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56 lines
1.7 KiB
Markdown
56 lines
1.7 KiB
Markdown
# 评测LMDeploy模型
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我们支持评测使用[LMDeploy](https://github.com/InternLM/lmdeploy)加速过的大语言模型。LMDeploy 由 MMDeploy 和 MMRazor 团队联合开发，是涵盖了 LLM 任务的全套轻量化、部署和服务解决方案。 **TurboMind** 是 LMDeploy 推出的高效推理引擎。OpenCompass 对 TurboMind 进行了适配，本教程将介绍如何使用 OpenCompass 来对 TurboMind 加速后的模型进行评测。
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## 环境配置
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### 安装OpenCompass
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请根据OpenCompass[安装指南](https://opencompass.readthedocs.io/en/latest/get_started.html) 来安装算法库和准备数据集。
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### 安装LMDeploy
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使用pip安装LMDeploy( python 3.8+)
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```shell
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pip install lmdeploy
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```
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## 评测
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我们使用InternLM作为例子来介绍如何评测
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### 第一步: 获取InternLM模型
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```shell
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# 1. Download InternLM model(or use the cached model's checkpoint)
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# Make sure you have git-lfs installed (https://git-lfs.com)
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git lfs install
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git clone https://huggingface.co/internlm/internlm-chat-7b /path/to/internlm-chat-7b
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# if you want to clone without large files – just their pointers
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# prepend your git clone with the following env var:
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GIT_LFS_SKIP_SMUDGE=1
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# 2. Convert InternLM model to turbomind's format, which will be in "./workspace" by default
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python3 -m lmdeploy.serve.turbomind.deploy internlm-chat-7b /path/to/internlm-chat-7b
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```
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### 第二步: 验证转换后的模型
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```shell
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python -m lmdeploy.turbomind.chat ./workspace
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```
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### 第三步: 评测转换后的模型
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在OpenCompass项目文件执行：
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```shell
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python run.py configs/eval_internlm_chat_7b_turbomind.py -w outputs/turbomind
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```
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当模型完成推理和指标计算后，我们便可获得模型的评测结果
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