Low-Rank Adaptation (LoRA)
Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning technique that adapts large AI models to specific tasks by training small, low-rank matrices instead of updating the model's full set of parameters.
What is Low-Rank Adaptation (LoRA)?
LoRA keeps the original model parameters frozen and adds trainable low-rank matrices to selected layers, typically within the model's attention components. During fine-tuning, only these smaller matrices are updated. This significantly reduces the number of trainable parameters, memory requirements, and computational resources needed to customize a model.
Why is Low-Rank Adaptation (LoRA) Important?
LoRA makes it more practical to customize large models without the cost of full fine-tuning. Organizations can create task-specific adaptations using fewer resources while preserving the capabilities of the original model. Separate LoRA adapters can also be maintained for different tasks or use cases.
Common use cases
LoRA is commonly used for adapting large language models, image generation models, domain-specific AI applications, instruction tuning, and other scenarios where efficient model customization is needed.