Quantization publisher

PeterAM4

PeterAM4 publishes 7 quantizations across 1 models in our index, averaging 7.889 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
7
Models covered
1
Avg effective bpw
7.889
across their files

Same model, same quant label, different bytes

largest disagreements first
ModelQuantPeterAM4vsTheirsDifference
Qwen3-Embedding-0.6BQ5_K_M0.41 GiBmradermacher0.41 GiB-0.0%
Qwen3-Embedding-0.6BQ5_K_S0.41 GiBmradermacher0.41 GiB-0.0%
Qwen3-Embedding-0.6BQ6_K0.46 GiBmradermacher0.46 GiB-0.0%
Qwen3-Embedding-0.6BQ8_00.60 GiBmradermacher0.60 GiB-0.0%
Qwen3-Embedding-0.6BQ8_00.60 GiBxthor0.60 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
Qwen3-Embedding-0.6B70.41 GiB