Abstract
Parameter-Efficient Fine-Tuning (PEFT) methods address the increasing size of Large Language Models (LLMs). Currently, many newly introduced PEFT methods are challenging to replicate, deploy, or compare with one another. To address this, we introduce PEFT-Factory, a unified framework for efficient fine-tuning LLMs using both off-the-shelf and custom PEFT methods. While its modular design supports extensibility, it natively provides a representative set of 19 PEFT methods, 27 classification and text generation datasets addressing 12 tasks, and both standard and PEFT-specific evaluation metrics. As a result, PEFT-Factory provides a ready-to-use, controlled, and stable environment, improving replicability and benchmarking of PEFT methods. PEFT-Factory is a downstream framework that originates from the popular LLaMA-Factory, and is publicly available at this https URL
Abstract (translated)
参数高效微调（Parameter-Efficient Fine-Tuning，PEFT）方法旨在解决大型语言模型（Large Language Models，LLMs）尺寸不断增大的问题。目前，许多新引入的PEFT方法在复制、部署或相互比较时存在挑战性。为了解决这一问题，我们推出了PEFT-Factory，这是一个统一框架，用于通过现成和自定义PEFT方法高效微调LLMs。该框架采用模块化设计支持扩展性，并原生提供了一套具有代表性的19种PEFT方法、27个涵盖12项任务的分类和文本生成数据集，以及标准和特定于PEFT的评估指标。因此，PEFT-Factory为PEFT方法提供了可直接使用且稳定的环境，提高了其复制性和基准测试能力。 PEFT-Factory起源于流行的LLaMA-Factory，并可在以下网址公开获取：[https://github.com/PeLo-Lab/peft-factory](https://github.com/PeLo-Lab/peft-factory)
URL
https://arxiv.org/abs/2512.02764
