Reproduce “WaterKron and FlipFlop Hessian: Information-Theoretically Grounded Quantization with Kronecker-factored Hessians” on OpenResearch
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About this paper
Johann Birnick, Rayan Saab
WaterKron combines two-sided GPTQ with row- and column-specific quantization scales and entropy coding to compress language-model weights. The authors choose its Kronecker-factored Hessian factors with FlipFlop, which targets mismatch rather than Frobenius error. For Llama-3.2-1B at a target 2 bits per weight, FlipFlop-2 yields 0.76 nats KL on WikiText-2 versus 1.09 for Frobenius-2; this comparison is not a universal guarantee.
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