From 30cba84222e24653b444d70ed440aec00b964e41 Mon Sep 17 00:00:00 2001 From: Hongsheng Liu Date: Tue, 4 Aug 2026 14:18:51 +0800 Subject: [PATCH] [Doc] Update README and installation docs for v0.26.0 (#5715) Signed-off-by: hsliuustc0106 --- README.md | 13 +++++-------- docs/README.md | 5 +++-- docs/configuration/README.md | 2 +- docs/getting_started/installation/README.md | 3 +++ docs/getting_started/installation/gpu/cuda.inc.md | 4 ++-- docs/getting_started/installation/npu/npu.inc.md | 12 ++++++++---- 6 files changed, 22 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index e7fc6912e..9d63a4b1e 100644 --- a/README.md +++ b/README.md @@ -16,14 +16,10 @@ Easy, fast, and cheap omni-modality model serving for everyone --- *Latest News* 🔥 +- [2026/08] We released [0.26.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.26.0) - aligned with the vLLM 0.26 release line, featuring [MiniMax H3](recipes/MiniMaxAI/MiniMax-H3.md) joint video/audio generation, an experimental full-duplex realtime runtime for [MiniCPM-o 4.5](recipes/OpenBMB/MiniCPM-o-4_5.md), distributed layerwise diffusion offload, and broader model, hardware, streaming, TTS, and quantization support. - [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support. -- [2026/06] We released [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) - an **omnimodal world-model** release aligned with vLLM 0.22, featuring [Nvidia Cosmos3](recipes/cosmos3/Cosmos3-Nano.md)/DreamZero world model support, expanded quantization coverage across Blackwell/NPU/XPU, TTS production improvements, new models including MiniCPM-o 4.5, MOSS-TTS, and Lance, plus RL integration with [VeRL-Omni](https://github.com/verl-project/verl-omni). -- [2026/05] We released [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0) - refreshes the serving/runtime stack for large-scale omni workloads, and improves diffusion model performance, quantization, and hardware readiness across CUDA, ROCm, MUSA, NPU, and XPU backends. -- [2026/03] We released [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0) - strengthens the core runtime through a large entrypoint refactor and scheduler/runtime cleanups, expands unified quantization and diffusion execution, broadens multimodal model coverage, and improves production readiness across audio, omni, image, video, RL, and multi-platform deployments. +- [2026/06] Starting with [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0), vLLM-Omni publishes a stable release aligned with every even-numbered upstream vLLM minor version. [0.16.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.16.0), [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0), [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0), and [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) continued this cadence, expanding omni and world-model support with [NVIDIA Cosmos3](recipes/cosmos3/Cosmos3-Nano.md) and DreamZero, adding models such as MiniCPM-o 4.5, MOSS-TTS, and Lance, and advancing TTS, diffusion, distributed execution, quantization, RL integration through [VeRL-Omni](https://github.com/verl-project/verl-omni), and CUDA/ROCm/MUSA/NPU/XPU coverage. - [2026/03] Check out our first public [project deepdive](https://youtu.be/sgwNfsNnR9I) at the vLLM Hong Kong Meetup! -- [2026/03] **[vllm-omni-skills](https://github.com/hsliuustc0106/vllm-omni-skills)** is a community-driven collection of AI assistant skills that help developers work with vLLM-Omni more effectively. These skills can be used with popular agentic AI coding assistants like **Cursor IDE**, **Claude**, **Codex**, and more. -- [2026/02] We released [0.16.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.16.0) - A major alignment + capability release that rebases onto **upstream vLLM v0.16.0** and significantly expands performance, distributed execution, and production readiness across **Qwen3-Omni / Qwen3-TTS**, **Bagel**, **MiMo-Audio**, **GLM-Image** and the **Diffusion (DiT) image/video stack**—while also improving platform coverage (CUDA / ROCm / NPU / XPU), CI quality, and documentation. -- [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. - [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. --- @@ -55,12 +51,13 @@ vLLM-Omni is flexible and easy to use with: - Tensor, pipeline, data and expert parallelism support for distributed inference - Streaming outputs - OpenAI-compatible API server +- Full-duplex realtime serving with streaming audio input and output (experimental) vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including: -- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL) +- **Omni-modality models** (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL) - **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3) -- **Diffusion models** — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX) +- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX) - **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy) ## Getting Started diff --git a/docs/README.md b/docs/README.md index f6263483b..f3dfe70c2 100644 --- a/docs/README.md +++ b/docs/README.md @@ -52,12 +52,13 @@ vLLM-Omni is flexible and easy to use with: - Tensor, pipeline, data and expert parallelism support for distributed inference - Streaming outputs - OpenAI-compatible API server +- Full-duplex realtime serving with streaming audio input and output (experimental) vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including: -- Omni-modality models (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL) +- Omni-modality models (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL) - TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3) -- Diffusion models — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX) +- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX) - Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy) For more information, checkout the following: diff --git a/docs/configuration/README.md b/docs/configuration/README.md index 390176e9c..09772ccd1 100644 --- a/docs/configuration/README.md +++ b/docs/configuration/README.md @@ -2,7 +2,7 @@ This section lists the most common options for running vLLM-Omni. -For options within a vLLM Engine. Please refer to [vLLM Configuration](https://docs.vllm.ai/en/v0.16.0/configuration/index.html) +For options within a vLLM Engine, please refer to the [vLLM 0.26 configuration guide](https://docs.vllm.ai/en/v0.26.0/configuration/index.html). Currently, the main options are maintained by stage configs for each model. diff --git a/docs/getting_started/installation/README.md b/docs/getting_started/installation/README.md index 89562c53c..b49cee794 100644 --- a/docs/getting_started/installation/README.md +++ b/docs/getting_started/installation/README.md @@ -1,5 +1,8 @@ # Installation +!!! important + vLLM-Omni is released against the matching upstream vLLM major/minor version. vLLM-Omni 0.26.x requires vLLM 0.26.x; the stable v0.26.0 instructions pin vLLM 0.26.0. + vLLM-Omni supports the following hardware platforms: - [GPU](gpu.md) diff --git a/docs/getting_started/installation/gpu/cuda.inc.md b/docs/getting_started/installation/gpu/cuda.inc.md index 9d568bde3..a68bfdb58 100644 --- a/docs/getting_started/installation/gpu/cuda.inc.md +++ b/docs/getting_started/installation/gpu/cuda.inc.md @@ -5,7 +5,7 @@ # --8<-- [end:requirements] # --8<-- [start:set-up-using-python] -vLLM-Omni depends vLLM. So please follow instructions below mainly for vLLM. +vLLM-Omni depends on the matching major/minor release of vLLM. The vLLM-Omni 0.26.x release line uses vLLM 0.26.x. !!! note PyTorch installed via `conda` will statically link `NCCL` library, which can cause issues when vLLM tries to use `NCCL`. See for more details. @@ -124,7 +124,7 @@ If you want to specify the base vLLM version: ```bash DOCKER_BUILDKIT=1 docker build \ -f docker/Dockerfile.cuda \ - --build-arg BASE_IMAGE=vllm/vllm-openai:v0.22.1 \ + --build-arg BASE_IMAGE=vllm/vllm-openai:v0.26.0 \ -t vllm-omni-cuda . ``` diff --git a/docs/getting_started/installation/npu/npu.inc.md b/docs/getting_started/installation/npu/npu.inc.md index b24487f3f..a0d4c86b0 100644 --- a/docs/getting_started/installation/npu/npu.inc.md +++ b/docs/getting_started/installation/npu/npu.inc.md @@ -53,16 +53,20 @@ We are keeping [issue #886](https://github.com/vllm-project/vllm-omni/issues/886 You can also build vLLM-Omni from the latest main branch if you want to use the latest features or bug fixes. (But sometimes it will break for a while. You can check [issue #886](https://github.com/vllm-project/vllm-omni/issues/886) for the status of the latest commit of vLLM-Omni main branch on NPU.) ```bash -# Pin vLLM version to 0.18.0 -git clone -b v0.18.0 https://github.com/vllm-project/vllm.git +# Pin vLLM and vLLM-Ascend to the v0.26 release line +git clone -b v0.26.0 https://github.com/vllm-project/vllm.git +cd vllm VLLM_TARGET_DEVICE=empty pip install -v -e . +cd .. -git clone -b v0.18.0rc1 https://github.com/vllm-project/vllm-ascend.git +git clone -b releases/v0.26.0rc https://github.com/vllm-project/vllm-ascend.git +cd vllm-ascend pip install -v -e . +cd .. # Install vLLM-Omni from the latest main branch git clone https://github.com/vllm-project/vllm-omni.git -cd /vllm-workspace/vllm-omni +cd vllm-omni pip install -v -e . --no-build-isolation # or VLLM_OMNI_TARGET_DEVICE=npu pip install -v -e . export VLLM_WORKER_MULTIPROC_METHOD=spawn