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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2512.02764 [cs.CL] |
| (or arXiv:2512.02764v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2512.02764 arXiv-issued DOI via DataCite |
|
| Related DOI: | https://doi.org/10.18653/v1/2026.eacl-demo.15
DOI(s) linking to related resources |
Submission history
From: Robert Belanec [view email]
[v1]
Tue, 2 Dec 2025 13:44:41 UTC (1,192 KB)
[v2]
Sun, 22 Feb 2026 16:16:16 UTC (1,192 KB)
[v3]
Tue, 12 May 2026 18:50:51 UTC (1,192 KB)