Low Rank Adapter

Low-rank adapters (LoRA) are a parameter-efficient fine-tuning technique for large language models (LLMs) and other deep learning models, aiming to adapt pre-trained models to new tasks with minimal additional parameters. Current research focuses on optimizing LoRA architectures (e.g., Monarch Rectangular Fine-tuning), exploring their effectiveness across diverse applications (e.g., image restoration, clinical NLP, knowledge graph embedding), and improving their efficiency through compression, dynamic routing, and adaptive rank allocation. This approach significantly reduces the computational cost and memory requirements of fine-tuning, making it crucial for deploying large models on resource-constrained devices and enabling more efficient exploration of model architectures and training strategies.