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GPTQ

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papersTODAY 04:00 UTC

WaterKron Method Ties Kronecker-Factored Hessian Choice to Information Theory for Quantization

A new preprint introduces WaterKron, a post-training quantization approach that pairs two-sided GPTQ with waterfilling-based scaling that varies by row and column, along with entropy coding. The authors also present FlipFlop Hessian, a way of selecting Kronecker-factored Hessian approximations that they ground in information-theoretic arguments. The work targets how such approximations should be chosen when compressing neural networks after training.

quantization/llm-inference/arxiv/compression