From 3fb3c8aba4aa3d65bd4d263ace604eddaf7ad2f8 Mon Sep 17 00:00:00 2001 From: Hongsheng Liu Date: Tue, 3 Feb 2026 13:19:36 +0800 Subject: [PATCH] Docs update paper link (#1169) Signed-off-by: hsliu Signed-off-by: hsliu_ustc Co-authored-by: hsliu_ustc --- README.md | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index e55025126..a61a45032 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,8 @@ Easy, fast, and cheap omni-modality model serving for everyone --- *Latest News* 🔥 -- [2026/02] We released [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0) - This is the first **stable release** of vLLM-Omni that expands Omni’s diffusion / image-video generation and audio / TTS stack, improves distributed execution and memory efficiency, and broadens platform/backend coverage (GPU/ROCm/NPU/XPU). It also brings meaningful upgrades to serving APIs, profiling & benchmarking, and overall stability. + +- [2026/02] We released [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0) - This is the first **stable release** of vLLM-Omni that expands Omni’s diffusion / image-video generation and audio / TTS stack, improves distributed execution and memory efficiency, and broadens platform/backend coverage (GPU/ROCm/NPU/XPU). It also brings meaningful upgrades to serving APIs, profiling & benchmarking, and overall stability. Please check our latest [paper](https://arxiv.org/abs/2602.02204) for architecture design and performance results. - [2026/01] We released [0.12.0rc1](https://github.com/vllm-project/vllm-omni/releases/tag/v0.12.0rc1) - a major RC milestone focused on maturing the diffusion stack, strengthening OpenAI-compatible serving, expanding omni-model coverage, and improving stability across platforms (GPU/NPU/ROCm), please check our latest [design](https://docs.google.com/presentation/d/1qv4qMW1rKAqDREMXiUDLIgqqHQe7TDPj/edit?usp=sharing&ouid=110473603432222024453&rtpof=true&sd=true). - [2025/11] vLLM community officially released [vllm-project/vllm-omni](https://github.com/vllm-project/vllm-omni) in order to support omni-modality models serving. @@ -67,6 +68,19 @@ Visit our [documentation](https://vllm-omni.readthedocs.io/en/latest/) to learn We welcome and value any contributions and collaborations. Please check out [Contributing to vLLM-Omni](https://vllm-omni.readthedocs.io/en/latest/contributing/) for how to get involved. +## Citation + +If you use vLLM-Omni for your research, please cite our [paper](https://arxiv.org/abs/2602.02204): + +```bibtex +@article{yin2026vllmomni, + title={vLLM-Omni: Fully Disaggregated Serving for Any-to-Any Multimodal Models}, + author={Peiqi Yin, Jiangyun Zhu, Han Gao, Chenguang Zheng, Yongxiang Huang, Taichang Zhou, Ruirui Yang, Weizhi Liu, Weiqing Chen, Canlin Guo, Didan Deng, Zifeng Mo, Cong Wang, James Cheng, Roger Wang, Hongsheng Liu}, + journal={arXiv preprint arXiv:2602.02204}, + year={2026} +} +``` + ## Join the Community Feel free to ask questions, provide feedbacks and discuss with fellow users of vLLM-Omni in `#sig-omni` slack channel at [slack.vllm.ai](https://slack.vllm.ai) or vLLM user forum at [discuss.vllm.ai](https://discuss.vllm.ai).