debug

Run commands inside a remote Docker container via the file-based command relay (tools/debugger). Use when the user says "run in Docker", "run on GPU", "debug remotely", "run test in container", "check nvidia-smi", "run pytest in Docker", or needs to execute any command inside a Docker container that shares the repo filesystem. Requires the user to have started server.sh inside the container first.

Zur Installation springen Dateien prüfen

Repository: NVIDIA/Model-Optimizer
Letzte Quellaktivität: 12. August 2026 um 16:33
Erkannte Sprache von SKILL.md: Englisch
Sterne: 5.434
Forks: 775

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Mit einem KI-Assistenten installieren

Diesen Prompt kopieren und in den verwendeten KI-Assistenten einfügen.

Follow https://skillsmp.com/skill-install/prompt.md to install only the Agent Skill at https://github.com/NVIDIA/Model-Optimizer/tree/main/plugins/modelopt/skills/debug.

Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.

npx skills add https://github.com/NVIDIA/Model-Optimizer --skill debug

Der Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.

Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

SKILL.md

Quellanweisungen · Schreibgeschützte Vorschau

name: debug
description: Run commands inside a remote Docker container via the file-based command relay (tools/debugger). Use when the user says "run in Docker", "run on GPU", "debug remotely", "run test in container", "check nvidia-smi", "run pytest in Docker", or needs to execute any command inside a Docker container that shares the repo filesystem. Requires the user to have started server.sh inside the container first.

# Remote Docker Debugger Execute commands inside a Docker container from the host using the file-based command relay. **Read `tools/debugger/CLAUDE.md` for full usage details** — it has the protocol and examples. ## Quick Reference ```bash # Check connection bash tools/debugger/client.sh status # Connect to server (user must start server.sh in Docker first) bash tools/debugger/client.sh handshake # Run a command bash tools/debugger/client.sh run "<command>" # Long-running command (default timeout is 600s) bash tools/debugger/client.sh --timeout 1800 run "<command>" # Cancel the currently running command bash tools/debugger/client.sh cancel # Reconnect after server restart bash tools/debugger/client.sh flush bash tools/debugger/client.sh handshake ```

Auf GitHub ansehen

Zurück zur Suche Zurück zur Installation

speculative-decoding

Train, debug, and validate a speculative decoding draft model (EAGLE3, DFlash, DSpark, Domino) through the ModelOpt launcher pipeline. Use when the user wants to add a new model to a draft-training pipeline, asks why a pipeline run failed, wants experiment logs reviewed, or wants to check whether a run's acceptance rate meets threshold. Triggers on "EAGLE3", "DFlash", "DSpark", "Domino", "draft model", "acceptance rate", "speculative decoding pipeline". Do NOT use for quantizing a model (use ptq) or serving a checkpoint (use deployment).

2026-10-23

qad

Run explicitly requested ModelOpt Quantization-Aware Distillation (QAD) through Megatron Bridge (recommended) or Hugging Face Transformers to recover a measured BF16-to-PTQ accuracy gap. Use only when the user explicitly asks for QAD, including its topology, data preparation, launch, resume, checkpoint export, or recovery decisions.

2026-10-23

evaluation

Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "run nel". Handles deployment, config generation, and evaluation execution. Always load when validating completed runs or reporting evaluation scores, including standalone launching-evals analysis. Not for quantizing models (use ptq), standalone serving (use deployment), or comparing completed baseline-vs-quantized results (use compare-results).

2026-10-22

Alle aus diesem Repository ansehen

debug

Quellinformationen

Installationsoptionen

Mit einem KI-Assistenten installieren

Quelldateien prüfen

Mehr aus diesem Repository

Mit einem KI-Assistenten installieren

Mehr aus diesem Repository