LoRA

Low-rank adaptation (LoRA) is a technique in machine learning that enables efficient fine-tuning of large pre-trained models by introducing low-rank updates to model parameters. It reduces computational and storage costs while maintaining performance, particularly in natural language processing and computer vision tasks.