拉-洛拉:具有参数效率的微调,具有层层适应的低级适应
Jiancheng Gu1, Jiabin Yuan1, Jiyuan Cai2
1School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
概括
层层适应性低等级适应 (La-LoRA) 通过动态赋予模型层等级来提高参数效率的微调. 这种方法优化了对各种任务的适应性,优于标准的低级适应性 (LoRA).
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 参数高效微调 (PEFT) 可以有效地调整大型模型.
- 低级适应 (LoRA) 是一种流行的PEFT方法,结预训练的重量并使用低级矩阵.
- 由于不同层的重要性不同,LoRA在各层之间统一的等级分配是次优的.
研究的目的:
- 为了改进PEFT,引入层级适应性低级适应 (La-LoRA).
- 为了解决LoRA对统一等级分配的限制.
- 通过基于层贡献的动态分配等级来增强模型适应性.
主要方法:
- 提出了La-LoRA,一种新的PEFT方法.
- 实施的动态贡献驱动参数预算 (DCDPB) 用于等级分配.
- 采用缩减的标准加权动态等级分配 (TNW-DRA) 进行渐进的等级调整.
- 将每个层视为一个独立的单元来优化排名.
主要成果:
- 在各种任务和模型中,La-LoRA表现出一致的性能改善.
- 拟议的方法表现优于现有的PEFT基准.
- 动态等级分配在优化模型适应方面被证明是有效的.
结论:
- 与标准的LoRA相比,La-LoRA提供了更高的性能和计算效率.
- 层层的自适应方法有效地处理模型层的异质重要性.
- 拉洛拉 (La-LoRA) 提供了一种灵活有效的解决方案,用于调整大型预训练模型.
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