Quantization publisher

Tesslate

Tesslate publishes 13 quantizations across 1 models in our index, averaging 5.754 effective bits per weight. Their files differ in size from other publishers' builds of the same nominal quantization on 12 of the pairs we can compare — the same label does not mean the same file.

From the file· summed file bytes
Repositories
1
Quantizations
13
Models covered
1
Avg effective bpw
5.754
across their files

Same model, same quant label, different bytes

largest disagreements first
ModelQuantTesslatevsTheirsDifference
OmniCoder-9BQ3_K_S3.97 GiBbartowski4.35 GiB-8.8%
OmniCoder-9BQ6_K6.85 GiBbartowski7.17 GiB-4.4%
OmniCoder-9BQ5_K_M6.08 GiBbartowski6.38 GiB-4.8%
OmniCoder-9BQ3_K_M4.31 GiBbartowski4.58 GiB-6.0%
OmniCoder-9BQ4_K_S4.98 GiBbartowski5.21 GiB-4.4%
OmniCoder-9BQ2_K3.56 GiBbartowski3.79 GiB-5.8%
OmniCoder-9BQ5_K_S5.87 GiBbartowski6.08 GiB-3.4%
OmniCoder-9BQ3_K_L4.59 GiBbartowski4.76 GiB-3.6%
OmniCoder-9BQ4_K_M5.34 GiBbartowski5.50 GiB-2.9%
OmniCoder-9BQ4_04.95 GiBbartowski5.11 GiB-3.1%
OmniCoder-9BQ8_08.87 GiBbartowski8.89 GiB-0.2%
OmniCoder-9BBF1616.69 GiBbartowski16.69 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
OmniCoder-9B133.56 GiB