相关实验视频
大型预训练变压器模型的大小修剪,混合高斯先前的高斯先前的混合
Mingxuan Zhang1, Yan Sun2, Faming Liang1
1Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.
概括
混合高斯先修剪 (MGPP) 通过修剪不必要的重量,有效地减少了大型AI模型. 这种新的方法提高了自然语言处理任务的性能,即使在高稀疏性水平.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 大型预训练过的变压器模型在AI应用中实现了最先进的性能.
- 在这些模型中,实质性参数计数会给部署带来挑战.
- 现有的修剪方法,如大小修剪有局限性,特别是在NLP的转移学习.
研究的目的:
- 介绍了一种新的基于大小的修剪算法,混合高斯前修剪 (MGPP).
- 通过减少它们的尺寸来解决部署大型AI模型的挑战.
- 在修剪过程中保持模型的表现力.
主要方法:
- 开发了MGPP,一种基于大小的修剪算法,利用混合高斯先例进行规范化.
- 经过修剪的非表达性权重,以混合物为指导,高斯的先验.
- 在各种NLP任务中进行了广泛的评估.
主要成果:
- MGPP在现有修剪方法上表现出优越性,特别是在高稀疏性设置中.
- 评估包括自然语言理解,问答和自然语言生成任务.
- 算法有效地保留了模型的表达能力.
结论:
- MGPP为压缩大型变压器模型提供了有效的解决方案.
- 拟议的方法可以提高各种NLP任务的性能.
- 稀疏变压器的一致性的理论理由支持MGPP的有效性.
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