来自Northeastern University 及University of California, Riverside等大学的研究者发表了“Parameter-Efficient Fine-Tuningfor 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:
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...
Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to new tasks. NVIDIA NIM for LLMs (NIM for LLMs) supports LoRA PEFT adapters trained by the NeMo Framework and Hugging Face Transformers libraries. When submitting inference requests to the NIM, t...
Parameter-Efficient Fine-Tuning (PEFT) Quick Start Guide Supported PEFT methods Positional embeddings Positional interpolation References Megatron Core Customization References Machine Translation Models ONNX Export of Megatron Models Quantization Multimodal Language Models ...
🤗 PEFT Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only ...
Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only fine-tune ...
为了解决这个问题,PEFT库(Parameter-Efficient Fine-Tuning)应运而生。PEFT库是一种用于高效微调预训练语言模型的库。它的基本原理是不需要微调所有的模型参数,而是只微调少量的额外参数,从而显著降低计算和存储成本。通过只微调少量参数,PEFT库可以在不牺牲性能的情况下,实现大规模模型的快速适应。PEFT库的实现方法主要...
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 ...
首先,这个PEFT大家有没有很熟悉呀,咱们先来看看Huggingface是怎么说这个PEFT的。 Huggingface的GitHub链接:github.com/huggingface/ 网站截图 目前大语言模型如果要进行微调,主要有两种方式Full parameter fine-tuning和这个Parameter Efficient Fine Tuning。Full parameter fine-tuning显而易见,那就是大语言模型整个语言模型...
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...