UpstageAI/evalverse-FastChat

UpstageAI/evalverse-FastChat

Description: Submodule of evalverse forked from lm-sys/FastChat

Language: Python

License: Apache-2.0

Stars: 0

Forks: 0

Open issues: 0

Created: 2024-03-28T03:01:38Z

Pushed: 2024-07-28T18:36:44Z

Default branch: main

Fork: no

Archived: yes

README:

FastChat

| **Demo** | **Discord** | **X** |

FastChat is an open platform for training, serving, and evaluating large language model based chatbots.

• FastChat powers Chatbot Arena (https://chat.lmsys.org/), serving over 6 million chat requests for 50+ LLMs.
• Chatbot Arena has collected over 200K 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

• [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](#vicuna-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](#longchat).
• [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](#install)
• [Model Weights](#model-weights)
• [Inference with Command Line Interface](#inference-with-command-line-interface)
• [Serving with Web GUI](#serving-with-web-gui)
• [API](#api)
• [Evaluation](#evaluation)
• [Fine-tuning](#fine-tuning)
• [Citation](#citation)

Install

Method 1: With pip

pip3 install "fschat[model_worker,webui]"

Method 2: From source

1. Clone this repository and navigate to the FastChat folder

git clone https://github.com/lm-sys/FastChat.git
cd FastChat

If you are running on Mac:

brew install rust cmake

2. Install Package

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 | python3 -m fastchat.serve.cli --model-path lmsys/vicuna-13b-v1.5-16k | lmsys/vicuna-13b-v1.5-16k | | 33B | python3 -m fastchat.serve.cli --model-path lmsys/vicuna-33b-v1.3 | lmsys/vicuna-33b-v1.3 |

Old weights: see [docs/vicuna_weights_version.md](docs/vicuna_weights_version.md) for all versions of weights and their differences.

Other Models

Besides Vicuna, we also released two additional models: LongChat and FastChat-T5. You can use the commands below to chat with them. They will automatically download the weights from Hugging Face repos.

| Model | Chat Command | Hugging Face Repo | | --- | --- | --- | | LongChat-7B | python3 -m fastchat.serve.cli --model-path lmsys/longchat-7b-32k-v1.5 | lmsys/longchat-7b-32k | | FastChat-T5-3B | python3 -m fastchat.serve.cli --model-path lmsys/fastchat-t5-3b-v1.0 | lmsys/fastchat-t5-3b-v1.0 |

Inference with Command Line Interface

(Experimental Feature: You can specify --style rich to enable rich text output and better text streaming quality for some non-ASCII content. This may not work properly on certain terminals.)

Supported Models

FastChat supports a wide range of models, including LLama 2, Vicuna, Alpaca, Baize, ChatGLM, Dolly, Falcon, FastChat-T5, GPT4ALL, Guanaco, MTP, OpenAssistant, OpenChat, RedPajama, StableLM, WizardLM, xDAN-AI and more.

See a complete list of supported models and instructions to add a new model [here](docs/model_support.md).

Single GPU

The command below requires around 14GB of GPU memory for Vicuna-7B and 28GB of GPU memory for Vicuna-13B. See the ["Not Enough Memory" section](#not-enough-memory) below if you do not have enough memory. --model-path can be a local folder or a Hugging Face repo name.

python3 -m fastchat.serve.cli --model-path lmsys/vicuna-7b-v1.5

Multiple GPUs

You can use model parallelism to aggregate GPU memory from multiple GPUs on the same machine.

python3 -m fastchat.serve.cli --model-path lmsys/vicuna-7b-v1.5 --num-gpus 2

Tips: Sometimes…