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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/vrvlive/knowlege-distillation

Distilling the Knowledge in a Neural Network

https://github.com/wannabeOG/ExpertNet-Pytorch

Distilling the Knowledge in a Neural Network

https://github.com/weixiongust/rlhf-reward-modeling

Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts

https://github.com/willGuimont/transformers

Sparse Universal Transformer

https://github.com/wonbeomjang/Knowledge-Distilling-PyTorch

Distilling the Knowledge in a Neural Network

https://github.com/xhluca/dl-translate

No Language Left Behind: Scaling Human-Centered Machine Translation

https://github.com/xhlulu/dl-translate

No Language Left Behind: Scaling Human-Centered Machine Translation

https://github.com/yangyucheng000/Papers/tree/main/DistillExplanation

Distilling the Knowledge in a Neural Network

https://github.com/yangyucheng000/University/tree/main/model-1/qwen2

Qwen2 Technical Report

https://github.com/yangyucheng000/University/tree/main/model-1/roc_bert

COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search

https://github.com/yangyucheng000/University/tree/main/model-2/jamba

Jamba-1.5: Hybrid Transformer-Mamba Models at Scale

https://github.com/yangyucheng000/University/tree/main/model-3/qwen2_moe

Qwen2 Technical Report

https://github.com/yangyucheng000/papercode-2/tree/main/pfedkd-master

pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning

https://github.com/yangze01/Distilling_the_Knowledge_in_a_Neural_Network

Distilling the Knowledge in a Neural Network

https://github.com/yangze01/Distilling_the_Knowledge_in_a_Neural_Network_pytorch

Distilling the Knowledge in a Neural Network

https://github.com/yell/kaggle-camera

Distilling the Knowledge in a Neural Network

https://github.com/yikangshen/megablocks

ST-MoE: Designing Stable and Transferable Sparse Expert Models

https://github.com/ymcui/chinese-llama-alpaca-3

Rethinking LLM Language Adaptation: A Case Study on Chinese Mixtral

https://github.com/yoshitomo-matsubara/torchdistill

Distilling the Knowledge in a Neural Network

https://github.com/yurangja99/Pytorch-Sketch-RNN

Distilling the Knowledge in a Neural Network

https://github.com/yzd-v/cls_KD

Distilling the Knowledge in a Neural Network

https://github.com/yzhao062/yzhao062

ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels

https://github.com/zh320/realtime-semantic-segmentation-pytorch

Distilling the Knowledge in a Neural Network

https://github.com/ziye2chen/LLMs-for-Mathematical-Analysis

Qwen2 Technical Report

https://github.com/zju-vipa/KamalEngine

KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation

https://github.com/zongwei97/untrack

XTrack: Multimodal Training Boosts RGB-X Video Object Trackers

https://github.com/zurichnlp/nmtscore

No Language Left Behind: Scaling Human-Centered Machine Translation

https://huggingface.co/isp-uv-es/superIX

Swin2-MoSE: A New Single Image Super-Resolution Model for Remote Sensing

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