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mradermacher/mistralai_Ministral-3-8B-Instruct-2512-abliterated-GGUF overview

About < quantize version: 2 < output tensor quantised: 1 < convert type: hf < vocab type: < tags: < quants: x f16 Q4 K S Q2 K Q8 0 Q6 K Q3 K M Q3 K S Q3 K L Q4…

transformersggufenbase_model:Nitral-Archive/mistralai_Ministral-3-8B-Instruct-2512-bf16-abliteratedbase_model:quantized:Nitral-Archive/mistralai_Ministral-3-8B-Instruct-2512-bf16-abliteratedendpoints_compatibleregion:usconversational

Runs locally from ~3.12 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
622
Likes
3
Pipeline
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Repository Files & Downloads

12 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
mistralai_Ministral-3-8B-Instruct-2512-abliterated.IQ4_XS.ggufGGUFGGUF4.41 GBDownload
mistralai_Ministral-3-8B-Instruct-2512-abliterated.Q2_K.ggufGGUFGGUF3.12 GBDownload
mistralai_Ministral-3-8B-Instruct-2512-abliterated.Q3_K_L.ggufGGUFGGUF4.25 GBDownload
mistralai_Ministral-3-8B-Instruct-2512-abliterated.Q3_K_M.ggufGGUFGGUF3.95 GBDownload
mistralai_Ministral-3-8B-Instruct-2512-abliterated.Q3_K_S.ggufGGUFGGUF3.60 GBDownload
mistralai_Ministral-3-8B-Instruct-2512-abliterated.Q4_K_M.ggufGGUFGGUF4.84 GBDownload
mistralai_Ministral-3-8B-Instruct-2512-abliterated.Q4_K_S.ggufGGUFGGUF4.61 GBDownload
mistralai_Ministral-3-8B-Instruct-2512-abliterated.Q5_K_M.ggufGGUFGGUF5.64 GBDownload
mistralai_Ministral-3-8B-Instruct-2512-abliterated.Q5_K_S.ggufGGUFGGUF5.51 GBDownload
mistralai_Ministral-3-8B-Instruct-2512-abliterated.Q6_K.ggufGGUFGGUF6.49 GBDownload
mistralai_Ministral-3-8B-Instruct-2512-abliterated.Q8_0.ggufGGUFGGUF8.41 GBDownload
mistralai_Ministral-3-8B-Instruct-2512-abliterated.f16.ggufGGUFGGUF15.82 GBDownload

Model Details

Model IDmradermacher/mistralai_Ministral-3-8B-Instruct-2512-abliterated-GGUF
Authormradermacher
Pipeline—
License—
Base modelNitral-Archive/mistralai_Ministral-3-8B-Instruct-2512-bf16-abliterated
Last modified2026-10-19T01:34:57.000Z

Model README

---

base_model: Nitral-Archive/mistralai_Ministral-3-8B-Instruct-2512-bf16-abliterated

language:

  • en

library_name: transformers

mradermacher:

readme_rev: 1

quantized_by: mradermacher

---

About

<!-- ### quantize_version: 2 -->

<!-- ### output_tensor_quantised: 1 -->

<!-- ### convert_type: hf -->

<!-- ### vocab_type: -->

<!-- ### tags: -->

<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->

<!-- ### quants_skip: -->

<!-- ### skip_mmproj: 1 -->

static quants of https://huggingface.co/Nitral-Archive/mistralai_Ministral-3-8B-Instruct-2512-bf16-abliterated

<!-- provided-files -->

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/mistralai_Ministral-3-8B-Instruct-2512-abliterated-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's

READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for

more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |

|:-----|:-----|--------:|:------|

| GGUF | Q2_K | 3.5 | |

| GGUF | Q3_K_S | 4.0 | |

| GGUF | Q3_K_M | 4.3 | lower quality |

| GGUF | Q3_K_L | 4.7 | |

| GGUF | IQ4_XS | 4.8 | |

| GGUF | Q4_K_S | 5.1 | fast, recommended |

| GGUF | Q4_K_M | 5.3 | fast, recommended |

| GGUF | Q5_K_S | 6.0 | |

| GGUF | Q5_K_M | 6.2 | |

| GGUF | Q6_K | 7.1 | very good quality |

| GGUF | Q8_0 | 9.1 | fast, best quality |

| GGUF | f16 | 17.1 | 16 bpw, overkill |

Here is a handy graph by ikawrakow comparing some lower-quality quant

types (lower is better):

!image.png

And here are Artefact2's thoughts on the matter:

https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to

questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting

me use its servers and providing upgrades to my workstation to enable

this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

<!-- end -->

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