Thank you for your interest in contributing to vLLM!
Our community is open to everyone and welcomes all kinds of contributions, no matter how small or large.
There are several ways you can contribute to the project:
Thank you for your interest in contributing to vLLM! Our community is open to everyone and welcomes all kinds of contributions, no matter how small or large. There are several ways you can contribute to the project:
- Identify and report any issues or bugs.
- Request or add a new model.
- Request or add support for a new model.
- Suggest or implement new features.
- Improve documentation or contribute a how-to guide.
However, remember that contributions aren't just about code.
We believe in the power of community support; thus, answering queries, assisting others, and enhancing the documentation are highly regarded and beneficial contributions.
We also believe in the power of community support; thus, answering queries, offering PR reviews, and assisting others are also highly regarded and beneficial contributions.
Finally, one of the most impactful ways to support us is by raising awareness about vLLM.
Talk about it in your blog posts, highlighting how it's driving your incredible projects.
Express your support on Twitter if vLLM aids you, or simply offer your appreciation by starring our repository.
Finally, one of the most impactful ways to support us is by raising awareness about vLLM. Talk about it in your blog posts and highlight how it's driving your incredible projects. Express your support on social media if you're using vLLM, or simply offer your appreciation by starring our repository!
## Setup for development
## Developing
### Build from source
Depending on the kind of development you'd like to do (e.g. Python, CUDA), you can choose to build vLLM with or without compilation. Check out the [building from source](https://docs.vllm.ai/en/latest/getting_started/installation.html#build-from-source) documentation for details.
```bash
pip install-e.# This may take several minutes.
```
### Testing
## Testing
```bash
pip install-r requirements-dev.txt
...
...
@@ -36,15 +29,16 @@ mypy
# Unit tests
pytest tests/
```
**Note:** Currently, the repository does not pass the mypy tests.
**Note:** Currently, the repository does not pass the ``mypy`` tests.
## Contribution Guidelines
## Contributing Guidelines
### Issues
### Issue Reporting
If you encounter a bug or have a feature request, please [search existing issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue) first to see if it has already been reported. If not, please [file a new issue](https://github.com/vllm-project/vllm/issues/new/choose), providing as much relevant information as possible.
If you encounter a bug or have a feature request, please check our issues page first to see if someone else has already reported it.
If not, please file a new issue, providing as much relevant information as possible.
> [!IMPORTANT]
> If you discover a security vulnerability, please follow the instructions [here](/SECURITY.md#reporting-a-vulnerability).
### Pull Requests & Code Reviews
...
...
@@ -53,4 +47,4 @@ Please check the PR checklist in the [PR template](.github/PULL_REQUEST_TEMPLATE
### Thank You
Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM.
Your contributions make vLLM a great tool for everyone!
All of your contributions help make vLLM a great tool and community for everyone!
RUN pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/cpu/intel_extension_for_pytorch-2.4.0%2Bgitfbaa4bc-cp310-cp310-linux_x86_64.whl
RUN pip install intel_extension_for_pytorch==2.4.0
FROM intel/oneapi-basekit:2024.1.0-devel-ubuntu20.04
FROM intel/oneapi-basekit:2024.2.1-0-devel-ubuntu22.04 AS vllm-base
RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
-[2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there!
-[2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team [here](https://docs.google.com/presentation/d/1B_KQxpHBTRa_mDF-tR6i8rWdOU5QoTZNcEg2MKZxEHM/edit?usp=sharing). Learn more from the [talks](https://raysummit.anyscale.com/flow/anyscale/raysummit2024/landing/page/sessioncatalog?tab.day=20241001&search.sessiontracks=1719251906298001uzJ2) from other vLLM contributors and users!
-[2024/09] We hosted [the sixth vLLM meetup](https://lu.ma/87q3nvnh) with NVIDIA! Please find the meetup slides [here](https://docs.google.com/presentation/d/1wrLGwytQfaOTd5wCGSPNhoaW3nq0E-9wqyP7ny93xRs/edit?usp=sharing).
-[2024/07] We hosted [the fifth vLLM meetup](https://lu.ma/lp0gyjqr) with AWS! Please find the meetup slides [here](https://docs.google.com/presentation/d/1RgUD8aCfcHocghoP3zmXzck9vX3RCI9yfUAB2Bbcl4Y/edit?usp=sharing).
-[2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog post [here](https://blog.vllm.ai/2024/07/23/llama31.html).
-[2024/06] We hosted [the fourth vLLM meetup](https://lu.ma/agivllm) with Cloudflare and BentoML! Please find the meetup slides [here](https://docs.google.com/presentation/d/1iJ8o7V2bQEi0BFEljLTwc5G1S10_Rhv3beed5oB0NJ4/edit?usp=sharing).
...
...
@@ -36,10 +37,12 @@ vLLM is fast with:
- Efficient management of attention key and value memory with **PagedAttention**
- Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), INT4, INT8, and FP8.
- Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.
- Speculative decoding
- Chunked prefill
**Performance benchmark**: We include a [performance benchmark](https://buildkite.com/vllm/performance-benchmark/builds/4068) that compares the performance of vllm against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [text-generation-inference](https://github.com/huggingface/text-generation-inference) and [lmdeploy](https://github.com/InternLM/lmdeploy)).
**Performance benchmark**: We include a performance benchmark at the end of [our blog post](https://blog.vllm.ai/2024/09/05/perf-update.html). It compares the performance of vLLM against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [SGLang](https://github.com/sgl-project/sglang) and [LMDeploy](https://github.com/InternLM/lmdeploy)). The implementation is under [nightly-benchmarks folder](.buildkite/nightly-benchmarks/) and you can [reproduce](https://github.com/vllm-project/vllm/issues/8176) this benchmark using our one-click runnable script.
vLLM is flexible and easy to use with:
...
...
@@ -48,20 +51,21 @@ vLLM is flexible and easy to use with:
- Tensor parallelism and pipeline parallelism support for distributed inference
- Streaming outputs
- OpenAI-compatible API server
- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs
-(Experimental) Prefix caching support
-(Experimental) Multi-lora support
- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron.
- Prefix caching support
- Multi-lora support
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
- Transformer-like LLMs (e.g., Llama)
- Mixture-of-Expert LLMs (e.g., Mixtral)
- Embedding Models (e.g. E5-Mistral)
- Multi-modal LLMs (e.g., LLaVA)
Find the full list of supported models [here](https://docs.vllm.ai/en/latest/models/supported_models.html).
## Getting Started
Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):
Install vLLM with `pip` or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):
```bash
pip install vllm
...
...
@@ -99,6 +103,7 @@ vLLM is a community project. Our compute resources for development and testing a
- Roblox
- RunPod
- Sequoia Capital
- Skywork AI
- Trainy
- UC Berkeley
- UC San Diego
...
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@@ -116,4 +121,12 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
```
\ No newline at end of file
```
## Contact Us
* For technical questions and feature requests, please use Github issues or discussions.
* For discussing with fellow users, please use Discord.
* For coordinating contributions and development, please use Slack.
* For security disclosures, please use Github's security advisory feature.
* For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.
