MoEITS is an information-theoretic algorithm for pruning experts in MoE-LLMs that produces models with higher accuracy and greater size reduction than prior state-of-the-art methods on Mixtral 8x7B, Qwen1.5-2.7B, and DeepSeek-V2-Lite.
BoolQ: Exploring the surprising difficulty of natural yes/no questions
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EMO pretrains MoEs using document boundaries to induce semantic expert specialization, enabling modular subset deployment with minimal accuracy loss unlike standard MoEs.
citing papers explorer
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MoEITS: A Green AI approach for simplifying MoE-LLMs
MoEITS is an information-theoretic algorithm for pruning experts in MoE-LLMs that produces models with higher accuracy and greater size reduction than prior state-of-the-art methods on Mixtral 8x7B, Qwen1.5-2.7B, and DeepSeek-V2-Lite.
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EMO: Pretraining Mixture of Experts for Emergent Modularity
EMO pretrains MoEs using document boundaries to induce semantic expert specialization, enabling modular subset deployment with minimal accuracy loss unlike standard MoEs.