LoRA가 YOLO 같은 대규모 AI 모델을 효율적으로 미세 조정하여 비용을 절감하고 최소한의 리소스로 엣지 배포를 가능하게 하는 방법을 알아보세요.
LoRA (Low-Rank Adaptation) is an efficient technique used to adapt large pre-trained machine learning (ML) models, such as those used for natural language processing (NLP) or computer vision (CV), to specific tasks or datasets without retraining the entire model. It significantly reduces the computational cost and memory requirements associated with fine-tuning massive models, making advanced AI more accessible. LoRA falls under the umbrella of Parameter-Efficient Fine-Tuning (PEFT) methods, which focus on adapting models with minimal changes to their parameters.
Traditional fine-tuning involves updating all the parameters (or model weights) of a pre-trained model using new data. For models with billions of parameters, like many modern LLMs or large vision models, this process demands substantial computational resources, particularly GPU memory and time. LoRA operates on the principle, supported by research, that the changes needed to adapt a model often reside in a lower-dimensional space, meaning they don't require altering every single weight.
Instead of modifying all the original weights, LoRA freezes them and injects smaller, trainable "low-rank" matrices into specific layers of the model architecture, often within Transformer blocks (a common component in many large models, explained further in the Attention Is All You Need paper). Only these newly added matrices (often called adapters) are updated during the fine-tuning process. This drastically reduces the number of trainable parameters, often by orders of magnitude (e.g., millions instead of billions), while still achieving performance comparable to full fine-tuning in many cases. The original LoRA research paper provides further technical details on the methodology and its effectiveness. This approach makes the fine-tuning process significantly faster and less memory-intensive.
The primary advantage of LoRA is its efficiency, leading to several key benefits:
LoRA의 효율성은 다양한 영역에서 가치를 발휘합니다: