Quantization publisher

TheBloke

TheBloke publishes 1,241 quantizations across 107 models in our index, averaging 5.032 effective bits per weight. Their files differ in size from other publishers' builds of the same nominal quantization on 25 of the pairs we can compare — the same label does not mean the same file.

From the file· summed file bytes
Repositories
109
Quantizations
1,241
Models covered
107
Avg effective bpw
5.032
across their files

Same model, same quant label, different bytes

largest disagreements first
ModelQuantTheBlokevsTheirsDifference
Mistral-7B-v0.1Q8_07.17 GiBbartowski10.62 GiB-32.5%
deepseek-llm-67b-chatQ2_K26.54 GiBmradermacher23.40 GiB+13.4%
Mistral-7B-v0.1Q6_K5.53 GiBbartowski8.20 GiB-32.5%
Mistral-7B-v0.1Q5_K_M4.78 GiBbartowski7.08 GiB-32.5%
Mistral-7B-v0.1Q5_04.65 GiBbartowski6.89 GiB-32.4%
Mistral-7B-v0.1Q5_K_S4.65 GiBbartowski6.89 GiB-32.4%
Mixtral-8x7B-Instruct-v0.1Q3_K_M18.96 GiBmradermacher21.00 GiB-9.7%
dolphin-2.6-mixtral-8x7bQ3_K_M18.96 GiBmradermacher21.00 GiB-9.7%
Mixtral-8x7B-v0.1Q3_K_M18.96 GiBNousResearch21.00 GiB-9.7%
Mixtral-8x7B-Instruct-v0.1Q3_K_M18.96 GiBbartowski21.00 GiB-9.7%
dolphin-2.5-mixtral-8x7bQ3_K_M18.96 GiBMaziyarPanahi21.00 GiB-9.7%
Mixtral-8x7B-v0.1Q3_K_M18.96 GiBbartowski21.00 GiB-9.7%
Mistral-7B-v0.1Q4_K_M4.07 GiBbartowski6.02 GiB-32.4%
Mixtral-8x7B-Instruct-v0.1Q4_K_M24.63 GiBmradermacher26.49 GiB-7.1%
dolphin-2.6-mixtral-8x7bQ4_K_M24.63 GiBmradermacher26.49 GiB-7.1%
Mixtral-8x7B-v0.1Q4_K_M24.63 GiBNousResearch26.49 GiB-7.0%
Mixtral-8x7B-Instruct-v0.1Q4_K_M24.63 GiBbartowski26.49 GiB-7.0%
dolphin-2.5-mixtral-8x7bQ4_K_M24.63 GiBMaziyarPanahi26.49 GiB-7.0%
Mixtral-8x7B-v0.1Q4_K_M24.63 GiBbartowski26.49 GiB-7.0%
Mistral-7B-v0.1Q4_K_S3.86 GiBbartowski5.70 GiB-32.3%
Mistral-7B-v0.1Q4_03.83 GiBbartowski5.66 GiB-32.3%
Mistral-7B-v0.1Q3_K_L3.56 GiBbartowski5.26 GiB-32.4%
Capybara-Tess-Yi-34B-200KQ2_K13.56 GiBmradermacher11.94 GiB+13.5%
WizardCoder-Python-34B-V1.0Q2_K13.23 GiBmradermacher11.65 GiB+13.6%
Mistral-7B-v0.1Q3_K_M3.28 GiBbartowski4.84 GiB-32.3%

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