What is LORA and Q-LORA Finetuning?

Low-Rank Adaptation (LoRA) and its variant Quantized Low-Rank Adaptation (Q-LoRA) are efficient fine-tuning techniques for large language models (LLMs). They allow for the adaptation of pre-trained models to new tasks or domains without the need to retrain the entire model, reducing computational resources and time. LoRA introduces low-rank matrices that interact with the original weights to adapt the model to new tasks, while Q-LoRA incorporates quantization into the fine-tuning process, further reducing memory footprint and computational requirements. These techniques have various use cases in natural language processing, computer vision, edge computing, multilingual adaptation, and personalized AI services. They democratize AI by making powerful tools accessible to a broader range of users and contribute to more sustainable AI practices through reduced energy consumption.