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Large Language Models (LLMs) have transformed the landscape of artificialintelligence, while their enormous size presents significant challenges interms of computational costs. We introduce LoRAShear, a novel efficientapproach to structurally prune LLMs and recover knowledge. Given general LLMs,LoRAShear at first creates the dependency graphs over LoRA modules to discoverminimally removal structures and analyze the knowledge distribution. It thenproceeds progressive structured pruning on LoRA adaptors and enables inherentknowledge transfer to better preserve the information in the redundantstructures. To recover the lost knowledge during pruning, LoRAShearmeticulously studies and proposes a dynamic fine-tuning schemes with dynamicdata adaptors to effectively narrow down the performance gap to the fullmodels. Numerical results demonstrate that by only using one GPU within acouple of GPU days, LoRAShear effectively reduced footprint of LLMs by 20% withonly 1.0% performance degradation and significantly outperformsstate-of-the-arts. The source code will be available athttps://github.com/microsoft/lorashear.

LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery