LoRA Fine Tuning

Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning technique for large language models (LLMs) and other deep learning models, aiming to improve performance on specific tasks with minimal computational cost and memory overhead. Current research focuses on improving LoRA's accuracy, efficiency, and applicability across diverse tasks, including federated learning and continual learning, often exploring variations like rsLoRA, VeRA, and HydraLoRA, as well as methods for compressing and combining multiple LoRA adapters. This approach holds significant importance for making LLMs more accessible and adaptable, enabling researchers and practitioners to customize models for various applications without the resource constraints of full fine-tuning.