HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning

arXiv:2404.19245v1 Announce Type: new
Abstract: Adapting Large Language Models (LLMs) to new tasks through fine-tuning has been made more efficient by the introduction of Parameter-Efficient Fine-Tuning (PEFT) techniques, such as LoRA. However, these methods often underperform compared to full fine-tuning, particularly in scenarios involving complex datasets. This issue becomes even more pronounced in complex domains, highlighting the need for improved PEFT approaches that can achieve better performance. Through a series of experiments, we have uncovered two critical insights that shed …

architecture arxiv cs.ai cs.cl fine-tuning lora type