Quantization publisher
sumeshi
sumeshi publishes 1 quantizations across 1 models in our index, averaging 16.104 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
1
Models covered
1
Avg effective bpw
16.104
across their files
Same model, same quant label, different bytes
largest disagreements first
| Model | Quant | sumeshi | vs | Theirs | Difference |
|---|---|---|---|---|---|
| privacy-filter-multilingual | F16 | 2.62 GiB | LocalAI-io | 2.62 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
| Model | Quantizations | Smallest |
|---|---|---|
| privacy-filter-multilingual | 1 | 2.62 GiB |