Parameter-efficient fine-tuning methods for pretrained language models阅读笔记 阿喵来看乎乎 南京理工大学 管理科学与工程博士4 人赞同了该文章 文章这种东西真是越读越多啊,哭哭哭,今天这个文章是2023年发表的一篇,关于Parameter Efficient Fine Tuning(简称PEFT)的
Parameter-efficient fine-tuningHypernetworksVisual recognitionModern techniques of pre-training and fine-tuning have significantly improved the performance of models on downstream tasks. However, this improvement faces challenges when pre-trained models encounter the necessity to adapt sequentially to multiple...
在一篇综述文章《Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning》中,将PEFT方法归为四类—— 1、Additive methods:最大且应用最广泛的一类方法。这类方法通过额外参数或者layer,扩大预训练模型的规模,仅仅训练新增的参数。 2、Selective methods:微调一个网络的部分参数。 3、Reparametrizati...
@Misc{peft, title = {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods}, author = {Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, Sayak Paul}, howpublished = {\url{https://github.com/huggingface/peft}}, year = {2022} }...
@Misc{peft, title = {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods}, author = {Sourab Mangrulkar and Sylvain Gugger and Lysandre Debut and Younes Belkada and Sayak Paul and Benjamin Bossan}, howpublished = {\url{https://github.com/huggingface/peft}}, year = {2022} ...
This “delta tuning” [1] approach can be seen as a refined version of retraining specific layers or appending a classifier to a pre-trained model, aiming for comparable performance as fine-tuning the entire model. Following [1]’s nomenclature, parameter-efficient fine-tuning (PEFT) methods ...
In this work, we newly bring parameter-efficient fine-tuning methods to proteomics. Using the parameter-efficient method LoRA, we train new models for two important proteomic tasks: predicting protein-protein interactions (PPI) and predicting the symmetry of homooligomers. We...
Parameter-efficient fine-tuning in large language models: a survey of methodologies Article Open access 03 May 2025 Reparameterization-Based Parameter-Efficient Fine-Tuning Methods for Large Language Models: A Systematic Survey Chapter © 2025 Structure-inducing pre-training Article Open access 01...
B.PEFT概述 (Overview on Parameter Efficient Fine Tuning) 微调仍然是提升LLM在未见用户数据集和任务上性能的关键。随着模型规模的增长(例如,从GPT-2的15亿到GPT-3的175亿),标准的完全微调范式需要成千上万个GPU并行工作,这是高度低效和不可持续的。一种算法被提出,即参数高效微调(PEFT),旨在调整最少的参数以...
The fine-tuning of Large Language Models (LLMs) is pivotal for achieving optimal performance across diverse downstream tasks. However, while full fine-tuning delivers superior results, it entails significant computational and resource costs. Parameter-Efficient Fine-Tuning (PEFT) methods, such as LoRA...