| # 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 | |
| ''' | |