claudecodex

tt-serve-model

Diagnose a model launch and verify a running server on Tenstorrent hardware, deterministically and evidence-based.

12 on GitHubby Tenstorrentv0.2.0Apache-2.0updated 8 days agoDeveloper Tools
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Plugin tt-serve-model from marketplace tenstorrent-skills in tenstorrent/skills. Commands read from the repo's manifest — 8 days ago.

What it installs

Skills (4)

  • discover

    Answer the 16 Discovery-stage host questions deterministically — host facts, driver, boards, hugepages, docker, HF cache, port 8000, device holders, --tt-device string, firmware minimums, idle telemetry, reset verdict, deployable models, gating, disk, clean-box. Fixed commands, fixed docs, fixed out

  • launch

    Diagnose an in-progress or completed model launch on Tenstorrent hardware — launch steps, mandatory docker flags, failure-signature classification, minimum-override selection and safety, override persistence, realistic timing, and displaced services. Deterministic, evidence-based. Use once a launch

  • retrieve

    Answer the Model Retrieval-stage questions deterministically for one model on this host — HF existence, gating, config; tt-inference-server spec entry (image, commits, status, ceilings, known_issues); image-vs-repo spec drift; GHCR tag, size, ancestry; run.py vs docker; and the tt-model-manager bund

  • verify

    Verify a running vLLM/TT-inference-server model is actually healthy, correct, and performant — health, served model/context, generation correctness (arithmetic, sequential state, code), mesh/hardware utilization, throughput and batching, reasoning-model quirks, and root-cause elimination when someth

View source on GitHub

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