title: AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models
    with Unsupervised Coefficient Optimization
summary: Recently, model merging methods have demonstrated powerful strengths in
  combining abilities on various tasks from multiple Large Language Models
  (LLMs). While previous model merging methods mainly focus on merging
  homogeneous models with identical architecture, they meet challenges when
  dealing with Multimodal Large Language Models (MLLMs) with inherent
  heterogeneous property, including differences in model architecture and the
  asymmetry in the parameter space. In this work, we propose AdaMMS, a novel
  model merging method tailored for heterogeneous MLLMs. Our method tackles the
  challenges in three steps: mapping, merging and searching. Specifically, we
  first design mapping function between models to apply model merging on MLLMs
  with different architecture. Then we apply linear interpolation on model
  weights to actively adapt the asymmetry in the heterogeneous MLLMs. Finally in
  the hyper-parameter searching step, we propose an unsupervised hyper-parameter
  selection method for model merging. As the first model merging method capable
  of merging heterogeneous MLLMs without labeled data, extensive experiments on
  various model combinations demonstrated that AdaMMS outperforms previous model
  merging methods on various vision-language benchmarks.
paper_authors._id: 67ec99788088196efd062021
paper_authors.name: Yiyang Du
paper_authors._id: 67ec99788088196efd062022
paper_authors.name: Xiaochen Wang
paper_authors._id: 67ec99788088196efd062023
paper_authors.name: Chi Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-02T08:22:49.411Z
paper_authors._id: 642086ed290342c5df85662d
paper_authors.avatarUrl: /avatars/915a4d7b89455ae97b8544c79286ddf8.svg
paper_authors.fullname: Chi Chen
paper_authors.type: user
paper_authors.user: carboncoo
paper_authors._id: 67ec99788088196efd062024
paper_authors.name: Jiabo Ye
paper_authors._id: 67ec99788088196efd062025
paper_authors.name: Yiru Wang
paper_authors._id: 67ec99788088196efd062026
paper_authors.name: Peng Li
paper_authors._id: 67ec99788088196efd062027
paper_authors.name: Ming Yan
paper_authors._id: 67ec99788088196efd062028
paper_authors.name: Ji Zhang
paper_authors._id: 67ec99788088196efd062029
paper_authors.name: Fei Huang
paper_authors._id: 67ec99788088196efd06202a
paper_authors.name: Zhifang Sui
paper_authors._id: 67ec99788088196efd06202b
paper_authors.name: Maosong Sun
paper_authors._id: 67ec99788088196efd06202c
paper_authors.name: Yang Liu
paper_title: AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models
    with Unsupervised Coefficient Optimization
paper_summary: Recently, model merging methods have demonstrated powerful strengths in
  combining abilities on various tasks from multiple Large Language Models
  (LLMs). While previous model merging methods mainly focus on merging
  homogeneous models with identical architecture, they meet challenges when
  dealing with Multimodal Large Language Models (MLLMs) with inherent
  heterogeneous property, including differences in model architecture and the
  asymmetry in the parameter space. In this work, we propose AdaMMS, a novel
  model merging method tailored for heterogeneous MLLMs. Our method tackles the
  challenges in three steps: mapping, merging and searching. Specifically, we
  first design mapping function between models to apply model merging on MLLMs
  with different architecture. Then we apply linear interpolation on model
  weights to actively adapt the asymmetry in the heterogeneous MLLMs. Finally in
  the hyper-parameter searching step, we propose an unsupervised hyper-parameter
  selection method for model merging. As the first model merging method capable
  of merging heterogeneous MLLMs without labeled data, extensive experiments on
  various model combinations demonstrated that AdaMMS outperforms previous model
  merging methods on various vision-language benchmarks.
paper_ai_summary: AdaMMS, a novel unsupervised model merging method, effectively combines heterogeneous Multimodal Large Language Models by addressing architectural differences and parameter asymmetry.
paper_ai_keywords: model merging Large Language Models (LLMs) Multimodal Large Language Models (MLLMs) mapping linear interpolation unsupervised hyper-parameter selection vision-language benchmarks