DarwinLM introduces an evolutionary approach to structured pruning that dramatically reduces computational costs while maintaining performance.

• Employs non-uniform compression strategies that respect different model components' sensitivity to pruning
• Achieves up to 2x speedup in inference without specialized hardware requirements
• Uses innovative structured pruning techniques that work across various deployment environments
• Enables real-time applications of large language models in resource-constrained settings

This research addresses a critical engineering challenge: making powerful LLMs accessible for practical applications without requiring expensive computing infrastructure.

DarwinLM: Evolutionary Structured Pruning of Large Language Models