Having examined methods to reduce the numerical precision of model parameters, we now turn our attention to pruning. Pruning techniques aim to reduce the size and potentially accelerate the inference of Large Language Models (LLMs) by removing components deemed less significant. The fundamental idea is to induce sparsity, eliminating connections or entire structural elements without critically impacting model performance.

This chapter covers various approaches to achieve this sparsity effectively. You will learn to:

• Distinguish between unstructured (weight-level) and structured (group-level, like channels or attention heads) pruning, understanding their respective impacts on hardware efficiency.
• Implement and analyze magnitude-based pruning methods and explore more dynamic techniques like movement pruning.
• Apply structured pruning strategies specifically designed for transformer architectures.
• Develop approaches for combining pruning with quantization to compound optimization benefits.
• Understand how compilers and hardware leverage sparsity patterns generated by pruning.
• Evaluate the consequences of pruning on model accuracy, generation quality, and overall capability.

By the end of this chapter, you will have a practical understanding of how to select, apply, and evaluate sophisticated pruning methodologies for optimizing LLMs.