## 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)
## Install
### Method 1: With pip
```bash
pip3 install fschat
```
### 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 Weights
### Vicuna Weights
We release [Vicuna](https://vicuna.lmsys.org/) weights as delta weights to comply with the LLaMA model license.
You can add our delta to the original LLaMA weights to obtain the Vicuna weights. Instructions:
1. Get the original LLaMA weights in the huggingface format by following the instructions [here](https://huggingface.co/docs/transformers/main/model_doc/llama).
2. Use the following scripts to get Vicuna weights by applying our delta. They will automatically download delta weights from our Hugging Face [account](https://huggingface.co/lmsys).
**NOTE**:
Weights v1.1 are only compatible with ```transformers>=4.28.0``` and ``fschat >= 0.2.0``.
Please update your local packages accordingly. If you follow the above commands to do a fresh install, then you should get all the correct versions.
#### Vicuna-7B
This conversion command needs around 30 GB of CPU RAM.
See the "Low CPU Memory Conversion" section below if you do not have enough memory.
```bash
python3 -m fastchat.model.apply_delta \
--base-model-path /path/to/llama-7b \
--target-model-path /output/path/to/vicuna-7b \
--delta-path lmsys/vicuna-7b-delta-v1.1
```
#### Vicuna-13B
This conversion command needs around 60 GB of CPU RAM.
See the "Low CPU Memory Conversion" section below if you do not have enough memory.
```bash
python3 -m fastchat.model.apply_delta \
--base-model-path /path/to/llama-13b \
--target-model-path /output/path/to/vicuna-13b \
--delta-path lmsys/vicuna-13b-delta-v1.1
```
#### Old weights
See [docs/vicuna_weights_version.md](docs/vicuna_weights_version.md) for all versions of weights and their differences.
#### Low CPU Memory Conversion
You can try these methods to reduce the CPU RAM requirement of weight conversion.
1. Append `--low-cpu-mem` to the commands above, which will split large weight files into smaller ones and use the disk as temporary storage. This can keep the peak memory at less than 16GB.
2. Create a large swap file and rely on the operating system to automatically utilize the disk as virtual memory.
### FastChat-T5
Simply run the line below to start chatting.
It will automatically download the weights from a Hugging Face [repo](https://huggingface.co/lmsys/fastchat-t5-3b-v1.0).
```bash
python3 -m fastchat.serve.cli --model-path 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
The following models are tested:
- Vicuna, Alpaca, LLaMA, Koala
- [lmsys/fastchat-t5-3b-v1.0](https://huggingface.co/lmsys/fastchat-t5)
- [BlinkDL/RWKV-4-Raven](https://huggingface.co/BlinkDL/rwkv-4-raven)
- [databricks/dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b)
- [OpenAssistant/oasst-sft-1-pythia-12b](https://huggingface.co/OpenAssistant/oasst-sft-1-pythia-12b)
- [project-baize/baize-lora-7B](https://huggingface.co/project-baize/baize-lora-7B)
- [StabilityAI/stablelm-tuned-alpha-7b](https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b)
- [THUDM/chatglm-6b](https://huggingface.co/THUDM/chatglm-6b)
#### Single GPU
The command below requires around 28GB of GPU memory for Vicuna-13B and 14GB of GPU memory for Vicuna-7B.
See the "No Enough Memory" section below if you do not have enough memory.
```
python3 -m fastchat.serve.cli --model-path /path/to/model/weights
```
#### 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 /path/to/model/weights --num-gpus 2
```
#### CPU Only
This runs on the CPU only and does not require GPU. It requires around 60GB of CPU memory for Vicuna-13B and around 30GB of CPU memory for Vicuna-7B.
```
python3 -m fastchat.serve.cli --model-path /path/to/model/weights --device cpu
```
#### Metal Backend (Mac Computers with Apple Silicon or AMD GPUs)
Use `--device mps` to enable GPU acceleration on Mac computers (requires torch >= 2.0).
Use `--load-8bit` to turn on 8-bit compression.
```
python3 -m fastchat.serve.cli --model-path /path/to/model/weights --device mps --load-8bit
```
Vicuna-7B can run on a 32GB M1 Macbook with 1 - 2 words / second.
#### No Enough Memory
If you do not have enough memory, you can enable 8-bit compression by adding `--load-8bit` to commands above.
This can reduce memory usage by around half with slightly degraded model quality.
It is compatible with the CPU, GPU, and Metal backend.
Vicuna-13B with 8-bit compression can run on a single NVIDIA 3090/4080/T4/V100(16GB) GPU.
```
python3 -m fastchat.serve.cli --model-path /path/to/model/weights --load-8bit
```
In addition to that, you can add `--cpu-offloading` to commands above to offload weights that don't fit on your GPU onto the CPU memory. This requires 8-bit compression to be enabled and the bitsandbytes package to be installed, which is only available on linux operating systems.
## Serving with Web GUI
To serve using the web UI, you need three main components: web servers that interface with users, model workers that host one or more models, and a controller to coordinate the webserver and model workers. Here are the commands to follow in your terminal:
#### Launch the controller
```bash
python3 -m fastchat.serve.controller
```
This controller manages the distributed workers.
#### Launch the model worker
```bash
python3 -m fastchat.serve.model_worker --model-path /path/to/model/weights
```
Wait until the process finishes loading the model and you see "Uvicorn running on ...". You can launch multiple model workers to serve multiple models concurrently. The model worker will connect to the controller automatically.
To ensure that your model worker is connected to your controller properly, send a test message using the following command:
```bash
python3 -m fastchat.serve.test_message --model-name vicuna-7b
```
You will see a short output.
#### Launch the Gradio web server
```bash
python3 -m fastchat.serve.gradio_web_server
```
This is the user interface that users will interact with.
By following these steps, you will be able to serve your models using the web UI. You can open your browser and chat with a model now.
## API
### Huggingface Generation APIs
See [fastchat/serve/huggingface_api.py](fastchat/serve/huggingface_api.py)
### OpenAI-compatible RESTful APIs & SDK
(Experimental. We will keep improving the API and SDK.)
#### Chat Completion
Reference: https://platform.openai.com/docs/api-reference/chat/create
Some features/compatibilities to be implemented:
- [ ] streaming
- [ ] support of some parameters like `top_p`, `presence_penalty`
- [ ] proper error handling (e.g. model not found)
- [ ] the return value in the client SDK could be used like a dict
**RESTful API Server**
First, launch the controller
```bash
python3 -m fastchat.serve.controller
```
Then, launch the model worker(s)
```bash
python3 -m fastchat.serve.model_worker --model-name 'vicuna-7b-v1.1' --model-path /path/to/vicuna/weights
```
Finally, launch the RESTful API server
```bash
export FASTCHAT_CONTROLLER_URL=http://localhost:21001
python3 -m fastchat.serve.api --host localhost --port 8000
```
Test the API server
```bash
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "vicuna-7b-v1.1",
"messages": [{"role": "user", "content": "Hello!"}]
}'
```
**Client SDK**
Assuming environment variable `FASTCHAT_BASEURL` is set to the API server URL (e.g., `http://localhost:8000`), you can use the following code to send a request to the API server:
```python
import os
from fastchat import client
client.set_baseurl(os.getenv("FASTCHAT_BASEURL"))
completion = client.ChatCompletion.create(
model="vicuna-7b-v1.1",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(completion.choices[0].message)
```
## Evaluation
Our AI-enhanced evaluation pipeline is based on GPT-4. This section provides a high-level summary of the pipeline. For detailed instructions, please refer to the [evaluation](fastchat/eval) documentation.
### Pipeline Steps
1. Generate answers from different models: Use `qa_baseline_gpt35.py` for ChatGPT, or specify the model checkpoint and run `get_model_answer.py` for Vicuna and other models.
2. Generate reviews with GPT-4: Use GPT-4 to generate reviews automatically. This step can also be performed manually if the GPT-4 API is not available to you.
3. Generate visualization data: Run `generate_webpage_data_from_table.py` to generate data for a static website, which allows you to visualize the evaluation data.
4. Visualize the data: Serve a static website under the `webpage` directory. You can use `python3 -m http.server` to serve the website locally.
### Data Format and Contribution
We use a data format encoded with JSON Lines for evaluation. The format includes information on models, prompts, reviewers, questions, answers, and reviews.
You can customize the evaluation process or contribute to our project by accessing the relevant [data](fastchat/eval/table/).
For detailed instructions, please refer to the [evaluation](fastchat/eval) documentation.
## Fine-tuning
### Data
Vicuna is created by fine-tuning a LLaMA base model using approximately 70K user-shared conversations gathered from ShareGPT.com with public APIs. To ensure data quality, we convert the HTML back to markdown and filter out some inappropriate or low-quality samples. Additionally, we divide lengthy conversations into smaller segments that fit the model's maximum context length. For detailed instructions to clean the ShareGPT data, check out [here](docs/commands/data_cleaning.md).
Due to some concerns, we may not release the ShareGPT dataset at the moment. If you would like to try the fine-tuning code, you can run it with some dummy questions in [dummy.json](playground/data/dummy.json). You can follow the same format and plug in your own data.
### Code and Hyperparameters
Our code is based on [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) with additional support for multi-round conversations.
We use similar hyperparameters as the Stanford Alpaca.
| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
| --- | ---: | ---: | ---: | ---: | ---: |
| Vicuna-13B | 128 | 2e-5 | 3 | 2048 | 0 |
### Fine-tuning Vicuna-7B with Local GPUs
You can use the following command to train Vicuna-7B with 4 x A100 (40GB).
```bash
torchrun --nproc_per_node=4 --master_port=20001 fastchat/train/train_mem.py \
--model_name_or_path ~/model_weights/llama-7b \
--data_path playground/data/dummy.json \
--bf16 True \
--output_dir output \
--num_train_epochs 3 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 2 \
--gradient_accumulation_steps 16 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 1200 \
--save_total_limit 10 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap" \
--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--lazy_preprocess True
```
If you meet out-of-memory during model saving, see solutions [here](https://github.com/pytorch/pytorch/issues/98823).
### Fine-tuning on Any Cloud with SkyPilot
[SkyPilot](https://github.com/skypilot-org/skypilot) is a framework built by UC Berkeley for easily and cost effectively running ML workloads on any cloud (AWS, GCP, Azure, Lambda, etc.).
To use SkyPilot, install it with the following command and setup the cloud credentials locally following the instructions [here](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html).
```bash
# Install skypilot from the master branch
pip install git+https://github.com/skypilot-org/skypilot.git
```
#### Vicuna
Vicuna can be trained on 8 A100 GPUs with 80GB memory. The following command will automatically launch a node satisfying the requirement, setup and run the training job on it.
```bash
sky launch -c vicuna -s scripts/train-vicuna.yaml --env WANDB_API_KEY
```
Other options are also valid:
```bash
# Launch it on managed spot to save 3x cost (train Vicuna-13B with around $300)
sky spot launch -n vicuna scripts/train-vicuna.yaml --env WANDB_API_KEY
# Train a 7B model
sky launch -c vicuna -s scripts/train-vicuna.yaml --env WANDB_API_KEY --env MODEL_SIZE=7
```
Note: Please make sure the `WANDB_API_KEY` has been setup on your local machine. You can find the API key on your [wandb profile page](https://wandb.ai/authorize). If you would like to train the model without using wandb, you can replace the `--env WANDB_API_KEY` flag with `--env WANDB_MODE=offline`.