Quantization publisher

Xviers

Xviers publishes 13 quantizations across 1 models in our index, averaging 5.549 effective bits per weight. Their files differ in size from other publishers' builds of the same nominal quantization on 9 of the pairs we can compare — the same label does not mean the same file.

From the file· summed file bytes
Repositories
1
Quantizations
13
Models covered
1
Avg effective bpw
5.549
across their files

Same model, same quant label, different bytes

largest disagreements first
ModelQuantXviersvsTheirsDifference
whisper-large-v3-turboQ8_00.81 GiBhandy-computer0.83 GiB-2.5%
whisper-large-v3-turboQ6_K0.63 GiBhandy-computer0.64 GiB-1.8%
whisper-large-v3-turboQ8_00.81 GiBhandy-computer0.83 GiB-1.4%
whisper-large-v3-turboQ4_00.44 GiBoxide-lab0.43 GiB+2.4%
whisper-large-v3-turboQ4_00.43 GiBoxide-lab0.44 GiB-2.3%
whisper-large-v3-turboQ4_10.49 GiBoxide-lab0.48 GiB+2.1%
whisper-large-v3-turboQ4_10.48 GiBoxide-lab0.49 GiB-2.1%
whisper-large-v3-turboQ8_00.81 GiBoxide-lab0.81 GiB-1.1%
whisper-large-v3-turboQ8_00.81 GiBoxide-lab0.81 GiB+1.1%

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

ModelQuantizationsSmallest
whisper-large-v3-turbo130.27 GiB