AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models
  with Unsupervised Coefficient Optimization
Authors: name: Yiyang Du, name: Xiaochen Wang, name: Chi Chen, name: Jiabo Ye, name: Yiru Wang, name: Peng Li, name: Ming Yan, name: Ji Zhang, name: Fei Huang, name: Zhifang Sui, name: Maosong Sun, name: Yang Liu

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.