Enhancing Multi-modal Models with Heterogeneous MoE Adapters for Fine-tuning
Fuente:
arXiv
Saved in:
| Main Authors: | Zhou, Sashuai, Huang, Hai, Xia, Yan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge
by: Hu, Gang, et al.
Published: (2025)
by: Hu, Gang, et al.
Published: (2025)
MoE Parallel Folding: Heterogeneous Parallelism Mappings for Efficient Large-Scale MoE Model Training with Megatron Core
by: Liu, Dennis, et al.
Published: (2025)
by: Liu, Dennis, et al.
Published: (2025)
MoE-Sieve: Routing-Guided LoRA for Efficient MoE Fine-Tuning
by: Manzoni, Andrea
Published: (2026)
by: Manzoni, Andrea
Published: (2026)
MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs
by: Xia, Xinfeng, et al.
Published: (2025)
by: Xia, Xinfeng, et al.
Published: (2025)
Innovator: Scientific Continued Pretraining with Fine-grained MoE Upcycling
by: Liao, Ning, et al.
Published: (2025)
by: Liao, Ning, et al.
Published: (2025)
MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios
by: Li, Shuhuai, et al.
Published: (2026)
by: Li, Shuhuai, et al.
Published: (2026)
Faster MoE LLM Inference for Extremely Large Models
by: Yang, Haoqi, et al.
Published: (2025)
by: Yang, Haoqi, et al.
Published: (2025)
Multi-Head Attention as a Source of Catastrophic Forgetting in MoE Transformers
by: Chen, Anrui, et al.
Published: (2026)
by: Chen, Anrui, et al.
Published: (2026)
MoE Pathfinder: Trajectory-driven Expert Pruning
by: Yang, Xican, et al.
Published: (2025)
by: Yang, Xican, et al.
Published: (2025)
Aggregating Low Rank Adapters in Federated Fine-tuning
by: Trautmann, Evelyn, et al.
Published: (2025)
by: Trautmann, Evelyn, et al.
Published: (2025)
UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation
by: Duan, Lunhao, et al.
Published: (2024)
by: Duan, Lunhao, et al.
Published: (2024)
MoE-PHDS: One MoE checkpoint for flexible runtime sparsity
by: Hannah, Lauren. A, et al.
Published: (2025)
by: Hannah, Lauren. A, et al.
Published: (2025)
Llama 3 Meets MoE: Efficient Upcycling
by: Vavre, Aditya, et al.
Published: (2024)
by: Vavre, Aditya, et al.
Published: (2024)
GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
by: Wu, Haoze, et al.
Published: (2024)
by: Wu, Haoze, et al.
Published: (2024)
Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning
by: Li, Weihang, et al.
Published: (2026)
by: Li, Weihang, et al.
Published: (2026)
MoORE: SVD-based Model MoE-ization for Conflict- and Oblivion-Resistant Multi-Task Adaptation
by: Yuan, Shen, et al.
Published: (2025)
by: Yuan, Shen, et al.
Published: (2025)
MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache
by: Xue, Leyang, et al.
Published: (2024)
by: Xue, Leyang, et al.
Published: (2024)
KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
by: Xu, Zukang, et al.
Published: (2026)
by: Xu, Zukang, et al.
Published: (2026)
Multi-Head LatentMoE and Head Parallel: Communication-Efficient and Deterministic MoE Parallelism
by: Cui, Chenwei, et al.
Published: (2026)
by: Cui, Chenwei, et al.
Published: (2026)
MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
by: Ma, Songkai, et al.
Published: (2025)
by: Ma, Songkai, et al.
Published: (2025)
RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation
by: Zhou, Sashuai, et al.
Published: (2025)
by: Zhou, Sashuai, et al.
Published: (2025)
Effective MoE-based LLM Compression by Exploiting Heterogeneous Inter-Group Experts Routing Frequency and Information Density
by: Mi, Zhendong, et al.
Published: (2026)
by: Mi, Zhendong, et al.
Published: (2026)
MergeMoE: Efficient Compression of MoE Models via Expert Output Merging
by: Miao, Ruijie, et al.
Published: (2025)
by: Miao, Ruijie, et al.
Published: (2025)
GMoE: Empowering LLMs Fine-Tuning via MoE Graph Collaboration
by: Bai, Ting, et al.
Published: (2024)
by: Bai, Ting, et al.
Published: (2024)
MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing
by: Ma, Haiyue, et al.
Published: (2025)
by: Ma, Haiyue, et al.
Published: (2025)
Horseshoe Mixtures-of-Experts (HS-MoE)
by: Polson, Nick, et al.
Published: (2026)
by: Polson, Nick, et al.
Published: (2026)
FloE: On-the-Fly MoE Inference on Memory-constrained GPU
by: Zhou, Yuxin, et al.
Published: (2025)
by: Zhou, Yuxin, et al.
Published: (2025)
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
by: Zhang, Renrui, et al.
Published: (2023)
by: Zhang, Renrui, et al.
Published: (2023)
AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference
by: Zhong, Shuzhang, et al.
Published: (2024)
by: Zhong, Shuzhang, et al.
Published: (2024)
Certain Head, Uncertain Tail: Expert-Sample for Test-Time Scaling in Fine-Grained MoE
by: Chen, Yuanteng, et al.
Published: (2026)
by: Chen, Yuanteng, et al.
Published: (2026)
MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
by: Chen, Xiaodong, et al.
Published: (2025)
by: Chen, Xiaodong, et al.
Published: (2025)
Harder Tasks Need More Experts: Dynamic Routing in MoE Models
by: Huang, Quzhe, et al.
Published: (2024)
by: Huang, Quzhe, et al.
Published: (2024)
DeepFusion: Accelerating MoE Training via Federated Knowledge Distillation from Heterogeneous Edge Devices
by: Li, Songyuan, et al.
Published: (2026)
by: Li, Songyuan, et al.
Published: (2026)
RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
by: Tang, Yuhan, et al.
Published: (2025)
by: Tang, Yuhan, et al.
Published: (2025)
DyMoE: Dynamic Expert Orchestration with Mixed-Precision Quantization for Efficient MoE Inference on Edge
by: Huang, Yuegui, et al.
Published: (2026)
by: Huang, Yuegui, et al.
Published: (2026)
SD-MoE: Spectral Decomposition for Effective Expert Specialization
by: Huang, Ruijun, et al.
Published: (2026)
by: Huang, Ruijun, et al.
Published: (2026)
MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE
by: Huang, Zongle, et al.
Published: (2025)
by: Huang, Zongle, et al.
Published: (2025)
Delta Decompression for MoE-based LLMs Compression
by: Gu, Hao, et al.
Published: (2025)
by: Gu, Hao, et al.
Published: (2025)
MoE Lens -- An Expert Is All You Need
by: Chaudhari, Marmik, et al.
Published: (2026)
by: Chaudhari, Marmik, et al.
Published: (2026)
FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving
by: Liu, Qingxiu, et al.
Published: (2026)
by: Liu, Qingxiu, et al.
Published: (2026)
Similar Items
-
FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge
by: Hu, Gang, et al.
Published: (2025) -
MoE Parallel Folding: Heterogeneous Parallelism Mappings for Efficient Large-Scale MoE Model Training with Megatron Core
by: Liu, Dennis, et al.
Published: (2025) -
MoE-Sieve: Routing-Guided LoRA for Efficient MoE Fine-Tuning
by: Manzoni, Andrea
Published: (2026) -
MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs
by: Xia, Xinfeng, et al.
Published: (2025) -
Innovator: Scientific Continued Pretraining with Fine-grained MoE Upcycling
by: Liao, Ning, et al.
Published: (2025)