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cs.LG2024
How to Parameterize Asymmetric Quantization Ranges for Quantization-Aware Training
Jaeseong You, Minseop Park, Kyunggeun Lee +3
This paper investigates three different parameterizations of asymmetric uniform quantization for quantization-aware training: (1) scale and offset, (2) minimum and maximum, and (3)…
cs.LG2024★ 2 cited
GPTVQ: The Blessing of Dimensionality for LLM Quantization
Mart van Baalen, Andrey Kuzmin, Ivan Koryakovskiy +6
In this work we show that the size versus accuracy trade-off of neural network quantization can be significantly improved by increasing the quantization dimensionality. We propose…