PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models
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.
Fields of Study & Subjects
Topic Modeling
Natural Language Processing Techniques
Computational and Text Analysis Methods
Publication details
- Journal / source
- Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)
- Publisher
- Association for Computational Linguistics
- Pages
- 188–202
- Publication date
- 1 January 2026
- Language
- EN