This research introduces a novel entropy-based pruning strategy that significantly reduces computational and storage demands of large language models.

• Identifies redundancy patterns in Transformer blocks by tracking entropy changes
• Strategically removes less important blocks based on entropy measurements
• Maintains model performance while improving efficiency and deployment feasibility
• Provides a systematic approach for LLM optimization applicable across model architectures

For engineering teams, this approach offers practical solutions to the resource constraints that currently limit LLM deployment in production environments.

Entropy-Based Block Pruning for Efficient Large Language Models