VLLm with some additional features [GC, maxtoken] mod
Python
0
2,735 commits
updated Sep 26, 2024
| Documentation | Blog | Paper | Discord | Twitter/X |
vLLM, AMD, Anyscale Meet & Greet at Ray Summit 2024 (Monday, Sept 30th, 5-7pm PT) at Marriott Marquis San Francisco
We are excited to announce our special vLLM event in collaboration with AMD and Anyscale. Join us to learn more about recent advancements of vLLM on MI300X. Register here and be a part of the event!
Latest News 🔥
vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM is fast with:
Performance benchmark: We include a performance benchmark that compares the performance of vLLM against other LLM serving engines (TensorRT-LLM, text-generation-inference and lmdeploy).
vLLM is flexible and easy to use with:
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
Find the full list of supported models here.
Install vLLM with pip or from source:
pip install vllm
Visit our documentation to learn more.
We welcome and value any contributions and collaborations. Please check out CONTRIBUTING.md for how to get involved.
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
VLLm with some additional features [GC, maxtoken] mod
Python
0
2,735 commits
updated Sep 26, 2024
| Documentation | Blog | Paper | Discord | Twitter/X |
vLLM, AMD, Anyscale Meet & Greet at Ray Summit 2024 (Monday, Sept 30th, 5-7pm PT) at Marriott Marquis San Francisco
We are excited to announce our special vLLM event in collaboration with AMD and Anyscale. Join us to learn more about recent advancements of vLLM on MI300X. Register here and be a part of the event!
Latest News 🔥
vLLM is a fast and easy-to-use library for LLM inference and serving.
vLLM is fast with:
Performance benchmark: We include a performance benchmark that compares the performance of vLLM against other LLM serving engines (TensorRT-LLM, text-generation-inference and lmdeploy).
vLLM is flexible and easy to use with:
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
Find the full list of supported models here.
Install vLLM with pip or from source:
pip install vllm
Visit our documentation to learn more.
We welcome and value any contributions and collaborations. Please check out CONTRIBUTING.md for how to get involved.
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
We also have an official fundraising venue through OpenCollective. We plan to use the fund to support the development, maintenance, and adoption of vLLM.
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}