Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem

This post introduces a novel method for pruning Large Language Models (LLMs) by treating block removal as a constrained binary optimization problem, drawing parallels to the Ising model in physics. The core idea is to map the weights of redundant blocks to binary variables and solve an optimization problem to identify which blocks can be removed with minimal impact on model performance. This approach aims to achieve significant model compression by systematically removing entire blocks of parameters, rather than fine-grained pruning, leading to potential inference speedups and reduced memory footprint.