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生産性・共同作業 · SKILL.md

online-serving

online-serving

Operate vLLM-Omni HTTP serving, --omni CLI routing, OpenAI-compatible payloads, stage head/headless launches, realtime/streaming routes, and serving recovery without reopening repository evidence.

Independent GitHub snapshot · Automated classification · Original documentation governs

スキルの説明

GitHub

name: online-serving description: "Operate vLLM-Omni HTTP serving, --omni CLI routing, OpenAI-compatible payloads, stage head/headless launches, realtime/streaming routes, and serving recovery without reopening repository evidence." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

vLLM-Omni online serving

Use this sub-skill when the task involves starting a vLLM-Omni HTTP server, building OpenAI-compatible request payloads, choosing a streaming/realtime route, or recovering a failed serving launch.

Fast route

  1. Always include --omni when serving an Omni model. Without it, the command routes to ordinary upstream vLLM behavior and Omni-specific endpoints or flags may be missing.

  2. Choose a launch shape:

    • Single runtime: vllm serve MODEL --omni --port 8091 or vllm-omni serve MODEL --omni --port 8091.
    • Stage head plus headless worker processes: read CLI and OpenAI API.
  3. Choose an endpoint from CLI and OpenAI API: chat completions, image generation/edit, videos, text-to-speech, text-to-audio, or OpenAI-style model listing/health.

  4. For streaming and realtime use cases, read Realtime and streaming before writing a WebSocket or Server-Sent Events client.

  5. To generate a safe no-network payload scaffold, run the bundled helper from this sub-skill directory:

    python scripts/build_openai_payload.py --endpoint chat \
      --model Qwen/Qwen3-Omni-30B-A3B-Instruct \
      --prompt "Describe this scene" \
      --extra-json '{"modalities":["text"]}'
    
  6. If the server rejects the command, ignores request fields, cannot register a stage, or the client gets connection errors, read Troubleshooting.

Boundaries

  • Offline Omni / AsyncOmni Python generation, output objects, and local prompt dictionaries -> use offline-inference.
  • Deploy YAML schema, connector design, stage memory placement, and overlay validation -> use stage-configuration.
  • Model-family selection, hardware/backends, quantization/offload/cache recipes, and benchmark route selection -> use model-recipes.
  • Editing vLLM-Omni model implementations, custom pipelines, or TTS adapters -> use model-integration.

Safety notes

  • Do not run model-serving examples, download checkpoints, or open gated model licenses unless the user explicitly approves the GPU, network, cache, and time budget.
  • Treat repository examples that require a live server, browser, model weights, or service ports as evidence only; reproduce their payload intent with the bundled references and helper.
  • Do not reopen the original repository just to find routes, flags, or payload shapes. This sub-skill and its references are the runtime source of truth.
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