👉 Lo engineering, short for LoRA (Low-Rank Adaptation), is a technique used in machine learning to adapt pre-trained models for specific tasks with significantly reduced computational requirements. It involves modifying only a small fraction of the model's parameters, typically using low-rank matrix factorization, to fine-tune the model's behavior for a new task or dataset. This approach retains most of the pre-trained model's knowledge while allowing it to learn task-specific features efficiently, making it highly effective for deploying large models on resource-constrained devices without substantial retraining costs. By focusing on a few key parameters, LoRA achieves effective adaptation with minimal impact on overall model performance.
