## About
[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:
- **Omni-modality**: Text, image, video, and audio data processing
- **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
vLLM-omni is fast with:
- Seamless AR support for efficient KV Cache management with vLLM
- Pipelined stage execution overlapping
- Fully disaggregation based on omniConnector and dynamic resource allocation across stages
vLLM-omni is flexible and easy to use with:
- Seamless integration with popular Hugging Face models
- High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
- Tensor, pipeline, data and expert parallelism support for distributed inference
- Streaming outputs
- OpenAI-compatible API server
vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
- Multi-modality generation models (e.g. Qwen-image)
- Omni-modality models (e.g. Qwen-omni)
For more information, checkout the following:
