Evaluating Your Fine-Tune
Explore methods for assessing fine-tuned AI models to verify that they meet production standards. Understand the importance of testing on held-out prompts, validating output structure, and performing paired comparisons with base models. Gain skills in diagnosing regressions and ensuring your model maintains safety and functional integrity before deployment.
Training a model or a parameter-efficient fine-tuning (PEFT) adapter, such as LoRA, is only part of the work. We also need to determine whether the resulting model is ready for production rather than judging it from a few successful prompts. An adapter may improve tone or style while breaking downstream output contracts by producing invalid JSON, omitting required keys, or adding commentary around structured data. This lesson explains how to evaluate fine-tuned models and detect regressions before deployment.
The core principles of fine-tune evaluation
Evaluating a fine-tuned model requires a disciplined approach distinct from standard model training:
Evaluate only on held-out prompts: We never test our model using prompts present in the training set. Memorization masks true capabilities; testing on held-out data reveals whether the model learned underlying patterns or simply memorized responses.
Prioritize the output contract: A beautifully written explanation that fails JSON parsing breaks the application pipeline. Structural integrity and contract compliance take precedence over narrative style.
Side-by-side paired comparison: Always run identical held-out prompts through both the base model and the base + adapter model using identical decoding parameters (
temperature,top_p,max_tokens).
