Fine-Tuning Evaluation — Measure Your Custom Model's Performance

I will build a comprehensive evaluation framework for your fine-tuned model — covering held-out test set evaluation, task-specific metric calculation, LLM-as-judge quality scoring for open-ended outputs, regression testing confirming the fine-tuned model doesn't degrade on tasks the base model handled correctly, and a comparison report documenting fine-tuning impact across all evaluation dimensions. Fine-tuned model evaluation without regression testing produces models that improve on target tasks while degrading on general capabilities — a fine-tuned customer service model that answers customer questions better but can no longer follow basic instructions correctly is a net regression despite the task-specific improvement.

The framework covers held-out test set evaluation, task-specific metric implementation (ROUGE, BLEU, F1, or your domain metric), LLM-as-judge scoring for open-ended outputs, capability regression tests against the base model, and a fine-tuning impact report across all dimensions.