LoRA

LoRA, or Low-Rank Adaptation of Large Language Models, is a method used in the context of large language models to improve their efficiency and performance. In simple terms, it involves breaking down the large model into smaller, more manageable components that can be combined to approximate the original model's behavior. This is done by identifying and removing less important or redundant information from the model while retaining its core functionality. This helps in reducing the computational resources required for running the model, making it faster and more efficient. In the context of large language models, LoRA can lead to faster inference times and lower memory usage, making it easier to deploy and use these models in various applications.

This definition was generated by AI, using our BigNoodle model.