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Efficient and Effective Weight-Ensembling Mixture of Experts for
  Multi-Task Model Merging

Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging

29 October 2024
Li Shen
Anke Tang
Enneng Yang
G. Guo
Yong Luo
Lefei Zhang
Xiaochun Cao
Bo Du
Dacheng Tao
    MoMe
ArXivPDFHTML

Papers citing "Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging"

5 / 5 papers shown
Title
QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration
QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration
HamidReza Imani
Jiaxin Peng
Peiman Mohseni
Abdolah Amirany
Tarek A. El-Ghazawi
MoE
31
0
0
10 May 2025
A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications
A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications
Siyuan Mu
Sen Lin
MoE
135
2
0
10 Mar 2025
How to Merge Your Multimodal Models Over Time?
How to Merge Your Multimodal Models Over Time?
Sebastian Dziadzio
Vishaal Udandarao
Karsten Roth
Ameya Prabhu
Zeynep Akata
Samuel Albanie
Matthias Bethge
MoMe
98
3
0
09 Dec 2024
Task Singular Vectors: Reducing Task Interference in Model Merging
Task Singular Vectors: Reducing Task Interference in Model Merging
Antonio Andrea Gargiulo
Donato Crisostomi
Maria Sofia Bucarelli
Simone Scardapane
Fabrizio Silvestri
Emanuele Rodolà
MoMe
87
9
0
26 Nov 2024
SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task
  Learning with Deep Representation Surgery
SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task Learning with Deep Representation Surgery
Enneng Yang
Li Shen
Zhenyi Wang
G. Guo
Xingwei Wang
Xiaocun Cao
Jie Zhang
Dacheng Tao
MoMe
37
4
0
18 Oct 2024
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