README

FastChat
| Demo | Discord | X |

FastChat is an open platform for training, serving, and evaluating large language model based chatbots.
- FastChat powers Chatbot Arena (lmarena.ai), serving over 10 million chat requests for 70+ LLMs.
- Chatbot Arena has collected over 1.5M human votes from side-by-side LLM battles to compile an online LLM Elo leaderboard.

FastChat's core features include:
- The training and evaluation code for state-of-the-art models (e.g., Vicuna, MT-Bench).
- A distributed multi-model serving system with web UI and OpenAI-compatible RESTful APIs.

 News
- [2024/03] 🔥 We released Chatbot Arena technical report.
- [2023/09] We released LMSYS-Chat-1M, a large-scale real-world LLM conversation dataset. Read the report.
- [2023/08] We released Vicuna v1.5 based on Llama 2 with 4K and 16K context lengths. Download weights.
- [2023/07] We released Chatbot Arena Conversations, a dataset containing 33k conversations with human preferences. Download it here.


More

- [2023/08] We released LongChat v1.5 based on Llama 2 with 32K context lengths. Download weights.
- [2023/06] We introduced MT-bench, a challenging multi-turn question set for evaluating chatbots. Check out the blog post.
- [2023/06] We introduced LongChat, our long-context chatbots and evaluation tools. Check out the blog post.
- [2023/05] We introduced Chatbot Arena for battles among LLMs. Check out the blog post.
- [2023/03] We released Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90% ChatGPT Quality. Check out the blog post.





 Contents
- Install
- Model Weights
- Inference with Command Line Interface
- Serving with Web GUI
- API
- Evaluation
- Fine-tuning
- Citation

 Install

 Method 1: With pip

bash
pip3 install "fschat[model_worker,webui]"


 Method 2: From source

1. Clone this repository and navigate to the FastChat folder
bash
git clone https://github.com/lm-sys/FastChat.git
cd FastChat


If you are running on Mac:
bash
brew install rust cmake


2. Install Package
bash
pip3 install --upgrade pip   enable PEP 660 support
pip3 install -e ".[model_worker,webui]"


 Model Weights
 Vicuna Weights
Vicuna is based on Llama 2 and should be used under Llama's model license.

You can use the commands below to start chatting. It will automatically download the weights from Hugging Face repos.
Downloaded weights are stored in a .cache folder in the user's home folder (e.g., ~/.cache/huggingface/hub/).

See more command options and how to handle out-of-memory in the "Inference with Command Line Interface" section below.

NOTE: transformers=4.31 is required for 16K versions.

| Size | Chat Command | Hugging Face Repo |
| ---  | --- | --- |
| 7B   | python3 -m fastchat.serve.cli --model-path lmsys/vicuna-7b-v1.5  | lmsys/vicuna-7b-v1.5   |
| 7B-16k   | python3 -m fastchat.serve.cli --model-path lmsys/vicuna-7b-v1.5-16k  | lmsys/vicuna-7b-v1.5-16k   |
| 13B  | python3 -m fastchat.serve.cli --model-path lmsys/vicuna-13b-v1.5 | lmsys/vicuna-13b-v1.5 |
| 13B-16k  | pyt

Top issues

Latest release

Highlights
- Added SGLang worker for vision language models, lower latency and higher throughput https://github.com/lm-sys/FastChat/pull/2928
- Vision langauge WebUI https://github.com/lm-sys/FastChat/pull/2960
- OpenAI-compatible API server now supports image input https://github.com/lm-sys/FastChat/pull/2928
- Added LightLLM worker for higher throughput https://github.com/lm-sys/FastChat/blob/main/docs/lightllm_integration.md
- Added Apple MLX worker https://github.com/lm-sys/FastChat/pull/2940

 What's Changed
 fix specify local path issue use model from www.modelscope.cn by @liuyhw