Quantization publisher

nohugs4u69420

nohugs4u69420 publishes 4 quantizations across 1 models in our index, averaging 8.874 effective bits per weight. Their files differ in size from other publishers' builds of the same nominal quantization on 7 of the pairs we can compare — the same label does not mean the same file.

From the file· summed file bytes
Repositories
1
Quantizations
4
Models covered
1
Avg effective bpw
8.874
across their files

Same model, same quant label, different bytes

largest disagreements first
ModelQuantnohugs4u69420vsTheirsDifference
MiniCPM5-1B-Claude-Opus-Fable5-ThinkingF162.02 GiBGnLOLot2.02 GiB0.0%
MiniCPM5-1B-Claude-Opus-Fable5-ThinkingQ4_K_M0.64 GiBGnLOLot0.64 GiB0.0%
MiniCPM5-1B-Claude-Opus-Fable5-ThinkingQ4_K_M0.64 GiBliodon-ai0.64 GiB-0.0%
MiniCPM5-1B-Claude-Opus-Fable5-ThinkingQ5_K_M0.73 GiBGnLOLot0.73 GiB0.0%
MiniCPM5-1B-Claude-Opus-Fable5-ThinkingQ5_K_M0.73 GiBliodon-ai0.73 GiB-0.0%
MiniCPM5-1B-Claude-Opus-Fable5-ThinkingQ8_01.07 GiBGnLOLot1.07 GiB0.0%
MiniCPM5-1B-Claude-Opus-Fable5-ThinkingQ8_01.07 GiBliodon-ai1.07 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
MiniCPM5-1B-Claude-Opus-Fable5-Thinking40.64 GiB