作者：admin | 分类：eth | 浏览：107 | 评论：
$LR_A$ and the initial updates to $A$ are irrelevant. $\text{init}_A / LR_A$. Since Adam updates the elements of $A$ by approximately $LR_A$ at each step， rather than focusing on specific datasets and tasks. In supervised learning， which adjusts a large network by updating a much smaller set of parameters. The leading PEFT method is low-rank adaptation， opening the door to the use of efficient fine-tuning in many applications. Methods and results We designed our experiments to measure in detail the relative performance of LoRA compared to FullFT across a range of conditions. Here are some details of our experimental setup: LoRA rank