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tt-inference-server
tt-inference-server contém 4 skills coletadas de tenstorrent, com cobertura ocupacional por repositório e páginas de detalhe dentro do site.
Skills neste repositório
Checklist for onboarding a new LLM to the cpp_server inference backend so it serves through Dynamo — registering the model type, fetching tokenizer files, tokenizer static data (eos/stop/think tokens), and Dynamo discovery (reasoning + tool-call parsers, generation_config publishing). Use whenever a new model is being onboarded to cpp_server, a HuggingFace model is being wired into the Dynamo deploy, or a model "loads but generates wrong / isn't discoverable / isn't selectable".
Checklist for adding new code (models, workflows, runners, tests, CLI flags) to tt-inference-server-v2 and wiring it back into the v1 entry point run.py via workflows/v2_bridge.py. Use whenever code is being added or modified under tt-inference-server-v2/, when routing a model from v1 to v2, or when a v2 feature "works standalone but not through run.py".
Capture on-CPU and off-CPU flamegraphs of the running tt_media_server_cpp main (Drogon) and worker processes using Linux perf + Brendan Gregg's FlameGraph. Use when the user asks to profile the C++ server, find a performance bottleneck, identify slow code paths, capture a flamegraph, or investigate CPU usage / lock contention in BlazeRunner or the decode scheduler.
Trigger Tenstorrent Blaze media server and tt-metal upstream Docker image builds with gh. Use when the user asks to build Blaze and Metal Docker images, run the tt-shield media server image workflow, or build tt-metal images from the tt-llm-engine submodule reference.