Awesome-Model-Merging-Methods-Theories-Applications

[!TIP] If you have a relevant paper not included in the library, or have any clarification about the content of the paper, please contact us!

A comprehensive list of papers about 'Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities'.

Abstract

Model merging is an efficient empowerment technique in the machine learning community that does not require the collection of raw training data and does not require expensive computation. As model merging becomes increasingly prevalent across various fields, it is crucial to understand the available model merging techniques comprehensively. However, there is a significant gap in the literature regarding a systematic and thorough review of these techniques. To address this gap, this survey provides a comprehensive overview of model merging methods and theories, their applications in various domains and settings, and future research directions. Specifically, we first propose a new taxonomic approach that exhaustively discusses existing model merging methods. Secondly, we discuss the application of model merging techniques in large language models, multimodal large language models, and 10+ machine learning subfields, including continual learning, multi-task learning, few-shot learning, etc. Finally, we highlight the remaining challenges of model merging and discuss future research directions.

Citation

If you find our paper or this resource helpful, please consider cite:

@article{Survery_ModelMerging_2024,
  title={Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities},
  author={Yang, Enneng and Shen, Li and Guo, Guibing and Wang, Xingwei and Cao, Xiaochun and Zhang, Jie and Tao, Dacheng},
  journal={arXiv preprint arXiv:2408.07666},
  year={2024}
}

Thanks!

Framework

• Awesome-Model-Merging-Methods-Theories-Applications
  • Advanced Methods
    • Pre-Merging Methods
      • Linearization Fine-tuning
      • Architecture Transformation
      • Weight Alignment
      • Others
    • During Merging Methods
      • Basic Merging Methods
      • Weighted-based Merging Methods
      • Subspace-based Merging Methods
      • Routing-based Merging Methods
      • Post-calibration based Methods
    • Theories and Analysis of Model Merging
  • Application of Model Merging in Foundation Models
    • Model Merging in Large Language Model
      • Human Preference Alignment for LLMs
      • Detoxifcation of LLMs
      • Knowledge Unlearning of LLMs
      • Faster Training of LLMs
      • Combine the Capabilities of Expert LLMs
    • Model Merging in Multimodal Large Language Models
      • Model Merging for Multimodal Fusion
      • Model Merging for Cross-Modal Knowledge Transfer
    • Model Merging in Image Generative Models
      • Style Mixing in Generative Models
      • Reducing Training Cost of Generative Models
      • Enhancing the Faithfulness of Diffusion Models
  • Application of Model Merging in Different Machine Learning Subfields
    • Model Merging in Continual Learning
      • Model Merging to Mitigate Catastrophic Forgetting
    • Model Merging in Multi-Task/Multi-Objective/Multi-Domain/Auxiliary Learning
      • Model Merging for Knowledge Transfer in Multi-Task Learning
      • Model Merging for Knowledge Transfer in Multi-Objective Optimization
      • Model Merging for Knowledge Transfer in Multi-Domain Learning
      • Model Merging for Knowledge Transfer in Auxiliary Learning
    • Model Merging in Out-of-Distribution/Domain Generalization
      • Model Merging for Better Out-of-Distribution Generalization
      • Model Merging for Better Domain Generalization
    • Model Merging in Federated Learning
      • Model Merging for Local Knowledge Aggregation
    • Model Merging in Zero-shot/Few-shot Learning
      • Model Merging for Cross-task Generalization in Zero-shot Learning
      • Model Merging for Cross-task Generalization in Few-shot Learning
    • Model Merging in Adversarial Learning
      • Model Merging as an Attack
      • Model Merging as a Defense
  • Other Applications

Advanced Methods

Pre-Merging Methods

Linearization Fine-tuning

Architecture Transformation

Weight Alignment

Others

During Merging Methods

Basic Merging Methods

Weighted-based Merging Methods

Subspace-based Merging Method

Routing-based Merging Methods

Post-calibration based Methods

Theories and Analysis of Model Merging

Application of Model Merging in Foundation Models

Model Merging in Large Language Model

Human Preference Alignment for LLMs

Detoxifcation of LLMs

Knowledge Unlearning of LLMs

Faster Training of LLMs

Combine the Capabilities of Expert LLMs

Model Merging in Multimodal Large Language Models

Model Merging for Multimodal Fusion

Model Merging for Cross-Modal Knowledge Transfer

Model Merging in Image Generative Models

Style Mixing in Generative Models

Reducing Training Cost of Generative Models

Enhancing the Faithfulness of Diffusion Models

Application of Model Merging in Different Machine Learning Subfields

Model Merging in Continual Learning

Model Merging to Mitigate Catastrophic Forgetting

Model Merging in Multi-Task/Multi-Objective/Multi-Domain/Auxiliary Learning

Model Merging for Knowledge Transfer in Multi-Task Learning

Model Merging for Knowledge Transfer in Multi-Objective Optimization

Model Merging for Knowledge Transfer in Multi-Domain Learning

Model Merging for Knowledge Transfer in Auxiliary Learning

Model Merging in Out-of-Distribution/Domain Generalization

Model Merging for Better Out-of-Distribution Generalization

Model Merging for Better Domain Generalization

Model Merging in Federated Learning

Model Merging for Local Knowledge Aggregation

Model Merging in Zero-shot/Few-shot Learning

Model Merging for Cross-task Generalization in Zero-shot Learning

Model Merging for Cross-task Generalization in Few-shot Learning

Model Merging in Adversarial Learning

Model Merging as an Attack

Model Merging as a Defense

Other Applications

Star History

Contact

We welcome all researchers to contribute to this repository 'model merging in foundation models or machine learning'.

If you have a related paper that was not added to the library, please contact us.

Email: [email protected] / [email protected]