简介:本文将介绍轻量化微调(Parameter-Efficient Fine-Tuning)的概念、优势、应用场景和实现方法。通过轻量化微调,可以在保持模型性能的同时,显著降低模型的大小和计算成本,为深度学习模型在资源受限的场景下提供更高效的应用方案。 千帆应用开发平台“智能体Pro”全新上线 限时免费体验 面向慢思考场景,支持低代码配置的...
PEFT(Parameter-Efficient Fine-Tuning)是一种在预训练模型基础上进行微调的技术,旨在通过调整少量参数来适应特定任务,从而减少计算资源和时间消耗。以下是PEFT微调的基本步骤和常见方法: 1. 选择预训练模型 首先,选择一个适合任务的预训练模型,如BERT、GPT等。 2. 确定微调策略 PEFT的核心在于只调整部分参数,常见策略...
参数高效微调(Parameter-Efficient Fine-Tuning, PEFT)作为一种新兴的优化策略,旨在通过最小化需要调整的参数数量,实现高效的模型适应和性能提升。本文将深入探讨PEFT的核心概念、技术实现、应用案例以及未来的发展方向。 一、PEFT的核心概念 PEFT的核心思想是通过调整模型中的一部分参数,而不是全部参数,来实现对特定任务...
为此,PEFT(Parameter-Efficient Fine-Tuning)技术应运而生。PEFT是一种参数高效的微调方法,旨在在保持模型泛化能力的同时,仅通过微小的参数调整来适应特定任务。这种方法的核心思想是在微调过程中限制新引入的参数数量,从而减少过拟合的风险。一、PEFT的工作原理PEFT的基本思想是在微调过程中对预训练模型的参数进行限制,...
Continual learningParameter-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 ...
PEFT(Parameter-Efficient Fine-Tuning参数高效微调) huggingface:PEFT (huggingface.co) 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 ...
What is parameter-efficient fine-tuning (PEFT)? 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. By training a small set of parameters and preserving most of the lar...
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, ...
Arixv 2403 | Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey 论文:https://arxiv.org/abs/2403.14608 语雀文档:https://www.yuque.com/lart/papers/gvqrizgggd22g88n 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着的成就。然而,其前所未有的规模伴随着巨大的计算...
Additional Guidelines for Parameter-Efficient Fine-Tuning Prior to initiating your PEFT, ensure you’ve readied all necessary datasets and checkpoints. To load a pretrained checkpoint for PEFT, set therestore_from_pathfield in themodelsection to the path of the pretrained checkpoint in.nemoforma...