paper: Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning link: arxiv.org/pdf/2303.1564 TL,DR: 参数高效的微调 相关综述 Introduction 人类训练的模型从330M的BERT large 涨到了 1760亿 的GPT-3 但是显卡的size只是从12G 提高到了80G 如果在有限的资源下,微调巨大的语言模型,成为一个...
于是乎,大家开始走Parameter Efficient Fine Tuning的道路,即,对少量或者额外的一些模型参数进行微调,以让大模型可以高效训练更好地完成各种下游的应用,人Huggingface也说了,目前先进的一些PEFT技术已经实现了与微调整个模型一样的性能。另外,介绍里也提到,PEFT目前已经与Transformers、Diffusers和Accelerate集成了,这样大家也...
Parameter-efficient fine-tuning aims to achieve performance comparable to fine-tuning, using fewer trainable parameters. Several strategies (e.g., Adapters, prefix tuning, BitFit, and LoRA) have been proposed. However, their designs are hand-crafted separately, and it remains unclear whether ...
Motivated by the potential of Parameter Efficient Fine-Tuning (PEFT), we aim to address these issues by effectively leveraging PEFT to improve limited data and GPU resource issues in multi-scanner setups. In this paper, we introduce PETITE , P arameter E fficient Fine- T uning for Mult I ...
2.2 Part Parameter Tuning 通过训练预训练模型中部分参数,减少模型训练参数,提升模型训练效率,其中包括 layernorm&head tune(只训练模型的 layernorm 和 head 层)等。 代表论文: 论文标题: BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models ...
efficient fine-tuning (PEFT) (e.g. adapter modules, prompt tuning, sparse update methods, etc.) offers an alternative paradigm where a small set of parameters are trained to enable a model to perform the new task. In this paper, we rigorously compare few-shot ICL and PEFT and demonstrate...
Parameter-efficient fine-tuning (PEFT) of pre-trained language models (PLMs) has emerged as a highly successful approach, with training only a small number of parameters without sacrificing performance and becoming the de-facto learning paradigm with the increasing size of PLMs. However, existing PE...
Parameter-Efficient Finetuning Prompt Tuning And Prefix Tuning Adapters Extending Prefix Tuning and Adapters: LLaMA-Adapter Conclusion Finetuning Large Language Models Since GPT-2 (Radford et al.) and GPT-3 (Brown et al.), we have seen that generative large language models (LLMs) pretrained on...
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies The large models, as predicted by scaling raw forecasts, have made groundbreaking progress in many fields, particularly in natural language generation task... L Wang,S Chen,L Jiang,... 被引量: 0发表: 2024年 Exploring ...
paper:AdaptiveBudgetAllocationforParameter-EfficientFine-Tuning link:https://arxiv.org/pdf/2303.10512v1.pdf 一些解读:sliderSun:使用PEFT微调LLMs Motivation Lora中,对每个矩阵使用相同的分支。不过这些分支的重要性是不一样的。当我们的budge有限的时候,我能就希望能够动态地设计Lora分支的大小。对于不重要的地方...