TENP applies trapezoidal expert-neuron pruning to MoE models, retaining key experts while pruning others via projected neuron contribution, yielding only 1-point accuracy drop at 40% sparsity on DeepSeek with 10% code-generation gain.
EAC - M o E : Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
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VSRAQ is a MoE-specific quantization objective that combines value and structure alignment to preserve expert-selection behavior and reduce quality loss without inference overhead.
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TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts
TENP applies trapezoidal expert-neuron pruning to MoE models, retaining key experts while pruning others via projected neuron contribution, yielding only 1-point accuracy drop at 40% sparsity on DeepSeek with 10% code-generation gain.
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Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models
VSRAQ is a MoE-specific quantization objective that combines value and structure alignment to preserve expert-selection behavior and reduce quality loss without inference overhead.