Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Surveyarxiv.org/abs/2403.14608 摘要: 大型模型代表了多个应用领域的突破性进步,在各种任务中取得了显著的成就。然而,它们前所未有的规模带来了巨大的计算成本。这些模型通常由数十亿个参数组成,需要大量的计算资源来执行。特别是,在为特定的下游...
来自Northeastern University 及University of California, Riverside等大学的研究者发表了“Parameter-Efficient Fine-Tuning for Large models: A Comprehensive Survey”对PEFT技术进行全面综述,探讨各种PEFT算法及其应用,为研究人员提供深入的理解。 论文地址:https://arxiv.org/abs/2403.14608 以下为论文主要内容: 一、...
Parameter Efficient Fine-Tuning (PEFT) provides a practical solution by efficiently adjusting the large models over the various downstream tasks. In particular, PEFT refers to the process of adjusting the parameters of a pre-trained large model to adapt it to a specific task or domain while minim...
GLoRA:One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning O、Abstract 本文在 LoRA 的基础上,提出一种广义 LoRA (GLoRA,Generalized LoRA)。与 LoRA 相比,GLoRA 的理论更加通用,它采用一个通用的模块,来优化预训练的模型权重。 一、Motivation nameformulatheoryweakness VPT [x1,Z1,E1]=L1(...
UnderReviewUNLEASHINGTHEPOWEROFTASK-SPECIFICDIREC-TIONSINPARAMETEREFFICIENTFINE-TUNINGChongjieSi†∗ZhiyiShi⋄∗ShifanZhang†XiaokangYang†HanspeterPfister⋄WeiShen†✉†ShanghaiJiaoTongUniversity⋄HarvardUniversitychongjiesi@sjtu.e
github:GitHub - huggingface/peft: 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. 概念:其核心理念是通过仅调整模型的一小部分参数,而保持大部分预训练参数不变,从而大幅减少计算资源和存储需求 LORA(Low-Rank Adaptation低秩适应) github:GitHub - microsoft/LoRA: Code for loralib, an implement...
“How can we allocate the parameter budget adaptively according to [the] importance of modules to improve the performance of parameter-efficient fine-tuning?” What this translates to is “How can we give preference to the parameters that lead to better performance rather than treating...
Parameter-efficient fine-tuning (PEFT) is a method of improving the performance of pretrained large language models (LLMs) and neural networks for specific tasks or data sets.
Parameter-efficient finetuning stands at the forefront of this pursuit, allowing researchers and practitioners to reuse pretrained models while minimizing their computational and resource footprints. It also allows us to train AI models on a broader range of hardware, including devices with limited compu...
LLMs之IA3:《Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning》翻译与解读 《Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning》翻译与解读 地址 论文地址:https:///abs/2205.05638 ...