Efficient Fine-Tuning with LoRA | Generated by AI
Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning technique for large language models. Instead of updating all parameters of a pre-trained model, LoRA introduces small, low-rank matrices to adapt specific layers, typically attention or feedforward layers. These matrices capture task-specific updates while keeping the original model parameters frozen, significantly reducing memory and computational costs. For example, fine-tuning a large model like GPT might require updating billions of parameters, but LoRA can achieve comparable performance by adjusting only a tiny fraction of parameters, often less than 1% of the total. This makes LoRA ideal for resource-constrained environments, enabling efficient customization of large models for specific tasks without extensive retraining.
LoRA: Low-Rank Adaptation of Large Language Models