LoRA (Low-Rank Adaptation)

Low-Rank Adaptation (LoRA) is a technique used in machine learning to reduce the number of parameters in large models while maintaining performance. It works by decomposing weight matrices into lower-rank components, allowing for efficient fine-tuning of pre-trained models with fewer resources. This method is particularly beneficial in scenarios where computational power and memory are limited, enabling faster training and deployment of models. Common use cases include adapting large language models for specific tasks, such as sentiment analysis or translation, without the need for extensive retraining of the entire model.