MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba

2024-11-06

arXiv.org

AI Summary

This work addresses parameter-efficient fine-tuning (PEFT) for the Mamba state space model (SSM), presenting the first systematic PEFT study tailored to SSM architectures. We propose three Mamba-specific PEFT methods: adaptive LoRA, SSM-aware adapters, and structure-aware low-rank updates, integrated within a multi-strategy collaborative framework. Experiments demonstrate that our approaches significantly outperform Transformer baselines and existing Transformer-oriented PEFT methods across downstream tasks, while introducing less than 0.1% additional parameters and incurring negligible inference latency overhead. Our core contributions are threefold: (1) establishing the empirical effectiveness and superiority of PEFT for Mamba; (2) revealing how intrinsic SSM structural properties—such as selective state transitions and hardware-aware scanning—critically inform PEFT design; and (3) releasing the first open-source, fully reproducible Mamba-PEFT toolkit, enabling standardized evaluation and further research.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• State Space Models
• Parameter Optimization
• Transformer-based Models

Innovation

• Parameter Efficient Fine-Tuning
• Mamba Model Optimization
• Combined PEFT Methods