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update internlm 7B chat
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# InternLM-Chat-7B 对话 Web
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## 环境准备
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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`-->`1.11.0`-->`3.8(ubuntu20.04)`-->`11.3`
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接下来打开刚刚租用服务器的`JupyterLab`,并且打开其中的终端开始环境配置、模型下载和运行`demo`。
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pip换源和安装依赖包
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```shell
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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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pip install modelscope
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pip install transformers
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pip install streamlit==1.24.0
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pip install sentencepiece
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pip install accelerate
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```
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## 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('Shanghai_AI_Laboratory/internlm-chat-7b', cache_dir='/root/autodl-tmp', revision='master')
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```
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## 代码准备
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首先`clone`代码,打开autodl平台自带的学术镜像加速。学术镜像加速详细使用请看:https://www.autodl.com/docs/network_turbo/
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```shell
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source /etc/network_turbo
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```
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然后切换路径, clone代码.
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```shell
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cd /root/autodl-tmp
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git clone https://github.com/InternLM/InternLM.git
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```
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切换commit版本,与教程commit版本保持一致,可以让大家更好的复现。
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```shell
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cd InternLM
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git checkout 3028f07cb79e5b1d7342f4ad8d11efad3fd13d17
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```
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最后取消镜像加速,因为该加速可能对正常网络造成一定影响,避免对后续下载其他模型造成困扰。
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```shell
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unset http_proxy && unset https_proxy
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```
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将 `/root/autodl-tmp/InternLM/web_demo.py`中 29 行和 33 行的模型更换为本地的`/root/autodl-tmp/Shanghai_AI_Laboratory/internlm-chat-7b`。
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## web demo运行
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运行以下命令即可启动推理服务
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```shell
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cd /root/autodl-tmp/InternLM
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streamlit run web_demo.py --server.address 127.0.0.1 --server.port 6006
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```
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将 `autodl `的端口映射到本地的 [http://localhost:6006](http://localhost:6006/) 仅在此处展示一次,以下两个 Demo 都是同样的方法把 `autodl `中的 `6006 `端口映射到本机的 `http://localhost:6006`的方法都是相同的,方法如图所示
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# 大模型介绍
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## InternLM模型全链条开源
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