Having completed the fine-tuning process, the next logical step is to assess the model's performance and prepare it for inference. A model's value is determined by its effectiveness on a specific task, and this chapter focuses on the methods for measuring that effectiveness and preparing it for operational use.

We will begin by establishing a framework for evaluation. This includes implementing quantitative metrics such as ROUGE, BLEU, and perplexity, which can be derived from the cross-entropy loss $H(p,q)$ and is often expressed as $2^{H(p,q)}$. Alongside these automated scores, you will learn to conduct qualitative assessments, which use human judgment to check output for coherence and relevance. You will then see how to construct an automated pipeline to apply these techniques systematically.

The chapter concludes by bridging the gap between evaluation and deployment. This involves practical procedures such as merging trained PEFT adapters with the base model to create a standalone artifact and preparing the final model for efficient inference.