Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation

• URL: http://arxiv.org/abs/2411.15224v3
• Date: Mon, 24 Mar 2025 04:59:31 GMT
• Title: Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation
• Authors: Seokil Ham, Hee-Seon Kim, Sangmin Woo, Changick Kim,
• Abstract summary: We introduce two key insights-driven strategies for parameter-efficient fine-tuning (PEFT) in Mamba architecture.<n>We propose a novel PEFT method specialized to Mamba architecture: Projector-targeted Diagonal-centric Linear Transformation (ProDiaL)
• Score: 14.57480367514423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the growing interest in Mamba architecture as a potential replacement for Transformer architecture, parameter-efficient fine-tuning (PEFT) approaches for Mamba remain largely unexplored. In our study, we introduce two key insights-driven strategies for PEFT in Mamba architecture: (1) While state-space models (SSMs) have been regarded as the cornerstone of Mamba architecture, then expected to play a primary role in transfer learning, our findings reveal that Projectors -- not SSMs -- are the predominant contributors to transfer learning. (2) Based on our observation, we propose a novel PEFT method specialized to Mamba architecture: Projector-targeted Diagonal-centric Linear Transformation (ProDiaL). ProDiaL focuses on optimizing only the pretrained Projectors for new tasks through diagonal-centric linear transformation matrices, without directly fine-tuning the Projector weights. This targeted approach allows efficient task adaptation, utilizing less than 1% of the total parameters, and exhibits strong performance across both vision and language Mamba models, highlighting its versatility and effectiveness.

Related papers

• Differential Mamba [16.613266337054267]
  Sequence models like Transformers and RNNs often overallocate attention to irrelevant context, leading to noisy intermediate representations.<n>Recent work has shown that differential design can mitigate this issue in Transformers, improving their effectiveness across various applications.<n>We show that a naive adaptation of differential design to Mamba is insufficient and requires careful architectural modifications.
  arXiv  Detail & Related papers  (2025-07-08T17:30:14Z)
• Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection [88.47928738482719]
  Linear State Space Models (SSMs) offer remarkable performance gains in sequence modeling.<n>Recent advances, such as Mamba, further enhance SSMs with input-dependent gating and hardware-aware implementations.<n>We introduce Routing Mamba (RoM), a novel approach that scales SSM parameters using sparse mixtures of linear projection experts.
  arXiv  Detail & Related papers  (2025-06-22T19:26:55Z)
• DYNAMAX: Dynamic computing for Transformers and Mamba based architectures [2.5739385355356714]
  Early exits (EEs) offer a promising approach to reducing computational costs and latency by dynamically terminating inference once a satisfactory prediction confidence on a data sample is achieved. This work introduces DYNAMAX, the first framework to exploit the unique properties of Mamba architectures for early exit mechanisms.
  arXiv  Detail & Related papers  (2025-04-29T16:38:15Z)
• RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing [47.536214063122515]
  RoMA is a framework that enables scalable self-supervised pretraining of RS foundation models using large-scale, diverse, unlabeled data.<n>RoMA enhances scalability for high-resolution images through a tailored auto-regressive learning strategy.<n> experiments across scene classification, object detection, and semantic segmentation tasks demonstrate that RoMA-pretrained Mamba models consistently outperform ViT-based counterparts in both accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-03-13T14:09:18Z)
• TransMamba: Fast Universal Architecture Adaption from Transformers to Mamba [88.31117598044725]
  We explore cross-architecture training to transfer the ready knowledge in existing Transformer models to alternative architecture Mamba, termed TransMamba. Our approach employs a two-stage strategy to expedite training new Mamba models, ensuring effectiveness in across uni-modal and cross-modal tasks. For cross-modal learning, we propose a cross-Mamba module that integrates language awareness into Mamba's visual features, enhancing the cross-modal interaction capabilities of Mamba architecture.
  arXiv  Detail & Related papers  (2025-02-21T01:22:01Z)
• From Markov to Laplace: How Mamba In-Context Learns Markov Chains [36.22373318908893]
  We study in-context learning on Markov chains and uncover a surprising phenomenon. Unlike transformers, even a single-layer Mamba efficiently learns the in-context Laplacian smoothing estimator. These theoretical insights align strongly with empirical results and represent the first formal connection between Mamba and optimal statistical estimators.
  arXiv  Detail & Related papers  (2025-02-14T14:13:55Z)
• MobileMamba: Lightweight Multi-Receptive Visual Mamba Network [51.33486891724516]
  Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. We propose the MobileMamba framework, which balances efficiency and performance. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-24T18:01:05Z)
• MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba [0.5530212768657544]
  Mamba, a State Space Model (SSM)-based model, has attracted attention as a potential alternative to Transformers. We investigate the effectiveness of existing PEFT methods for Transformers when applied to Mamba. We propose new Mamba-specific PEFT methods that leverage the distinctive structure of Mamba.
  arXiv  Detail & Related papers  (2024-11-06T11:57:55Z)
• Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation [53.88562288388169]
  A common strategy for. Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViTs) involves adapting the model to downstream tasks. We propose a novel PEFT approach inspired by Singular Value Decomposition (SVD) for representing the adaptation matrix. SVD decomposes a matrix into the product of a left unitary matrix, a diagonal matrix of scaling values, and a right unitary matrix.
  arXiv  Detail & Related papers  (2024-10-30T12:08:30Z)
• ReMamba: Equip Mamba with Effective Long-Sequence Modeling [50.530839868893786]
  We propose ReMamba, which enhances Mamba's ability to comprehend long contexts. ReMamba incorporates selective compression and adaptation techniques within a two-stage re-forward process.
  arXiv  Detail & Related papers  (2024-08-28T02:47:27Z)
• Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models [92.36510016591782]
  We present a method that is able to distill a pretrained Transformer architecture into alternative architectures such as state space models (SSMs) Our method, called MOHAWK, is able to distill a Mamba-2 variant based on the Phi-1.5 architecture using only 3B tokens and a hybrid version (Hybrid Phi-Mamba) using 5B tokens. Despite using less than 1% of the training data typically used to train models from scratch, Phi-Mamba boasts substantially stronger performance compared to all past open-source non-Transformer models.
  arXiv  Detail & Related papers  (2024-08-19T17:48:11Z)
• MambaVision: A Hybrid Mamba-Transformer Vision Backbone [54.965143338206644]
  We propose a novel hybrid Mamba-Transformer backbone, MambaVision, specifically tailored for vision applications. We show that equipping the Mamba architecture with self-attention blocks in the final layers greatly improves its capacity to capture long-range spatial dependencies. For classification on the ImageNet-1K dataset, MambaVision variants achieve state-of-the-art (SOTA) performance in terms of both Top-1 accuracy and throughput.
  arXiv  Detail & Related papers  (2024-07-10T23:02:45Z)
• Mamba State-Space Models Are Lyapunov-Stable Learners [1.6385815610837167]
  Mamba state-space models (SSMs) were recently shown to outperform Transformer large language models (LLMs) across various tasks. We show that Mamba's recurrent dynamics are robust to small input changes. We also show that instruction tuning allows Mamba models to narrow this gap to 81% and Mamba-2 models to skyrocket over this gap to 132%.
  arXiv  Detail & Related papers  (2024-05-31T21:46:23Z)
• Demystify Mamba in Vision: A Linear Attention Perspective [72.93213667713493]
  Mamba is an effective state space model with linear computation complexity. We show that Mamba shares surprising similarities with linear attention Transformer. We propose a Mamba-Like Linear Attention (MLLA) model by incorporating the merits of these two key designs into linear attention.
  arXiv  Detail & Related papers  (2024-05-26T15:31:09Z)
• Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers [1.1499643186017316]
  We propose Cross-Architecture Transfer Learning (XATL) to improve efficiency of Transformer Language Models. Methodabbr significantly reduces the training time up to 2.5x times and converges to a better minimum with up to 2.6% stronger model on the LM benchmarks within the same compute budget.
  arXiv  Detail & Related papers  (2024-04-03T12:27:36Z)
• Is Mamba Capable of In-Context Learning? [63.682741783013306]
  State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL) This work provides empirical evidence that Mamba, a newly proposed state space model, has similar ICL capabilities.
  arXiv  Detail & Related papers  (2024-02-05T16:39:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.