diff --git a/README.md b/README.md index 25f0c148f..9d63a4b1e 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,6 @@ Easy, fast, and cheap omni-modality model serving for everyone - [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] 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. - [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. --- @@ -52,7 +51,7 @@ 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 -- **Experimental:** Full-duplex realtime serving with streaming audio input and output +- Full-duplex realtime serving with streaming audio input and output (experimental) vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including: diff --git a/docs/README.md b/docs/README.md index fc787b635..f3dfe70c2 100644 --- a/docs/README.md +++ b/docs/README.md @@ -52,7 +52,7 @@ 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 -- **Experimental:** Full-duplex realtime serving with streaming audio input and output +- Full-duplex realtime serving with streaming audio input and output (experimental) vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including: diff --git a/docs/getting_started/installation/gpu/musa.inc.md b/docs/getting_started/installation/gpu/musa.inc.md index ae9ba25a1..a7cbc848f 100644 --- a/docs/getting_started/installation/gpu/musa.inc.md +++ b/docs/getting_started/installation/gpu/musa.inc.md @@ -28,7 +28,7 @@ vLLM-Omni for MUSA requires building from source. Pre-built wheels are not curre ```bash git clone https://github.com/MooreThreads/vllm-musa.git cd vllm-musa -git checkout v0.26.0-dev +git checkout v0.18.0-dev pip install . --no-build-isolation -v ``` diff --git a/docs/getting_started/quickstart.md b/docs/getting_started/quickstart.md index 9480245c4..15ceb395e 100644 --- a/docs/getting_started/quickstart.md +++ b/docs/getting_started/quickstart.md @@ -32,9 +32,9 @@ uv pip install -e . For additional installation methods — please see the [installation guide](installation/README.md). !!! note - vLLM-Omni is released against the matching upstream vLLM major/minor version. vLLM-Omni 0.26.x requires vLLM 0.26.x; the commands above pin vLLM 0.26.0 for the vLLM-Omni 0.26 release line. If the versions are misaligned, you will see a warning when you import vLLM-Omni. + It is important to install the same major & minor version of vLLM and vLLM Omni, otherwise things may not work as expected. If the versions are misaligned, you will see a warning when you import vLLM Omni. - If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.26.0` and vLLM-Omni `0.26.0`, as vLLM-Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue. + If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.26.0` and vLLM Omni `0.26.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue. ## Offline Inference