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Fix typo in README.md (#1033)

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Ikko Eltociear Ashimine 2023-09-14 04:55:23 +09:00 committed by GitHub
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@ -35,13 +35,13 @@ vLLM is fast with:
vLLM is flexible and easy to use with: vLLM is flexible and easy to use with:
- Seamless integration with popular HuggingFace models - Seamless integration with popular Hugging Face models
- High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more - High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
- Tensor parallelism support for distributed inference - Tensor parallelism support for distributed inference
- Streaming outputs - Streaming outputs
- OpenAI-compatible API server - OpenAI-compatible API server
vLLM seamlessly supports many Huggingface models, including the following architectures: vLLM seamlessly supports many Hugging Face models, including the following architectures:
- Aquila (`BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc.) - Aquila (`BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc.)
- Baichuan (`baichuan-inc/Baichuan-7B`, `baichuan-inc/Baichuan-13B-Chat`, etc.) - Baichuan (`baichuan-inc/Baichuan-7B`, `baichuan-inc/Baichuan-13B-Chat`, etc.)
@ -72,7 +72,7 @@ Visit our [documentation](https://vllm.readthedocs.io/en/latest/) to get started
## Performance ## Performance
vLLM outperforms HuggingFace Transformers (HF) by up to 24x and Text Generation Inference (TGI) by up to 3.5x, in terms of throughput. vLLM outperforms Hugging Face Transformers (HF) by up to 24x and Text Generation Inference (TGI) by up to 3.5x, in terms of throughput.
For details, check out our [blog post](https://vllm.ai). For details, check out our [blog post](https://vllm.ai).
<p align="center"> <p align="center">