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mradermacher/Ministral-3-14B-Instruct-2512-BF16-abliterated-i1-GGUF overview

About < quantize version: 2 < output tensor quantised: 1 < convert type: hf < vocab type: < tags: nicoboss < quants: Q2 K IQ3 M Q4 K S IQ3 XXS Q3 K M small IQ4…

transformersggufmistral-commonhereticuncensoreddecensoredabliteratedreproducibleenfresdeitptnlzhjakoarbase_model:s3nh/Ministral-3-14B-Instruct-2512-BF16-abliteratedbase_model:quantized:s3nh/Ministral-3-14B-Instruct-2512-BF16-abliteratedlicense:apache-2.0endpoints_compatibleregion:us

Runs locally from ~7.1 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

25 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ1_M.ggufGGUFBF163.26 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ1_S.ggufGGUFBF163.03 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ2_M.ggufGGUFBF164.51 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ2_S.ggufGGUFBF164.20 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ2_XS.ggufGGUFBF163.99 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ2_XXS.ggufGGUFBF163.65 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ3_M.ggufGGUFBF165.84 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ3_S.ggufGGUFBF165.68 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ3_XS.ggufGGUFBF165.42 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ3_XXS.ggufGGUFBF165.05 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ4_NL.ggufGGUFBF167.27 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-IQ4_XS.ggufGGUFBF166.90 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q2_K.ggufGGUFBF164.89 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q2_K_S.ggufGGUFBF164.58 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q3_K_L.ggufGGUFBF166.72 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q3_K_M.ggufGGUFBF166.22 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q3_K_S.ggufGGUFBF165.66 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q4_0.ggufGGUFBF167.27 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q4_1.ggufGGUFBF167.99 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q4_K_M.ggufGGUFBF167.67 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q4_K_S.ggufGGUFBF167.30 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q5_K_M.ggufGGUFBF168.96 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q5_K_S.ggufGGUFBF168.74 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.i1-Q6_K.ggufGGUFBF1610.33 GBDownload
Ministral-3-14B-Instruct-2512-BF16-abliterated.imatrix.ggufGGUFBF167.1 MBDownload

Model Details

Model IDmradermacher/Ministral-3-14B-Instruct-2512-BF16-abliterated-i1-GGUF
Authormradermacher
Pipeline
Licenseapache-2.0
Base models3nh/Ministral-3-14B-Instruct-2512-BF16-abliterated
Last modified2026-07-11T12:06:13.000Z

Model README

---

base_model: s3nh/Ministral-3-14B-Instruct-2512-BF16-abliterated

language:

  • en
  • fr
  • es
  • de
  • it
  • pt
  • nl
  • zh
  • ja
  • ko
  • ar

library_name: transformers

license: apache-2.0

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

  • mistral-common
  • heretic
  • uncensored
  • decensored
  • abliterated
  • reproducible

---

About

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

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

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

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

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

<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->

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

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

weighted/imatrix quants of https://huggingface.co/s3nh/Ministral-3-14B-Instruct-2512-BF16-abliterated

<!-- provided-files -->

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

static quants are available at https://huggingface.co/mradermacher/Ministral-3-14B-Instruct-2512-BF16-abliterated-GGUF

This is a vision model - mmproj files (if any) will be in the static repository.

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 | imatrix | 0.1 | imatrix file (for creating your own quants) |

| GGUF | i1-IQ1_S | 3.4 | for the desperate |

| GGUF | i1-IQ1_M | 3.6 | mostly desperate |

| GGUF | i1-IQ2_XXS | 4.0 | |

| GGUF | i1-IQ2_XS | 4.4 | |

| GGUF | i1-IQ2_S | 4.6 | |

| GGUF | i1-IQ2_M | 4.9 | |

| GGUF | i1-Q2_K_S | 5.0 | very low quality |

| GGUF | i1-Q2_K | 5.3 | IQ3_XXS probably better |

| GGUF | i1-IQ3_XXS | 5.5 | lower quality |

| GGUF | i1-IQ3_XS | 5.9 | |

| GGUF | i1-Q3_K_S | 6.2 | IQ3_XS probably better |

| GGUF | i1-IQ3_S | 6.2 | beats Q3_K* |

| GGUF | i1-IQ3_M | 6.4 | |

| GGUF | i1-Q3_K_M | 6.8 | IQ3_S probably better |

| GGUF | i1-Q3_K_L | 7.3 | IQ3_M probably better |

| GGUF | i1-IQ4_XS | 7.5 | |

| GGUF | i1-IQ4_NL | 7.9 | prefer IQ4_XS |

| GGUF | i1-Q4_0 | 7.9 | fast, low quality |

| GGUF | i1-Q4_K_S | 7.9 | optimal size/speed/quality |

| GGUF | i1-Q4_K_M | 8.3 | fast, recommended |

| GGUF | i1-Q4_1 | 8.7 | |

| GGUF | i1-Q5_K_S | 9.5 | |

| GGUF | i1-Q5_K_M | 9.7 | |

| GGUF | i1-Q6_K | 11.2 | practically like static Q6_K |

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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