61 lines
2.4 KiB
Markdown
61 lines
2.4 KiB
Markdown
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---
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# Welcome to vLLM-Omni
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<source media="(prefers-color-scheme: dark)" src="./source/logos/vllm-omni-logo-text-dark.png">
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<img alt="vllm-omni" src="./source/logos/vllm-omni-logo-text-dark.png" width=55%>
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<h3 align="center">
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Easy, fast, and cheap omni-modality model serving for everyone
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</h3>
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<p style="text-align:center">
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<script async defer src="https://buttons.github.io/buttons.js"></script>
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<a class="github-button" href="https://github.com/vllm-project/vllm-omni" data-show-count="true" data-size="large" aria-label="Star">Star</a>
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<a class="github-button" href="https://github.com/vllm-project/vllm-omni/subscription" data-show-count="true" data-icon="octicon-eye" data-size="large" aria-label="Watch">Watch</a>
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<a class="github-button" href="https://github.com/vllm-project/vllm-omni/fork" data-show-count="true" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork">Fork</a>
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</p>
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## About
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[vLLM](https://github.com/vllm-project/vllm) was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni extends its support for omni-modality model inference and serving:
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- **Omni-modality**: Text, image, video, and audio data processing
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- **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
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- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
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<img alt="vllm-omni" src="./source/architecture/omni-modality model architecture.png" width=55%>
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</p>
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vLLM-omni is fast with:
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- Seamless AR support for efficient KV Cache management with vLLM
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- Pipelined stage execution overlapping
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- Fully disaggregation based on omniConnector and dynamic resource allocation across stages
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vLLM-omni is flexible and easy to use with:
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- Seamless integration with popular Hugging Face models
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- High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
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- Tensor, pipeline, data and expert parallelism support for distributed inference
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- Streaming outputs
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- OpenAI-compatible API server
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vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
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- Multi-modality generation models (e.g. Qwen-image)
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- Omni-modality models (e.g. Qwen-omni)
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For more information, checkout the following:
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