Quantization publisher
inclusionAI
inclusionAI publishes 5 quantizations across 1 models in our index, averaging 7.795 effective bits per weight. Their files differ in size from other publishers' builds of the same nominal quantization on 5 of the pairs we can compare — the same label does not mean the same file.
From the file· summed file bytes
Repositories
1
Quantizations
5
Models covered
1
Avg effective bpw
7.795
across their files
Same model, same quant label, different bytes
largest disagreements first
| Model | Quant | inclusionAI | vs | Theirs | Difference |
|---|---|---|---|---|---|
| Ling-mini-2.0 | Q2_K | 5.65 GiB | bartowski | 5.44 GiB | +3.8% |
| Ling-mini-2.0 | Q4_K_M | 9.23 GiB | bartowski | 9.26 GiB | -0.3% |
| Ling-mini-2.0 | Q6_K | 12.45 GiB | bartowski | 12.47 GiB | -0.1% |
| Ling-mini-2.0 | Q8_0 | 16.12 GiB | bartowski | 16.12 GiB | -0.0% |
| Ling-mini-2.0 | BF16 | 30.30 GiB | bartowski | 30.30 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 |
|---|---|---|
| Ling-mini-2.0 | 5 | 5.65 GiB |