Quantization publisher
MaziyarPanahi
MaziyarPanahi publishes 553 quantizations across 50 models in our index, averaging 4.909 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
51
Quantizations
553
Models covered
50
Avg effective bpw
4.909
across their files
Same model, same quant label, different bytes
largest disagreements first
| Model | Quant | MaziyarPanahi | vs | Theirs | Difference |
|---|---|---|---|---|---|
| dolphin-2.5-mixtral-8x7b | Q3_K_M | 21.00 GiB | TheBloke | 18.96 GiB | +10.7% |
| dolphin-2.5-mixtral-8x7b | Q4_K_M | 26.49 GiB | TheBloke | 24.63 GiB | +7.6% |
| dolphin-2.5-mixtral-8x7b | Q2_K | 16.12 GiB | TheBloke | 14.57 GiB | +10.6% |
| Trinity-Mini | Q3_K_L | 12.66 GiB | bartowski | 11.65 GiB | +8.7% |
| Trinity-Mini | Q3_K_L | 12.66 GiB | arcee-ai | 11.65 GiB | +8.7% |
| dolphin-2.5-mixtral-8x7b | Q5_K_M | 30.95 GiB | TheBloke | 30.02 GiB | +3.1% |
| BioMistral-7B | Q3_K_L | 3.56 GiB | BioMistral | 2.74 GiB | +29.9% |
| WhiteRabbitNeo-13B-v1 | Q2_K | 4.52 GiB | TheBloke | 5.06 GiB | -10.6% |
| Trinity-Mini | Q3_K_M | 11.68 GiB | bartowski | 11.27 GiB | +3.7% |
| Trinity-Mini | Q3_K_M | 11.68 GiB | arcee-ai | 11.27 GiB | +3.7% |
| dolphin-2.6-mistral-7b-dpo-laser | Q2_K | 2.53 GiB | TheBloke | 2.87 GiB | -11.8% |
| OpenHermes-2.5-Mistral-7B | Q2_K | 2.53 GiB | second-state | 2.87 GiB | -11.8% |
| OpenHermes-2.5-Mistral-7B | Q2_K | 2.53 GiB | TheBloke | 2.87 GiB | -11.8% |
| gemma-2b | Q2_K | 1.08 GiB | brittlewis12 | 0.84 GiB | +28.7% |
| Trinity-Mini | Q2_K | 9.01 GiB | arcee-ai | 8.78 GiB | +2.6% |
| Trinity-Mini | Q2_K | 9.01 GiB | bartowski | 8.78 GiB | +2.6% |
| gemma-2b | Q3_K_M | 1.29 GiB | brittlewis12 | 1.10 GiB | +17.4% |
| gemma-2b | Q3_K_L | 1.36 GiB | brittlewis12 | 1.17 GiB | +16.2% |
| gemma-2b | Q3_K_S | 1.20 GiB | brittlewis12 | 1.01 GiB | +18.9% |
| gemma-2b | Q4_K_M | 1.52 GiB | brittlewis12 | 1.39 GiB | +9.0% |
| gemma-2b | Q4_K_S | 1.45 GiB | brittlewis12 | 1.33 GiB | +9.5% |
| Trinity-Mini | Q4_K_M | 14.74 GiB | bartowski | 14.84 GiB | -0.7% |
| Trinity-Mini | Q4_K_M | 14.74 GiB | arcee-ai | 14.84 GiB | -0.7% |
| Trinity-Mini | Q5_K_M | 17.28 GiB | arcee-ai | 17.36 GiB | -0.4% |
| Trinity-Mini | Q5_K_M | 17.28 GiB | bartowski | 17.36 GiB | -0.4% |
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 |
|---|---|---|
| Phi-3.5-mini-instruct | 15 | 0.78 GiB |
| gemma-2-2b-it | 15 | 0.78 GiB |
| Mistral-Nemo-Instruct-2407 | 10 | 4.46 GiB |
| Mistral-Small-24B-Instruct-2501 | 10 | 8.28 GiB |
| Mistral-7B-Instruct-v0.3 | 15 | 1.50 GiB |
| Meta-Llama-3-8B-Instruct | 15 | 1.88 GiB |
| Llama-3.1-70B-Instruct | 13 | 14.29 GiB |
| Qwen2-7B-Instruct | 15 | 1.77 GiB |
| Yi-Coder-9B-Chat | 13 | 1.88 GiB |
| Mistral-Small-Instruct-2409 | 30 | 4.50 GiB |
| Mixtral-8x22B-v0.1 | 14 | 27.61 GiB |
| Llama-3-8B-Instruct-32k-v0.1 | 15 | 1.88 GiB |
| Mathstral-7B-v0.1 | 15 | 1.50 GiB |
| Yi-Coder-1.5B-Chat | 13 | 0.46 GiB |
| mistral-small-3.1-24b-instruct-2503-hf | 10 | 8.28 GiB |
| INTELLECT-2 | 6 | 11.47 GiB |
| Llama-3-8B-Instruct-64k | 10 | 2.96 GiB |
| WizardLM-2-7B | 12 | 2.53 GiB |
| Mistral-Large-Instruct-2411 | 1 | 42.09 GiB |
| Yi-1.5-6B-Chat | 9 | 2.18 GiB |
| solar-pro-preview-instruct | 15 | 4.46 GiB |
| llama-3-firefunction-v2 | 15 | 14.29 GiB |
| Trinity-Mini | 6 | 9.01 GiB |
| gpt-oss-20b-Derestricted | 6 | 11.24 GiB |
| Nemotron-Orchestrator-8B | 6 | 3.06 GiB |
| NVIDIA-Nemotron-Nano-12B-v2 | 6 | 4.38 GiB |
| gemma-7b | 10 | 3.24 GiB |
| phi-2 | 10 | 1.03 GiB |
| dolphin-2.5-mixtral-8x7b | 10 | 16.12 GiB |
| SmolLM-135M-Instruct | 10 | 0.08 GiB |