PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models
This work addresses a practical problem for researchers and practitioners in machine learning by offering a tool to standardize and simplify the evaluation of PEFT methods, though it is incremental as it builds on existing frameworks like LLaMA-Factory.
The paper tackles the challenge of replicating, deploying, and comparing Parameter-Efficient Fine-Tuning (PEFT) methods for Large Language Models by introducing PEFT-Factory, a unified framework that provides a ready-to-use environment with 19 PEFT methods, 27 datasets, and evaluation metrics, improving replicability and benchmarking.
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 https://github.com/kinit-sk/PEFT-Factory