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name: model-recipes description: "Select vLLM-Omni model families, endpoints, diffusion controls, quantization/offload/cache recipes, backend routes, and benchmark targets without reopening repository evidence." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

vLLM-Omni model recipes

Use this sub-skill when the task is to choose or adapt a vLLM-Omni model family, serving endpoint, hardware/backend route, diffusion optimization stack, or benchmark target. It is a router and decision aid; detailed payload code belongs to sibling usage sub-skills.

Fast route

1. Identify the desired output: text/chat, speech/TTS, image, video, audio/video, action, or robot policy.

2. Query the bundled static catalog when you need a quick shortlist:

   python scripts/query_model_catalog.py --task text-to-video --backend cuda
   python scripts/query_model_catalog.py --task tts --backend cuda --format json
   

3. Read references/model-selection-and-recipes.md for model-family taxonomy, endpoint/API choice, and route-level backend notes.

4. For diffusion image/video/audio or world/action models, read references/diffusion-quantization-and-offload.md before enabling batching, step execution, attention backend overrides, LoRA, HSDP, offload, cache, or quantization.

5. Read references/benchmarking.md before selecting TTFT, TPOT, TTFP, E2EL, RTF, or throughput targets.

6. Read references/troubleshooting.md when a model is gated, unavailable in cache, rejected by a backend, or fails after a quantization/offload/cache change.

Boundary with sibling sub-skills

• Need executable offline Python using Omni.generate, request dictionaries, or output accessors -> use offline-inference.
• Need curl, OpenAI SDK payloads, /v1/* request bodies, or server/client mechanics -> use online-serving.
• Need stage YAML, connectors, head/headless placement, or memory overlays -> use stage-configuration.
• Need to add or edit a model implementation, register a custom pipeline, or check TTS adapter internals -> use model-integration.

Safety and evidence limits

• This sub-skill is self-contained. Do not reopen the original checkout just to find model lists, recipes, or examples.
• Do not start downloads, gated-model access, native examples, or long benchmarks unless the user explicitly approves the model/cache/backend budget.
• Treat source examples that require GPUs, network downloads, or external model licenses as evidence only; reproduce their intent through the distilled guidance here.
• Prefer conservative BF16/eager or documented baseline routes when validating a new model-family/backend combination, then add one optimization at a time.