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Mixture-of-Experts

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/MinJunKang/Knowledge-Distillation_MNIST

Distilling the Knowledge in a Neural Network

https://github.com/MindCode-4/code-14/tree/main/RHFL

Robust Federated Learning by Mixture of Experts

https://github.com/MindCode-4/code-3/tree/main/deepseek_v2

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

https://github.com/MindCode-4/code-5/tree/main/time_series_transformer

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

https://github.com/MindSpore-paper-code-3/code6/tree/main/advanced_east

DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

https://github.com/MindSpore-scientific-2/code-14/tree/main/jamba

Jamba: A Hybrid Transformer-Mamba Language Model

https://github.com/MindSpore-scientific-2/code-14/tree/main/jetmoe

JetMoE: Reaching Llama2 Performance with 0.1M Dollars

https://github.com/MindSpore-scientific-2/code-14/tree/main/llava

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

https://github.com/MindSpore-scientific-2/code-7/tree/main/Fate

Fate: Fast Edge Inference of Mixture-of-Experts Models via Cross-Layer Gate

https://github.com/MindSpore-scientific-2/code-7/tree/main/Klotski

Klotski: Efficient Mixture-of-Expert Inference via Expert-Aware Multi-Batch Pipeline

https://github.com/MindSpore-scientific/code-9/tree/main/RHFL

Robust Federated Learning by Mixture of Experts

https://github.com/MiniMax-AI/MiniMax-01

MiniMax-01: Scaling Foundation Models with Lightning Attention

https://github.com/NeelayS/Knowledge-Distillation

Distilling the Knowledge in a Neural Network

https://github.com/PKU-YuanGroup/Video-LLaVA

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/dselect_k

DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning

https://github.com/RyanKim17920/DeepMoE

Deep Mixture of Experts via Shallow Embedding

https://github.com/SalesforceAIResearch/uni2ts

Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

https://github.com/TakieddineSOUALHI/Transfer_learning

Distilling the Knowledge in a Neural Network

https://github.com/TashinAhmed/BERT-Research

Gated Multimodal Units for Information Fusion

https://github.com/TashinAhmed/CNN_BERT

Gated Multimodal Units for Information Fusion

https://github.com/XueFuzhao/WideNet_Code

Go Wider Instead of Deeper

https://github.com/Yingqiancui/FT-Shield

FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models

https://github.com/agwaBom/towards_moe

Towards Understanding Mixture of Experts in Deep Learning

https://github.com/ai4bharat/indicbert

No Language Left Behind: Scaling Human-Centered Machine Translation

https://github.com/aidc-ai/ovis

Parrot: Multilingual Visual Instruction Tuning

https://github.com/aimagelab/mammoth

Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters

https://github.com/alibaba/EasyNLP/tree/master/examples/knowledge_distillation

Distilling the Knowledge in a Neural Network

https://github.com/altndrr/persona

Distilling the Knowledge in a Neural Network

https://github.com/andreeaiana/xmind

No Language Left Behind: Scaling Human-Centered Machine Translation

https://github.com/ankur-98/Multi-domain-Knowledge-Distillation

Distilling the Knowledge in a Neural Network

https://github.com/aquvitae/aquvitae

Distilling the Knowledge in a Neural Network

https://github.com/aryanasadianuoit/distillation_deep

Distilling the Knowledge in a Neural Network

https://github.com/baichuan-inc/Baichuan-Omni-1.5

Qwen2.5 Technical Report

https://github.com/baichuan-inc/baichuan-audio

Qwen2.5 Technical Report

https://github.com/byeongjun-park/SteerX

Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts

https://github.com/carmilea/carmilea

DeepSeek-V3 Technical Report

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.