OptiShear introduces an evolutionary framework for efficiently compressing large language models while preserving performance.

• Adapts pruning strategies to different LLM architectures using meta-pruning techniques
• Employs evolutionary optimization to find optimal pruning configurations
• Achieves superior compression-performance balance compared to fixed pruning methods
• Addresses the engineering challenge of deploying resource-intensive LLMs in constrained environments

This research enables more efficient deployment of powerful language models on devices with limited computational resources, making advanced AI more accessible and cost-effective.

OPTISHEAR: Towards Efficient and Adaptive Pruning of Large Language Models via Evolutionary Optimization