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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

18 October 2024
Enneng Yang
Li Shen
Zhenyi Wang
G. Guo
Xingwei Wang
Xiaocun Cao
Jie Zhang
Dacheng Tao
    MoMe
ArXivPDFHTML

Papers citing "SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task Learning with Deep Representation Surgery"

4 / 4 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
14
0
0
10 May 2025
From Task-Specific Models to Unified Systems: A Review of Model Merging Approaches
Wei Ruan
Tianze Yang
Y. Zhou
Tianming Liu
Jin Lu
MoMe
88
0
0
13 Mar 2025
Scalable Model Merging with Progressive Layer-wise Distillation
Scalable Model Merging with Progressive Layer-wise Distillation
Jing Xu
Jiazheng Li
J. Zhang
MoMe
FedML
79
0
0
18 Feb 2025
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
Li Shen
A. Tang
Enneng Yang
G. Guo
Yong Luo
Lefei Zhang
Xiaochun Cao
Bo Du
Dacheng Tao
MoMe
21
1
0
29 Oct 2024
1