Data Science & Analytics

Compare Results

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 evaluation), live NEL status/debugging (use launching-evals), or generic MLflow browsing without a comparison goal (use accessing-mlflow).

Publisher / source

NVIDIA/Model-Optimizer (found on GitHub top repositories)

Upstream record

NVIDIA/Model-Optimizer

Version

Version 1

Published

2026-11-05

Publication review

Approved for publication · recorded passed scan 2026-11-05

Content fingerprint

9779b89cc01b1ea54be9cdb92d44a3a72752b85784c9ecb42a1453282b0335fe