4 ms·
This is really an awesome introduction into quantization! One small comment about the GPTQ section: It uses asymmetric quantization and does so layer by layer
by microtonal 2y ago
This is really an awesome introduction into quantization! One small comment about the GPTQ section:
It uses asymmetric quantization and does so layer by layer such that each layer is processed independently before continuing to the next
GPTQ also supports symmetric quantization and almost everyone uses it. The problem with GPTQ asymmetric quantization is that all popular implementations have a bug [1] where all zero/bias values of 0 are reset to 1 during packing (out of 16 possible biases in 4-bit quantization), leading to quite a large loss in quality. Interestingly, it seems that people initially observed that symmetric quantization worked better than asymmetric quantization (which is very counter-intuitive, but made GPTQ symmetric quantization far more popular) and only discovered later that it is due to a bug.
[1] https://notes.danieldk.eu/ML/Formats/GPTQ#Packing+integers https://notes.danieldk.eu/ML/Formats/GPTQ#Packing+integers
- denali53 2y agoAgree - great intro! Could someone with much more knowledge point more to BitNet and other 1-bit models... seems like developments here could lead to a step change in small/local models? What is the theoretical limit to the power of such models?