Quantization publisher
renz7
renz7 publishes 3 quantizations across 1 models in our index, averaging 8.509 effective bits per weight. Their files differ in size from other publishers' builds of the same nominal quantization on 3 of the pairs we can compare — the same label does not mean the same file.
From the file· summed file bytes
Repositories
1
Quantizations
3
Models covered
1
Avg effective bpw
8.509
across their files
Same model, same quant label, different bytes
largest disagreements first
| Model | Quant | renz7 | vs | Theirs | Difference |
|---|---|---|---|---|---|
| ChineseErrorCorrector4-4B | F16 | 7.50 GiB | mradermacher | 7.50 GiB | -0.0% |
| ChineseErrorCorrector4-4B | IQ4_XS | 2.13 GiB | mradermacher | 2.13 GiB | -0.0% |
| ChineseErrorCorrector4-4B | Q4_K_M | 2.33 GiB | mradermacher | 2.33 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 |
|---|---|---|
| ChineseErrorCorrector4-4B | 3 | 2.13 GiB |