Quantization publisher

Memphi

Memphi publishes 1 quantizations across 1 models in our index, averaging 4.902 effective bits per weight. Their files differ in size from other publishers' builds of the same nominal quantization on 2 of the pairs we can compare — the same label does not mean the same file.

From the file· summed file bytes
Repositories
1
Quantizations
1
Models covered
1
Avg effective bpw
4.902
across their files

Same model, same quant label, different bytes

largest disagreements first
ModelQuantMemphivsTheirsDifference
Llama-3.1-8B-Lexi-Uncensored-V2Q4_K_M4.58 GiBQuantFactory4.58 GiB-0.0%
Llama-3.1-8B-Lexi-Uncensored-V2Q4_K_M4.58 GiBbartowski4.58 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
Llama-3.1-8B-Lexi-Uncensored-V214.58 GiB