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Mixture-of-Experts

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코드

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

https://github.com/castorini/rank_llm

RankLLM: A Python Package for Reranking with LLMs

https://github.com/chumingqian/Model_Compression_For_YOLOV3-V4

Distilling the Knowledge in a Neural Network

https://github.com/consequentai/fneval

Mixtral of Experts

https://github.com/copenlu/TESTED

Cross-Domain Label-Adaptive Stance Detection

https://github.com/cran/meteorits

Non-Normal Mixtures of Experts

https://github.com/davidmrau/mixture-of-experts

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

https://github.com/deepseek-ai/dualpipe

DeepSeek-V3 Technical Report

https://github.com/dgme-syz/MOE-LPR

MoE-LPR: Multilingual Extension of Large Language Models through Mixture-of-Experts with Language Priors Routing

https://github.com/elasticdeeplearning/edl

Distilling the Knowledge in a Neural Network

https://github.com/eugene/spngp

Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks

https://github.com/fabianwinter93/JAX

Fast Feedforward Networks

https://github.com/facebookresearch/fairscale

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

https://github.com/facebookresearch/sonar

No Language Left Behind: Scaling Human-Centered Machine Translation

https://github.com/fchamroukhi/MEteorits

Non-Normal Mixtures of Experts

https://github.com/fchamroukhi/SNMoE

Non-Normal Mixtures of Experts

https://github.com/franknb/Text-Summarization

Distilling the Knowledge in a Neural Network

https://github.com/funaudiollm/inspiremusic

Qwen2.5 Technical Report

https://github.com/gabinsane/multi-vaes-in-robotics

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models

https://github.com/gabinsane/multimodal-vae-comparison

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models

https://github.com/geefmegeld/pmoe-backdoor

Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks

https://github.com/generation21/generation6011

Distilling the Knowledge in a Neural Network

https://github.com/giangdip2410/competesmoe

CompeteSMoE - Effective Training of Sparse Mixture of Experts via Competition

https://github.com/haro-cyber/KnowledgeDistillation

Distilling the Knowledge in a Neural Network

https://github.com/haysacks/spot-the-coin

Distilling the Knowledge in a Neural Network

https://github.com/hit-scir/chinese-mixtral-8x7b

Mixtral of Experts

https://github.com/hugozhl/hetu

EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

https://github.com/hunto/image_classification_sota

Distilling the Knowledge in a Neural Network

https://github.com/huyphan168/PEER

Mixture of A Million Experts

https://github.com/ibm/dolomite-engine

Scattered Mixture-of-Experts Implementation

https://github.com/jamesding000/specgr

Towards Universal Sequence Representation Learning for Recommender Systems

https://github.com/jaychoi12/LG_KD

Distilling the Knowledge in a Neural Network

https://github.com/jchen98/compression

Distilling the Knowledge in a Neural Network

https://github.com/jingyaogong/minimind

Mixtral of Experts

https://github.com/jiyaowei/lp_nkgs

HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion

https://github.com/jpmcd/TensorFlow-KnowledgeDistillation

Distilling the Knowledge in a Neural Network

https://github.com/jquesnelle/scaled-rope

SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

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