AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models
  with Unsupervised Coefficient Optimization
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.
67ec99788088196efd062021
Yiyang Du
67ec99788088196efd062022
Xiaochen Wang
67ec99788088196efd062023
Chi Chen
claimed_verified
2025-04-02T08:22:49.411Z
642086ed290342c5df85662d
/avatars/915a4d7b89455ae97b8544c79286ddf8.svg
Chi Chen
user
carboncoo
67ec99788088196efd062024
Jiabo Ye
67ec99788088196efd062025
Yiru Wang
67ec99788088196efd062026
Peng Li
67ec99788088196efd062027
Ming Yan
67ec99788088196efd062028
Ji Zhang
67ec99788088196efd062029
Fei Huang
67ec99788088196efd06202a
Zhifang Sui
67ec99788088196efd06202b
Maosong Sun
67ec99788088196efd06202c
Yang Liu
AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models
  with Unsupervised Coefficient Optimization
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.
AdaMMS, a novel unsupervised model merging method, effectively combines heterogeneous Multimodal Large Language Models by addressing architectural differences and parameter asymmetry.
model merging Large Language Models (LLMs) Multimodal Large Language Models (MLLMs) mapping linear interpolation unsupervised hyper-parameter selection vision-language benchmarks