Having covered the implementation details of LoRA, QLoRA, and other Parameter-Efficient Fine-Tuning (PEFT) techniques, the next step is to assess their effectiveness and understand their trade-offs. This chapter concentrates on the methods for quantitatively and qualitatively evaluating these fine-tuning approaches.

We will examine standard performance metrics suited for PEFT, conduct benchmark comparisons against full fine-tuning, analyze model stability and generalization capabilities, investigate potential catastrophic forgetting, provide a detailed analysis of computational costs, and discuss the current limitations and open research questions associated with these techniques. Understanding these evaluation dimensions is necessary for making informed decisions about selecting and deploying PEFT strategies in practical applications.