melt / docs /vllm_integration.md
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# vLLM Integration
You can use [vLLM](https://vllm.ai/) as an optimized worker implementation in FastChat.
It offers advanced continuous batching and a much higher (~10x) throughput.
See the supported models [here](https://vllm.readthedocs.io/en/latest/models/supported_models.html).
## Instructions
1. Install vLLM.
```
pip install vllm
```
2. When you launch a model worker, replace the normal worker (`fastchat.serve.model_worker`) with the vLLM worker (`fastchat.serve.vllm_worker`). All other commands such as controller, gradio web server, and OpenAI API server are kept the same.
```
python3 -m fastchat.serve.vllm_worker --model-path lmsys/vicuna-7b-v1.5
```
If you see tokenizer errors, try
```
python3 -m fastchat.serve.vllm_worker --model-path lmsys/vicuna-7b-v1.5 --tokenizer hf-internal-testing/llama-tokenizer
```
If you use an AWQ quantized model, try
'''
python3 -m fastchat.serve.vllm_worker --model-path TheBloke/vicuna-7B-v1.5-AWQ --quantization awq
'''