[Doc] Update readme (#171)
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README.md
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README.md
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<img src="assets/Roadmap_ZH.png" alt="Roadmap_ZH">
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</a>
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### 🎯框架图
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### 🔗框架图
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<p align="center">
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<a href="https://github.com/SmartFlowAI/EmoLLM/">
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@ -170,10 +170,11 @@
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- [🎇最近更新](#最近更新)
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- [🏆荣誉栏](#荣誉栏)
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- [🎯路线图](#路线图)
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- [🎯框架图](#框架图)
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- [🔗框架图](#框架图)
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- [目录](#目录)
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- [开发前的配置要求](#开发前的配置要求)
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- [**使用指南**](#使用指南)
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- [快速体验](#快速体验)
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- [数据构建](#数据构建)
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- [微调指南](#微调指南)
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- [部署指南](#部署指南)
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@ -201,28 +202,35 @@ git clone https://github.com/SmartFlowAI/EmoLLM.git
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```
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2. 依次阅读或者选择感兴趣的部分阅读：
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- [快速体验](#快速体验)
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- [数据构建](#数据构建)
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- [微调指南](#微调指南)
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- [部署指南](#部署指南)
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- [RAG](#rag检索增强生成pipeline)
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- 查看更多详情
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### 数据构建
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### 🍪快速体验
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- 请阅读[快速体验](docs/quick_start.md)查阅
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### 📌数据构建
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- 请阅读[数据构建指南](generate_data/tutorial.md)查阅
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- 微调用到的数据集见[datasets](datasets/data.json)
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### 微调指南
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### 🎨微调指南
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详见[微调指南](xtuner_config/README.md)
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### 部署指南
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### 🔧部署指南
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- Demo部署：详见[部署指南](demo/README.md)
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- 基于[LMDeploy](https://github.com/InternLM/lmdeploy/)的量化部署：详见[deploy](./deploy/lmdeploy.md)
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### RAG(检索增强生成)Pipeline
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### ⚙RAG(检索增强生成)Pipeline
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- 详见[RAG](./rag/)
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@ -304,6 +312,7 @@ git clone https://github.com/SmartFlowAI/EmoLLM.git
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- [闻星大佬（小助手）](https://github.com/vansin)
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- [扫地升（公众号宣传）](https://mp.weixin.qq.com/s/78lrRl2tlXEKUfElnkVx4A)
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- 阿布（北大心理学硕士）
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- [HatBoy](https://github.com/hatboy)
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<!-- links -->
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25
README_EN.md
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README_EN.md
@ -199,41 +199,33 @@ git clone https://github.com/SmartFlowAI/EmoLLM.git
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```
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1. Read in sequence or read sections you're interested in：
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- [File Directory Explanation](#file-directory-explanation)
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- [Quick Start](#quick-start)
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- [Data Construction](#data-construction)
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- [Fine-tuning Guide](#fine-tuning-guide)
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- [Deployment Guide](#deployment-guide)
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- [RAG](#rag-retrieval-augmented-generation-pipeline)
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- View More Details
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### File Directory Explanation
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```
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├─assets: Image Resources
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├─datasets: Dataset
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├─demo: demo scripts
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├─generate_data: Data Generation Guide
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│ └─xinghuo
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├─scripts: Some Available Tools
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└─xtuner_config：Fine-tuning Guide
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└─images
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```
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### 🍪Quick start
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- Please read [Quick Start](docs/quick_start_EN.md) to see.
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### Data Construction
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### 📌Data Construction
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- Please read the [Data Construction Guide ](generate_data/tutorial_EN.md)for reference.
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- The dataset used for this fine-tuning can be found at [datasets](datasets/data.json)
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### Fine-tuning Guide
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### 🎨Fine-tuning Guide
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For details, see the [fine-tuning guide](xtuner_config/README_EN.md)
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### Deployment Guide
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### 🔧Deployment Guide
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- Demo deployment: see [deployment guide](./demo/README_EN.md) for details.
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- Quantitative deployment based on [LMDeploy](https://github.com/InternLM/lmdeploy/): see [deploy](./deploy/lmdeploy_EN.md)
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### RAG (Retrieval Augmented Generation) Pipeline
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### ⚙RAG (Retrieval Augmented Generation) Pipeline
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- See [RAG](./rag/)
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@ -307,6 +299,7 @@ The project is licensed under the MIT License. Please refer to the details
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- [Vanin](https://github.com/vansin)
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- [Bloom up (WeChat Official Account Promotion)](https://mp.weixin.qq.com/s/78lrRl2tlXEKUfElnkVx4A)
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- Abu (M.A. in Psychology, Peking University)
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- [HatBoy](https://github.com/hatboy)
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<!-- links -->
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docs/quick_start.md
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### 1、部署环境
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- 操作系统：Ubuntu 22.04.4 LTS
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- CPU：Intel (R) Xeon (R) CPU E 5-2650，32G（在线 GPU 服务器）
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- 显卡：NVIDIA RTX 4060Ti 16G，NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2
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- Python 3.11.5
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### 2、默认部署步骤
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- 1、Clone 代码或者手动下载代码放置服务器：
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```
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git clone https://github.com/SmartFlowAI/EmoLLM.git
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```
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- 2、安装 Python 依赖库：
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```
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# cd EmoLLM
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# pip install -r requirements.txt
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```
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- 3、安装 oss2 库用于自动下载模型（requirements.txt 文件缺少该库）
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```
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# pip install oss2
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```
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- 4、【重要】下载模型目录，选择你想要使用的模型，手动下载去 openxlab（如 [EmoLLM_Model](https://openxlab.org.cn/models/detail/jujimeizuo/EmoLLM_Model)） 或者 Huggingface 等其他平台下载模型目录，将全部文件放在 `EmoLLM/model` 目录下。注意，这一步必须要有，否则自动下载会报错，打包下载时并不会下载 LFS（如 pytorch_model-00001-of-00008.bin）文件的完整文件只是一个引用而已。
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- 5、下载模型，特指模型中的 pytorch_model-XXX.bin 文件，可通过手动或者自动方式下载模型。
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- 5.1、模型手动下载则去 openxlab（如 [EmoLLM_Model](https://openxlab.org.cn/models/detail/jujimeizuo/EmoLLM_Model)） 或者其他平台下载模型，将全部文件放在 `EmoLLM/model` 目录下，然后修改 web_demo-aiwei.py 或者 web_internlm2.py 文件（app. Py 调用哪一个就修改哪一个文件）注释掉开头的下面代码，防止脚本自动重新下载模型：
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```
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# download(model_repo='ajupyter/EmoLLM_aiwei',
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# output='model')
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```
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- 5.2、模型自动下载，进入 EmoLLM 目录后有三个文件，分别是 app.py、web_demo-aiwei.py、web_internlm2.py。app.py 是调用后面两个脚本的。想要调用哪一个脚本就注释掉另外一个脚本。并运行如下
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```
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# python ./app.py
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```
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- 注意：默认情况下 web_demo-aiwei.py 自动下载的是 openxlab 平台的 ajupyter/EmoLLM_aiwei 模型，web_internlm2.py 下载的是 jujimeizuo/EmoLLM_Model 模型。脚本会自动将模型下载到 EmoLLM/model 目录，下载完成成功运行后，按照 5.1 的手动下载的操作修改 web_demo-aiwei.py 或者 web_internlm2.py 文件注释掉下载代码，防止下次运行重新下载。
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6、运行 app.py 后等待模型下载并且加载完毕，可通过浏览器访问： http://0.0.0.0:7860 地址访问模型 web 页面。可修改 app.py 文件修改 web 页面访问端口。
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7、替换模型，EmoLLM 提供了多种开源模型，分别上传至 openxlab、Huggingface 平台，有[爹系男友心理咨询师 ](https://openxlab.org.cn/models/detail/chg0901/EmoLLM_Daddy-like_BF)、[老母亲心理咨询师](https://huggingface.co/brycewang2018/EmoLLM-mother/tree/main)、[温柔御姐心理医生艾薇](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_aiwei)等角色，有 EmoLLM_internlm2_7b_full、EmoLLM-InternLM7B-base-10e 等多个模型可选择，目前评测 [EmoLLM_internlm2_7b_ful](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full) 模型效果较好。可重复步骤 4、5手动或自动下载相关模型放在 `EmoLLM/model` 目录下或者修改 web_demo-aiwei.py 或者 web_internlm2.py 文件的 download 函数仓库地址自动下载。
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### 1. Deployment Environment
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- Operating system: Ubuntu 22.04.4 LTS
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- CPU: Intel (R) Xeon (R) CPU E 5-2650, 32G
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- Graphics card: NVIDIA RTX 4060Ti 16G, NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2
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- Python 3.11.5
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### 2. Default Deployment Steps
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- 1. Clone the code or manually download the code and place it on the server:
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```
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git clone https://github.com/SmartFlowAI/EmoLLM.git
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```
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- 2. Install Python dependencies:
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```
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# cd EmoLLM
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# pip install -r requirements.txt
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```
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- 3. Install the oss2 library for automatic model download (missing from the requirements.txt file):
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```
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# pip install oss2
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```
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- 4. Important:Download the model catalog, select the model you want to use, to download the model catalog manually, go to openxlab (like [EmoLLM_Model](https://openxlab.org.cn/models/detail/jujimeizuo/EmoLLM_Model)) or Huggingface or other platforms, Place all files in the `EmoLLM/model` directory. Note that this step is mandatory, otherwise the automatic download will report an error, The package download does not download the full LFS (like pytorch_model-00001-of-00008.bin) file, just a reference to it.
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- 5. Download the model, specifically the pytorch_model-XXX.bin file in the model,
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models can be downloaded manually or automatically.
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- 5.1. Models can be downloaded manually from openxlab or other platforms, put all the files in the `EmoLLM/model` directory, and then modify the web_demo-aiwei.py or web_internlm2.py files (whichever one is called by app.py) by commenting out the following code at the beginning, to prevent the script from automatically re-downloading the model：
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```
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# download(model_repo='ajupyter/EmoLLM_aiwei',
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# output='model')
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```
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- 5.2. Automatic model download, There are three files in the EmoLLM directory, app.py、web_demo-aiwei.py、web_internlm2.py, app.py calls the last two scripts. Comment out the other script if you want to call it. Run:
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```
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# python ./app.py
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```
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- Note: By default, web_demo-aiwei.py automatically downloads the ajupyter/EmoLLM_aiwei model of openxlab platform, and web_internlm2.py downloads the jujimeizuo/EmoLLM_Model model. The script will automatically download the model to EmoLLM/model directory, after the download is completed and run successfully, follow the manual download operation in 5.1 to modify the web_demo-aiwei.py or web_internlm2.py file to comment out the download code, so as to prevent re-downloading in the next run.
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6、 Run app.py and wait for the model to download and load. Then access the model web page through your browser by going to: http://0.0.0.0:7860 address. You can modify the app.py file to change the web page access port.
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7. Use of other models, EmoLLM offers several versions of the open source model, uploaded to openxlab、Huggingface, and other platforms. There are roles such as the [father's boyfriend counselor](https://openxlab.org.cn/models/detail/chg0901/EmoLLM_Daddy-like_BF), [the old mother's counselor](https://huggingface.co/brycewang2018/EmoLLM-mother/tree/main), and [the gentle royal psychiatrist](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_aiwei). There are several models to choose from such as EmoLLM_internlm2_7b_full, EmoLLM-InternLM7B-base-10e and so on.
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Current review [EmoLLM_internlm2_7b_ful] (https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full) model works better. You can repeat steps 4 and 5 to download the model manually or automatically in the `EmoLLM/model` directory or modify the download function in web_demo-aiwei.py or web_internlm2.py to download the model automatically.
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