In the evolving landscape of machine learning, the quest for more efficient training methods is constant. One such innovation that has gained attention is Low-Rank Adaptation (LoRA). This technique introduces a clever way to optimize the training process by decomposing the model's weight matrices into smaller, more manageable components. In this post, we'll delve into the workings of LoRA, its benefits, and its potential applications.