Large Language Models (LLMs) offer impressive capabilities, but their significant size and computational demands often hinder deployment in resource-constrained environments. This chapter introduces knowledge distillation (KD) as a technique to address this challenge. The core idea is to transfer the knowledge acquired by a large, complex "teacher" model to a smaller, more efficient "student" model, aiming to retain performance while reducing size and inference cost.

You will learn the fundamental principles behind knowledge distillation, moving from the original concept of using soft targets (teacher's output probabilities) to more advanced methods involving intermediate feature matching and attention transfer. We will cover:

• Designing different distillation objectives, such as minimizing the Kullback-Leibler divergence between teacher and student output distributions, often represented as $L_{KD} = D_{KL}(p_{student}||p_{teacher})$.
• Strategies for distilling knowledge, including task-specific versus task-agnostic approaches and self-distillation.
• Addressing the particular difficulties associated with distilling generative LLMs.
• Methods for evaluating the fidelity and performance of the resulting student models.
• Implementing a practical distillation pipeline.

By the end of this chapter, you will understand how to design, implement, and evaluate knowledge distillation processes specifically tailored for compressing large language models.