version: "1.0.0" name: vllm-omni description: "Explain, tune and extend vLLM-Omni multi-stage AR, diffusion and media-output inference with source-grounded search spaces and validation." license: Apache-2.0 metadata: disco-role: operating

vLLM-Omni engine knowledge

Use this skill to explain, configure, tune or extend vLLM-Omni stage-based autoregressive, generation and diffusion execution. Multimodal input alone does not select this skill; its distinguishing boundary is heterogeneous output and model-owned execution pipelines.

This is a source-grounded portable candidate at 52194f24fa42e14d1c5e096f4bea4dc7f95cffa1. It is not a GPU certification or a complete model catalogue. Read provenance when the checkout or installed packages differ.

Route by decision

Read only the relevant branch. Source links in evidence resolve to the pinned revision; instructions and decision criteria are bundled here. Native test commands require a matching engine checkout and environment.

Shared procedure

1. Identify exact model revision, task/output modality, reference implementation, GPU topology, software revisions, input/output shapes, concurrency and latency/quality contract. State unknowns; do not invent an optimal configuration.
2. Trace PipelineConfig → deploy resolution → orchestrator → stage pool/client → scheduler/runner → connector/output processor. Keep topology ownership separate from placement and resource knobs.
3. Label each proposal existing setting, implementation change, or research extension. A dataclass field is not proof that every model/backend supports it.
4. Establish a real-model baseline with the smallest supported concurrency. Preserve model, sampling, generated duration/resolution, seeds and quality target while tuning execution.
5. Compare correctness before latency, then report queue time, stage time, transfer time, peak memory and modality-specific end-to-end metrics. Preserve rejected trials and fallback paths.

Constraints that change decisions

• PipelineConfig and StagePipelineConfig own fixed model semantics and stage topology. Changing those requires a registered model variant and new validation; deploy YAML does not define arbitrary new model stages.
• A replica is not a model stage or a tensor-parallel rank. Stateful chunks must retain request/replica affinity until completion, cancellation or explicit failure handling.
• Async chunk transfer, AR continuous batching, diffusion request batching, diffusion step scheduling and streamed final media are different capabilities.
• Do not apply a Thinker quantization recipe to Talker/codec/VAE components without a compatible implementation and quality evidence.
• Successful YAML parsing, imports, CPU probes or mock tests do not establish GPU execution, media quality or throughput.

Return a decision with source evidence, feasible alternatives, assumptions, validation gates and recovery steps. No speedup is established by this skill alone.