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README.md
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README.md
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## chat.guanjihuan.com
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本仓库主要记录这篇博文中的代码：https://www.guanjihuan.com/archives/38502 。
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这里把 https://chat.guanjihuan.com 的主要实现代码进行开源。代码参考各个开源大模型的 GitHub 或 HuggingFace 主页、大语言模型的 API 官网，以及 HuggingFace 和 Pytorch 的文档等。
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硬件要求：如果是本地 GPU 运行模型，还需要 Nvidia 显卡，至少 6G 显存。说明：这里只测试了几个模型，还有更多开源大模型，感兴趣的可以自行测试。通常，8G 显存的显卡可以量化地加载 7B 左右的模型（70亿参数）；16G 显存的显卡可以完整加载 7B 左右的模型（70亿参数）或量化地加载 14B 左右的模型（140亿参数）；更大参数空间的模型的运行需要更大显存的显卡。开源大模型的排行榜有：
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+ https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
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+ https://cevalbenchmark.com/static/leaderboard.html
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+ https://opencompass.org.cn/leaderboard-llm
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+ https://www.superclueai.com
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### 一、基础环境
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运行这里的代码需要安装 Python 环境，可以选择安装 Anaconda：https://www.anaconda.com 。
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Web 框架是使用 Streamlit：https://streamlit.io 、https://github.com/streamlit/streamlit 。
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Streamlit 的安装：
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```
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pip install streamlit
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```
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运行命令：
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```
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streamlit run web_demo.py
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```
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或
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```
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python -m streamlit run web_demo.py
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```
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如果是在公网IP下访问，并指定8501端口和黑色主题，那么运行命令为：
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```
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streamlit run web_demo.py --theme.base dark --server.port 8501 --server.address 0.0.0.0
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```
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如果是本地运行开源大语言模型，为了防止一些不必要的报错，可以更新一下操作系统的显卡驱动并重启：
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```
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sudo apt-get update
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sudo apt-get install ubuntu-drivers-common
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sudo ubuntu-drivers autoinstall
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```
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此外，更新一下 Pytorch（ [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/) ）也可以防止一些报错：
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```
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conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
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```
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### 二、本地运行开源大语言模型
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#### 1. 开源模型 ChatGLM
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ChatGLM3-6B 主页：https://github.com/THUDM/ChatGLM3 。 安装该模型依赖的环境：
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```
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pip install -r requirements.txt
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```
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模型文件下载：https://huggingface.co/THUDM/chatglm3-6b-32k ，放在目录 THUDM/chatglm3-6b-32k 下。
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显存/内存要求：量化加载大概要 6G 显存；默认加载大概需要 13G 显存；CPU加载大概需要 25G 内存。
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运行命令：
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```
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python -m streamlit run ./ChatGLM.py --theme.base dark --server.port 8501
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```
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如果量化加载时 bitsandbytes 报错，那么安装该软件包：pip install bitsandbytes
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#### 2. 开源模型 Qwen
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Qwen 主页：https://github.com/QwenLM/Qwen 。 安装该模型依赖的环境：
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```
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pip install -r requirements.txt
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```
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Qwen-7B-Chat-Int4 模型文件下载：https://huggingface.co/Qwen/Qwen-7B-Chat-Int4 ，放在目录 Qwen/Qwen-7B-Chat-Int4 下。
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Qwen-14B-Chat-Int4 模型文件下载：https://huggingface.co/Qwen/Qwen-14B-Chat-Int4 ，放在目录 Qwen/Qwen-14B-Chat-Int4 下。
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显存要求：Qwen-7B-Chat-Int4 大概需要 8G 显存；Qwen-14B-Chat-Int4 大概需要 12G 显存。
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运行命令：
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```
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python -m streamlit run ./Qwen.py --theme.base dark --server.port 8501
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```
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此外，如果运行有报错，可能还需要安装：
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```
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pip install optimum
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pip install auto-gptq
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pip install --upgrade s3fs aiobotocore botocore
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```
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#### 3. 开源模型 InternLM
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InternLM 主页：https://github.com/InternLM/InternLM 。运行代码时，需要调用其中的 tools 文件夹。
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internlm-chat-7b 模型文件下载：https://huggingface.co/internlm/internlm-chat-7b ，放在 internlm/internlm-chat-7b 目录下。说明：提供的代码是加载 internlm-chat-7b 模型， 目前已经有 internlm2-chat-7b 模型，但个人还未测试。internlm2-chat-7b 模型文件下载：https://huggingface.co/internlm/internlm2-chat-7b
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显存要求：大概需要 7G 的显存。
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运行命令：
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```
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python -m streamlit run ./InternLM.py --theme.base dark --server.port 8501
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```
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#### 4. 开源模型 使用 Ollama 调用 llama3.2
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Ollama 部分参考这篇：https://www.guanjihuan.com/archives/43861
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这里的代码给出了具体的 Streamlit 的实现。
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#### 5. ……
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### 三、使用大语言模型 API
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#### 1. 智谱 - ChatGLM_Turbo
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智谱 API key 获取（收费，可免费试用）：https://maas.aminer.cn
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运行命令：
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```
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python -m streamlit run ./ChatGLM_Turbo.py --theme.base dark --server.port 8501
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```
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说明：当前代码只对 pip install zhipuai==1.0.7 有效，对最新版本不兼容。另外，早期使用的模型调用是 model="chatglm_turbo"， 官网文档最新的模型是 model="glm-3-turbo"。工单客服回复内容为：“chatglm_turbo与glm-3-turbo是不同的模型，glm-3-turbo理论上能力优于chatglm_turbo，且价格更便宜”。个人推荐的是 glm-4-flash 模型。
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#### 2. 阿里 - Qwen_Turbo
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阿里 API key 获取（有的收费，有的免费）：https://dashscope.aliyun.com
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运行命令：
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```
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python -m streamlit run ./Qwen_Turbo.py --theme.base dark --server.port 8501
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```
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需要安装软件包：pip install dashscope
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#### 3. 腾讯 - 混元大模型
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腾讯 API key 获取（有的收费，有的免费）：https://cloud.tencent.com/product/hunyuan
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运行命令：
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```
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python -m streamlit run ./Hunyuan_Lite.py --theme.base dark --server.port 8501
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```
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#### 4. 讯飞 - 星火大模型
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讯飞 API key 获取（有的收费，有的免费）：https://xinghuo.xfyun.cn
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运行命令：
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```
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python -m streamlit run ./星火大模型.py --theme.base dark --server.port 8501
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```
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#### 5. 百度 - ERNIE_Speed_128K
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百度千帆大模型平台 API key 获取（有的收费，有的免费）：https://console.bce.baidu.com/qianfan/overview
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运行命令：
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```
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python -m streamlit run ./ERNIE_Speed_128K.py --theme.base dark --server.port 8501
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```
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#### 6. 零一万物 - Yi_Spark
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零一万物大模型开放平台（有免费额度）：https://platform.lingyiwanwu.com
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需要安装 OpenAI 软件包：
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```
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pip install openai
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```
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运行命令：
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```
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python -m streamlit run ./Yi_Spark.py --theme.base dark --server.port 8501
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```
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#### 7. 火山引擎 - Doubao_lite_32k
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豆包大模型 - 火山引擎（有免费额度）：https://www.volcengine.com/product/doubao
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需要安装：
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```
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pip install volcengine-python-sdk
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```
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运行命令：
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```
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python -m streamlit run ./Doubao_lite_32k.py --theme.base dark --server.port 8501
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```
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#### 8. OpenAI - GPT_3.5_Turbo
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OpenAI 的 API 接口（需要海外的 IP 地址以及海外银行卡）：https://platform.openai.com
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需要安装 OpenAI 软件包：
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```
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pip install openai
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```
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运行命令：
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```
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python -m streamlit run ./GPT_3.5_Turbo.py --theme.base dark --server.port 8501
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```
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#### 9. ……
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本仓库记录这篇博文中的代码：https://www.guanjihuan.com/archives/38502
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78
模型API - DeepSeek - DeepSeek_Chat/DeepSeek_Chat.py
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模型API - DeepSeek - DeepSeek_Chat/DeepSeek_Chat.py
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"""
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This code is supported by the website: https://www.guanjihuan.com
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The newest version of this code is on the web page: https://www.guanjihuan.com/archives/38502
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"""
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import streamlit as st
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st.set_page_config(
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page_title="Chat",
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layout='wide'
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)
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import openai
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API_BASE = "https://api.deepseek.com"
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API_KEY = "your key"
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with st.sidebar:
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with st.expander('参数', expanded=True):
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top_p = st.slider('top_p', 0.01, 1.0, step=0.01, value=0.8, key='top_p_session')
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temperature = st.slider('temperature', 0.51, 1.0, step=0.01, value=0.85, key='temperature_session')
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def reset_parameter():
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st.session_state['top_p_session'] = 0.8
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st.session_state['temperature_session'] = 0.85
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reset_parameter_button = st.button('重置', on_click=reset_parameter)
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prompt = st.chat_input("在这里输入您的命令")
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def clear_all():
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st.session_state.messages = []
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st.session_state.ai_response = []
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if 'messages' not in st.session_state:
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st.session_state.messages = []
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if 'ai_response' not in st.session_state:
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st.session_state.ai_response = []
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for ai_response in st.session_state.ai_response:
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with st.chat_message(ai_response["role"], avatar=ai_response.get("avatar")):
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st.markdown(ai_response["content"])
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prompt_placeholder = st.chat_message("user", avatar='user')
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with st.chat_message("robot", avatar="assistant"):
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message_placeholder = st.empty()
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def response_of_deepseek_chat(prompt):
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st.session_state.messages.append({'role': 'user', 'content': prompt})
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client = openai.OpenAI(
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api_key=API_KEY,
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base_url=API_BASE
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)
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completion = client.chat.completions.create(
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model="deepseek-chat",
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messages=st.session_state.messages,
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stream=True,
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temperature=temperature,
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top_p=top_p,
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)
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full_content = ''
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for chunk in completion:
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response = chunk.choices[0].delta.content or ""
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full_content += response
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message_placeholder.markdown(full_content)
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if stop_button:
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break
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st.session_state.messages.append({'role': 'assistant',
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'content': full_content})
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st.session_state.ai_response.append({"role": "robot", "content": full_content, "avatar": "assistant"})
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return full_content
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if prompt:
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prompt_placeholder.markdown(prompt)
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st.session_state.ai_response.append({"role": "user", "content": prompt, "avatar": 'user'})
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stop = st.empty()
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stop_button = stop.button('停止', key='break_response')
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response_of_deepseek_chat(prompt)
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stop.empty()
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button_clear = st.button("清空", on_click=clear_all, key='clear')
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92
模型API - DeepSeek - DeepSeek_Reasoner/DeepSeek_Reasoner.py
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模型API - DeepSeek - DeepSeek_Reasoner/DeepSeek_Reasoner.py
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"""
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This code is supported by the website: https://www.guanjihuan.com
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The newest version of this code is on the web page: https://www.guanjihuan.com/archives/38502
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"""
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import streamlit as st
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st.set_page_config(
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page_title="Chat",
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layout='wide'
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)
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import openai
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API_BASE = "https://api.deepseek.com"
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API_KEY = "your key"
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with st.sidebar:
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with st.expander('参数', expanded=True):
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top_p = st.slider('top_p', 0.01, 1.0, step=0.01, value=0.8, key='top_p_session')
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temperature = st.slider('temperature', 0.51, 1.0, step=0.01, value=0.85, key='temperature_session')
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def reset_parameter():
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st.session_state['top_p_session'] = 0.8
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st.session_state['temperature_session'] = 0.85
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reset_parameter_button = st.button('重置', on_click=reset_parameter)
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prompt = st.chat_input("在这里输入您的命令")
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def clear_all():
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st.session_state.messages = []
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st.session_state.ai_response = []
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if 'messages' not in st.session_state:
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st.session_state.messages = []
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if 'ai_response' not in st.session_state:
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st.session_state.ai_response = []
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for ai_response in st.session_state.ai_response:
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with st.chat_message(ai_response["role"], avatar=ai_response.get("avatar")):
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st.markdown(ai_response["content"])
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prompt_placeholder = st.chat_message("user", avatar='user')
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with st.chat_message("robot", avatar="assistant"):
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message_placeholder = st.empty()
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def response_of_deepseek_chat(prompt):
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st.session_state.messages.append({'role': 'user', 'content': prompt})
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client = openai.OpenAI(
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api_key=API_KEY,
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base_url=API_BASE
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)
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completion = client.chat.completions.create(
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model="deepseek-reasoner",
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messages=st.session_state.messages,
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stream=True,
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temperature=temperature,
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top_p=top_p,
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)
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full_content = ''
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all_full_content = ''
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think_or_not = 1
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answer_or_not = 1
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for chunk in completion:
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response = chunk.choices[0].delta.content or ""
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reasoning_content = chunk.choices[0].delta.reasoning_content
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if response == None:
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if think_or_not == 1:
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all_full_content += '[开始思考]\n\n'
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think_or_not = 0
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all_full_content += reasoning_content
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else:
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if answer_or_not == 1:
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all_full_content += '\n\n[结束思考]\n\n'
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answer_or_not = 0
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all_full_content += response
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full_content += response
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message_placeholder.markdown(all_full_content)
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if stop_button:
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break
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st.session_state.messages.append({'role': 'assistant',
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'content': full_content})
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st.session_state.ai_response.append({"role": "robot", "content": all_full_content, "avatar": "assistant"})
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return all_full_content
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if prompt:
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prompt_placeholder.markdown(prompt)
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st.session_state.ai_response.append({"role": "user", "content": prompt, "avatar": 'user'})
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stop = st.empty()
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stop_button = stop.button('停止', key='break_response')
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response_of_deepseek_chat(prompt)
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stop.empty()
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button_clear = st.button("清空", on_click=clear_all, key='clear')
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Loading…
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