Quantization publisher

KakTakOne

KakTakOne publishes 2 quantizations across 1 models in our index, averaging 10.440 effective bits per weight. Their files differ in size from other publishers' builds of the same nominal quantization on 1 of the pairs we can compare — the same label does not mean the same file.

From the file· summed file bytes
Repositories
1
Quantizations
2
Models covered
1
Avg effective bpw
10.440
across their files

Same model, same quant label, different bytes

largest disagreements first
ModelQuantKakTakOnevsTheirsDifference
Huihui-gemma-4-12B-coder-fable5-composer2.5-v1-abliteratedQ4_K_M6.87 GiBmradermacher6.87 GiB-0.0%

A quantization label describes a target, not a recipe. Publishers make different choices about which tensors to keep at higher precision, and some apply an importance matrix while others don't — so two files both honestly labelled the same thing can differ measurably in size and in quality.

Models they publish