DarwinLM: Evolutionary Structured Pruning of Large Language Models

Understand

Large Language Models (LLMs) have achieved significant success across various NLP tasks.

• However, their massive computational costs limit their widespread use, particularly in real-time applications.
• Structured pruning offers an effective solution by compressing models and directly providing end-to-end speed improvements, regardless of the hardware environment.
• Meanwhile, different components of the model exhibit varying sensitivities towards pruning, calling for non-uniform model compression.