Rhea Sanjay Sukthanker, Riccardo Grazzi, James Hensman, Pashmina Cameron · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28537 · open-access PDF
Post-training quantization (PTQ) compresses the weights of a trained model with limited compute and data. Down to 4 bits it is near-lossless, but at 2 bits scalar PTQ configurations fall to chance-level accuracy. We study three choices usually treated as implementation details that matter little at 4 bits but decide 2-bit quality: the quantization representation, the calibration data, and the recovery fine-tuning data, across six dense and mixture-of experts models (ranging from $3.8$ to $32$B parameters). Representation is the largest factor and vector codebooks restore usable models where sc
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