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.
A Comprehensive Evaluation of Quantization Strategies for Large Language Models , booktitle =
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Empirical evaluation of quantization effects on eight LLMs across bit widths, showing performance generally declines at lower precision but with model-size-dependent resilience and acceptable accuracy at 2 bits for many cases.
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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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K-Quantization and its Impact on Output Performance
Empirical evaluation of quantization effects on eight LLMs across bit widths, showing performance generally declines at lower precision but with model-size-dependent resilience and acceptable accuracy at 2 bits for many cases.