Data Science & Analytics
Establish baseline-vs-candidate evaluation plans, delegate missing evaluations, compare validated results, and decide quantization feasibility. Use when the user asks to compare baseline vs quantized runs, explain an accuracy drop/regression, verify whether a quantized checkpoint is acceptable, or compare NEL/MLflow evaluation outputs. Do NOT use for generic single-model evaluation without comparison intent (use modelopt-evaluation), live NEL status/debugging (use modelopt-launching-evals), or generic MLflow browsing without a comparison goal (use modelopt-accessing-mlflow).
Publisher / source
NVIDIA/Model-Optimizer (found on GitHub top repositories)
Upstream record
Version
Version 1
Published
2026-11-13
Publication review
Approved for publication
Content fingerprint
abc4971354c53984af2958c8aaca765d28531f83199e6ce5ce0a71a61b99e9d7
Review before you use it
Publication approval is a recorded review state, not a guarantee of safe execution. Check the upstream license, required tools and permissions, and test in your intended client.
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Source: https://prompeteer.ai/skills-hub/9b44cf8f-cc8d-42a7-ab55-59d93fa15dc3