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https://github.com/xusenlinzy/api-for-open-llm

Openai style api for open large language models, using LLMs just as chatgpt! Support for LLaMA, LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, Xverse, SqlCoder, CodeLLaMA, ChatGLM, ChatGLM2, ChatGLM3 etc. 开源大模型的统一后端接口
https://github.com/xusenlinzy/api-for-open-llm

baichuan chatglm code-llama docker internlm langchain llama llama2 llms nlp openai qwen sqlcoder xverse

Last synced: 29 days ago
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Openai style api for open large language models, using LLMs just as chatgpt! Support for LLaMA, LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, Xverse, SqlCoder, CodeLLaMA, ChatGLM, ChatGLM2, ChatGLM3 etc. 开源大模型的统一后端接口

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README

        

# API for Open LLMs









![llm.png](assets/images/llm.png)

图片来自于论文: [A Survey of Large Language Models](https://arxiv.org/pdf/2303.18223.pdf)

## 📢 新闻

+ 【2024.06.13】 支持 `MiniCPM-Llama3-V-2_5` 模型,修改环境变量 `MODEL_NAME=minicpm-v` `PROMPT_NAME=minicpm-v` `DTYPE=bfloat16`

+ 【2024.06.12】 支持 `GLM-4V` 模型,修改环境变量 `MODEL_NAME=glm-4v` `PROMPT_NAME=glm-4v` `DTYPE=bfloat16`, 测试示例见 [glm4v](./tests/glm4v.py)

+ 【2024.06.08】 已支持 `QWEN2` 模型,修改环境变量 `MODEL_NAME=qwen2` `PROMPT_NAME=qwen2`

+ 【2024.06.05】 支持 `GLM4` 模型,修改环境变量 `MODEL_NAME=chatglm4` `PROMPT_NAME=chatglm4`

+ 【2024.04.18】 支持 `Code Qwen` 模型,[sql问答demo](https://github.com/xusenlinzy/api-for-open-llm/tree/master/streamlit-demo/streamlit_gallery/components/sql_chat)

+ 【2024.04.16】 支持 `Rerank` 重排序模型,[使用方式](./docs/RAG.md)

+ 【2024.02.26】 `QWEN1.5` 模型需要修改环境变量 `MODEL_NAME=qwen2` `PROMPT_NAME=qwen2`

更多新闻和历史请转至 [此处](https://github.com/xusenlinzy/api-for-open-llm/blob/master/docs/NEWS.md)

---

**此项目主要内容**

此项目为开源大模型的推理实现统一的后端接口,与 `OpenAI` 的响应保持一致,具有以下特性:

+ ✨ 以 `OpenAI ChatGPT API` 的方式调用各类开源大模型

+ 🖨️ 支持流式响应,实现打印机效果

+ 📖 实现文本嵌入模型,为文档知识问答提供支持

+ 🦜️ 支持大规模语言模型开发工具 [`langchain` ](https://github.com/hwchase17/langchain) 的各类功能

+ 🙌 只需要简单的修改环境变量即可将开源模型作为 `chatgpt` 的替代模型,为各类应用提供后端支持

+ 🚀 支持加载经过自行训练过的 `lora` 模型

+ ⚡ 支持 [vLLM](https://github.com/vllm-project/vllm) 推理加速和处理并发请求

## 内容导引

| 章节 | 描述 |
|:-----------------------------------------------------------------------------------------------:|:-----------------------------:|
| [💁🏻‍♂支持模型](https://github.com/xusenlinzy/api-for-open-llm#-支持模型) | 此项目支持的开源模型以及简要信息 |
| [🚄启动方式](https://github.com/xusenlinzy/api-for-open-llm/blob/master/docs/SCRIPT.md) | 启动模型的环境配置和启动命令 |
| [⚡vLLM启动方式](https://github.com/xusenlinzy/api-for-open-llm/blob/master/docs/VLLM_SCRIPT.md) | 使用 `vLLM` 启动模型的环境配置和启动命令 |
| [💻调用方式](https://github.com/xusenlinzy/api-for-open-llm#-使用方式) | 启动模型之后的调用方式 |
| [❓常见问题](https://github.com/xusenlinzy/api-for-open-llm/blob/master/docs/FAQ.md) | 一些常见问题的回复 |

## 🐼 支持模型

**语言模型**

| 模型 | 模型参数大小 |
|----------------------------------------------------|----------------------------------|
| [Baichuan](https://huggingface.co/baichuan-inc) | 7B/13B |
| [ChatGLM](https://huggingface.co/THUDM) | 6B |
| [DeepSeek](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B |
| [InternLM](https://huggingface.co/internlm) | 7B/20B |
| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B |
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B |
| [LLaMA-3](https://huggingface.co/meta-llama) | 8B/70B |
| [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B |
| [Qwen1.5](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/32B/72B/110B |
| [Qwen2](https://huggingface.co/Qwen) | 0.5B/1.5B/7B/57B/72B |
| [Yi (1/1.5)](https://huggingface.co/01-ai) | 6B/9B/34B |

启动方式详见 [vLLM启动方式](https://github.com/xusenlinzy/api-for-open-llm/blob/master/docs/VLLM_SCRIPT.md)、[transformers启动方式](https://github.com/xusenlinzy/api-for-open-llm/blob/master/docs/SCRIPT.md)

**嵌入模型**

| 模型 | 维度 | 权重链接 |
|:-------------------------:|:----:|:-----------------------------------------------------------------------------------:|
| bge-large-zh | 1024 | [bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) |
| m3e-large | 1024 | [moka-ai/m3e-large](https://huggingface.co/moka-ai/m3e-large) |
| text2vec-large-chinese | 1024 | [text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese) |
| bce-embedding-base_v1(推荐) | 768 | [bce-embedding-base_v1](https://huggingface.co/maidalun1020/bce-embedding-base_v1) |

## 🤖 使用方式

### 环境变量

+ `OPENAI_API_KEY`: 此处随意填一个字符串即可

+ `OPENAI_API_BASE`: 后端启动的接口地址,如:http://192.168.0.xx:80/v1

### [聊天界面](./applications)

```shell
cd streamlit-demo
pip install -r requirements.txt
streamlit run streamlit_app.py
```

![img.png](assets/images/demo.png)

### [openai v1.1.0](https://github.com/openai/openai-python)

👉 Chat Completions

```python
from openai import OpenAI

client = OpenAI(
api_key="EMPTY",
base_url="http://192.168.20.59:7891/v1/",
)

# Chat completion API
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "你好",
}
],
model="gpt-3.5-turbo",
)
print(chat_completion)
# 你好👋!我是人工智能助手 ChatGLM3-6B,很高兴见到你,欢迎问我任何问题。

# stream = client.chat.completions.create(
# messages=[
# {
# "role": "user",
# "content": "感冒了怎么办",
# }
# ],
# model="gpt-3.5-turbo",
# stream=True,
# )
# for part in stream:
# print(part.choices[0].delta.content or "", end="", flush=True)
```

👉 Completions

```python
from openai import OpenAI

client = OpenAI(
api_key="EMPTY",
base_url="http://192.168.20.59:7891/v1/",
)

# Chat completion API
completion = client.completions.create(
model="gpt-3.5-turbo",
prompt="你好",
)
print(completion)
# 你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
```

👉 Embeddings

```python
from openai import OpenAI

client = OpenAI(
api_key="EMPTY",
base_url="http://192.168.20.59:7891/v1/",
)

# compute the embedding of the text
embedding = client.embeddings.create(
input="你好",
model="text-embedding-ada-002"
)
print(embedding)

```

### 可接入的项目

**通过修改 `OPENAI_API_BASE` 环境变量,大部分的 `chatgpt` 应用和前后端项目都可以无缝衔接!**

+ [ChatGPT-Next-Web: One-Click to deploy well-designed ChatGPT web UI on Vercel](https://github.com/Yidadaa/ChatGPT-Next-Web)

```shell
docker run -d -p 3000:3000 \
-e OPENAI_API_KEY="sk-xxxx" \
-e BASE_URL="http://192.168.0.xx:80" \
yidadaa/chatgpt-next-web
```

![web](assets/images/web.png)

+ [dify: An easy-to-use LLMOps platform designed to empower more people to create sustainable, AI-native applications](https://github.com/langgenius/dify)

```shell
# 在docker-compose.yml中的api和worker服务中添加以下环境变量
OPENAI_API_BASE: http://192.168.0.xx:80/v1
DISABLE_PROVIDER_CONFIG_VALIDATION: 'true'
```

![dify](assets/images/dify.png)

## 📜 License

此项目为 `Apache 2.0` 许可证授权,有关详细信息,请参阅 [LICENSE](LICENSE) 文件。

## 🚧 References

+ [ChatGLM: An Open Bilingual Dialogue Language Model](https://github.com/THUDM/ChatGLM-6B)

+ [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://arxiv.org/abs/2211.05100)

+ [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971v1)

+ [Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)

+ [Phoenix: Democratizing ChatGPT across Languages](https://github.com/FreedomIntelligence/LLMZoo)

+ [MOSS: An open-sourced plugin-augmented conversational language model](https://github.com/OpenLMLab/MOSS)

+ [FastChat: An open platform for training, serving, and evaluating large language model based chatbots](https://github.com/lm-sys/FastChat)

+ [LangChain: Building applications with LLMs through composability](https://github.com/hwchase17/langchain)

+ [ChuanhuChatgpt](https://github.com/GaiZhenbiao/ChuanhuChatGPT)

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=xusenlinzy/api-for-open-llm&type=Date)](https://star-history.com/#xusenlinzy/api-for-open-llm&Date)