://openxlab.org.cn/docs/models/%E4%B8%8A%E4%BC%A0%E6%A8%A1%E5%9E%8B.html)
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### Openxlab
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[ModelScope模型上传](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E5%88%9B%E5%BB%BA%E4%B8%8E%E6%96%87%E4%BB%B6%E4%B8%8A%E4%BC%A0)
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## 其他
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欢迎大家给[xtuner](https://github.com/InternLM/xtuner)和[EmoLLM](https://github.com/aJupyter/EmoLLM)点点star~
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🎉🎉🎉🎉🎉
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