MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba
Keywords: parameter-efficient fine-tuning, PEFT, Mamba, State Space Model, SSM
TL;DR: We explore and propose parameter-efficient fine-tuning for Mamba.
Abstract: An ecosystem of Transformer-based models has been established by building large models with extensive data.
Parameter-efficient fine-tuning (PEFT) is a crucial technology for deploying these models to downstream tasks with minimal cost while achieving effective performance. Recently, Mamba, a State Space Model (SSM)-based model, has attracted attention as a potential alternative to Transformers. While many large-scale Mamba-based models have been proposed, efficiently adapting pre-trained Mamba-based models to downstream tasks remains unexplored.
In this paper, we conduct an exploratory analysis of PEFT methods for Mamba. We investigate the effectiveness of existing PEFT methods for Transformers when applied to Mamba. We also modify these methods to better align with the Mamba architecture. Additionally, we propose new Mamba-specific PEFT methods that leverage the distinctive structure of Mamba. Our experiments indicate that PEFT performs more effectively for Mamba than Transformers. Lastly, we demonstrate how to effectively combine multiple PEFT methods and provide a framework that outperforms previous works. To ensure reproducibility, we will release the code after publication.
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 6536
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