Quantization publisher
batiai
batiai publishes 4 quantizations across 2 models in our index, averaging 7.275 effective bits per weight. Their files differ in size from other publishers' builds of the same nominal quantization on 6 of the pairs we can compare — the same label does not mean the same file.
From the file· summed file bytes
Repositories
2
Quantizations
4
Models covered
2
Avg effective bpw
7.275
across their files
Same model, same quant label, different bytes
largest disagreements first
| Model | Quant | batiai | vs | Theirs | Difference |
|---|---|---|---|---|---|
| Qwen3-Embedding-4B | Q8_0 | 3.99 GiB | cstr | 3.99 GiB | 0.0% |
| Qwen3-VL-Embedding-8B | Q6_K | 5.79 GiB | VesNFF | 5.79 GiB | 0.0% |
| Qwen3-VL-Embedding-8B | Q6_K | 5.79 GiB | mradermacher | 5.79 GiB | -0.0% |
| Qwen3-VL-Embedding-8B | Q8_0 | 7.50 GiB | mradermacher | 7.50 GiB | -0.0% |
| Qwen3-Embedding-4B | Q6_K | 3.08 GiB | mradermacher | 3.08 GiB | -0.0% |
| Qwen3-Embedding-4B | Q8_0 | 3.99 GiB | mradermacher | 3.99 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 |
|---|---|---|
| Qwen3-VL-Embedding-8B | 2 | 5.79 GiB |
| Qwen3-Embedding-4B | 2 | 3.08 GiB |