Example Concept: LoRA works by inserting low-rank matrices into the existing layers of a pre-trained model. These matrices are trained to capture the task-specific adjustments needed, while the original model weights remain frozen. This approach significantly reduces the number of parameters that need to be updated during fine-tuning, leading to faster training times and lower computational costs, while maintaining or even improving model performance on the new task.