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12
README.md
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@ -10,7 +10,7 @@ Easy, fast, and cheap LLM serving for everyone
</h3> </h3>
<p align="center"> <p align="center">
| <a href="https://vllm.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href=""><b>Blog</b></a> | | <a href="https://vllm.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://github.com/vllm-project/vllm/discussions"><b>Discussions</b></a> |
</p> </p>
@ -18,7 +18,7 @@ Easy, fast, and cheap LLM serving for everyone
*Latest News* 🔥 *Latest News* 🔥
- [2023/06] We officially released vLLM! vLLM has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid April. Check out our [blog post](). - [2023/06] We officially released vLLM! vLLM has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid April. Check out our [blog post](https://vllm.ai).
--- ---
@ -62,7 +62,7 @@ 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 HuggingFace 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](). For details, check out our [blog post](https://vllm.ai).
<p align="center"> <p align="center">
<picture> <picture>
@ -70,7 +70,7 @@ For details, check out our [blog post]().
<img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a10g_n1_light.png" width="45%"> <img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a10g_n1_light.png" width="45%">
</picture> </picture>
<picture> <picture>
<source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/figures/perf_a100_n1_dark.png"> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a100_n1_dark.png">
<img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a100_n1_light.png" width="45%"> <img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a100_n1_light.png" width="45%">
</picture> </picture>
<br> <br>
@ -79,11 +79,11 @@ For details, check out our [blog post]().
<p align="center"> <p align="center">
<picture> <picture>
<source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/figures/perf_a10g_n3_dark.png"> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a10g_n3_dark.png">
<img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a10g_n3_light.png" width="45%"> <img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a10g_n3_light.png" width="45%">
</picture> </picture>
<picture> <picture>
<source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/figures/perf_a100_n3_dark.png"> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a100_n3_dark.png">
<img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a100_n3_light.png" width="45%"> <img src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/figures/perf_a100_n3_light.png" width="45%">
</picture> <br> </picture> <br>
<em> Serving throughput when each request asks for 3 output completions. </em> <em> Serving throughput when each request asks for 3 output completions. </em>

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docs/source/index.rst
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@ -40,7 +40,7 @@ vLLM is flexible and easy to use with:
* Streaming outputs * Streaming outputs
* OpenAI-compatible API server * OpenAI-compatible API server
For more information, please refer to our `blog post <>`_. For more information, please refer to our `blog post <https://vllm.ai>`_.
Documentation Documentation

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docs/source/models/adding_model.rst
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@ -18,7 +18,7 @@ This document provides a high-level guide on integrating a `HuggingFace Transfor
0. Fork the vLLM repository 0. Fork the vLLM repository
-------------------------------- --------------------------------
Start by forking our `GitHub <https://github.com/vllm-project/vllm/issues>`_ repository and then :ref:`build it from source <build_from_source>`. Start by forking our `GitHub <https://github.com/vllm-project/vllm/>`_ repository and then :ref:`build it from source <build_from_source>`.
This gives you the ability to modify the codebase and test your model. This gives you the ability to modify the codebase and test your model.