LoRA is a framework for parameter-efficient fine-tuning of large-scale neural networks. It functions by injecting trainable low-rank decomposition matrices into frozen model layers, allowing for task-specific adaptation while preserving the integrity of the original base model weights.
The project distinguishes itself by enabling the direct merging of these trained low-rank matrices into primary model weights. This process eliminates additional computational overhead during inference, ensuring that adapted models maintain the same performance characteristics as the original architecture. Furthermore, the framework supports modular adaptation, allowing users to swap between different task-specific configurations by loading and unloading lightweight matrices without modifying the underlying model.
The toolkit provides comprehensive support for optimizing the entire model lifecycle, including storage-efficient checkpointing and targeted updates to bias vectors. By training only a small fraction of the total parameters, the library reduces the disk space required for model storage and facilitates the deployment of adapted states across diverse hardware systems.
