Quantization publisher

dominguesm

dominguesm publishes 10 quantizations across 1 models in our index, averaging 7.082 effective bits per weight. Their files differ in size from other publishers' builds of the same nominal quantization on 8 of the pairs we can compare — the same label does not mean the same file.

From the file· summed file bytes
Repositories
1
Quantizations
10
Models covered
1
Avg effective bpw
7.082
across their files

Same model, same quant label, different bytes

largest disagreements first
ModelQuantdominguesmvsTheirsDifference
NVIDIA-Nemotron-Nano-9B-v2Q4_04.94 GiBbartowski4.97 GiB-0.6%
NVIDIA-Nemotron-Nano-9B-v2Q4_15.43 GiBbartowski5.43 GiB-0.0%
NVIDIA-Nemotron-Nano-9B-v2Q4_K_M6.08 GiBbartowski6.08 GiB-0.0%
NVIDIA-Nemotron-Nano-9B-v2Q4_K_S5.79 GiBbartowski5.79 GiB-0.0%
NVIDIA-Nemotron-Nano-9B-v2Q5_K_M6.58 GiBbartowski6.58 GiB-0.0%
NVIDIA-Nemotron-Nano-9B-v2Q6_K8.51 GiBbartowski8.51 GiB-0.0%
NVIDIA-Nemotron-Nano-9B-v2Q2_K4.66 GiBbartowski4.66 GiB-0.0%
NVIDIA-Nemotron-Nano-9B-v2Q8_08.81 GiBbartowski8.81 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

ModelQuantizationsSmallest
NVIDIA-Nemotron-Nano-9B-v2104.66 GiB