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

The article discusses a novel approach to pruning large language models (LLMs) by reformulating block selection as a constrained binary optimization problem, akin to an Ising glass. This method allows for efficient identification of which transformer blocks to remove, significantly improving model performance while reducing size and inference time.