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FadedRedStar/Ministral-3-8B-Reasoning-2512-heretic-GGUF overview

Ministral 3 8B Reasoning 2512 heretic — GGUF This repository hosts GGUF weights and the associated vision projection matrix for Ministral 3 8B Reasoning 2512 h…

gguftext-generationmultimodalvisionreasoningmathstemagentictool-useabliterateduncensoredmistralq4_k_mconversationalimage-text-to-textenfresdeitptnlzhja

Runs locally from ~447.8 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
456
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Pipeline
image-text-to-text

Repository Files & Downloads

5 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Ministral-3-8B-Reasoning-2512-heretic-Q4_K_M.ggufGGUFQ4_K_M4.84 GBDownload
Ministral-3-8B-Reasoning-2512-heretic-Q5_K_M.ggufGGUFQ5_K_M5.64 GBDownload
Ministral-3-8B-Reasoning-2512-heretic-Q8_0.ggufGGUFQ8_08.41 GBDownload
mmproj-Ministral-3-8B-Reasoning-2512-heretic-BF16.ggufGGUFBF16826.5 MBDownload
mmproj-Ministral-3-8B-Reasoning-2512-heretic-Q8_0.ggufGGUFQ8_0447.8 MBDownload

Model Details

Model IDFadedRedStar/Ministral-3-8B-Reasoning-2512-heretic-GGUF
AuthorFadedRedStar
Pipelineimage-text-to-text
Licenseapache-2.0
Base modelcoder3101/Ministral-3-8B-Reasoning-2512-heretic
Last modified2026-07-04T22:08:55.000Z

Model README

---

base_model: coder3101/Ministral-3-8B-Reasoning-2512-heretic

base_model_relation: quantized

library_name: gguf

license: apache-2.0

language:

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

pipeline_tag: image-text-to-text

tags:

  • gguf
  • text-generation
  • multimodal
  • vision
  • reasoning
  • math
  • stem
  • agentic
  • tool-use
  • abliterated
  • uncensored
  • mistral
  • q4_k_m
  • conversational

---

Ministral-3-8B-Reasoning-2512-heretic — GGUF

This repository hosts GGUF weights and the associated vision projection matrix for Ministral-3-8B-Reasoning-2512-heretic, quantized from the source floating-point tensors provided by coder3101/Ministral-3-8B-Reasoning-2512-heretic.

About the Model

Ministral-3-8B-Reasoning-2512 is a vision-language model by Mistral AI from the Ministral 3 family, built for edge deployment. It combines an 8.4B language model with a 0.4B vision encoder for multimodal understanding, and is the reasoning post-trained variant — specifically optimised for math, coding, STEM, and complex multi-step reasoning. The model supports multilingual input, native function calling, JSON output, and best-in-class agentic capabilities at its scale.

The heretic suffix denotes post-processing via the Heretic v1.1.0 abliteration framework performed by coder3101, using the direction_index method to suppress refusal vectors while preserving reasoning chains and multimodal capabilities.

---

Model & Architecture Specifications

| Property | Value |

|---|---|

| Base Architecture | Ministral-3 (8.4B LM + 0.4B Vision Encoder) |

| Developed by | Mistral AI |

| Primary Use | Reasoning, math, STEM, vision, agentic tasks |

| Context Window | 262,144 tokens |

| Vision Encoder | 0.4B (integrated, early-fusion) |

| Languages | English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic |

| Abliteration Tool | Heretic v1.1.0 |

| Abliteration Method | direction_index (single-direction refusal suppression) |

| Prompt Format | ChatML |

---

Abliteration Parameters

| Parameter | Value |

|---|---|

| direction_index | 15.03 |

| attn.o_proj.max_weight | 1.17 |

| attn.o_proj.max_weight_position | 20.88 |

| attn.o_proj.min_weight | 0.55 |

| attn.o_proj.min_weight_distance | 2.72 |

| mlp.down_proj.max_weight | 1.49 |

| mlp.down_proj.max_weight_position | 23.45 |

| mlp.down_proj.min_weight | 1.40 |

| mlp.down_proj.min_weight_distance | 18.55 |

Abliteration Performance

> [!NOTE]

> The metrics below are self-reported by the original model author (coder3101) and have not been independently reproduced.

| Metric | This model | Original (mistralai/Ministral-3-8B-Reasoning-2512) |

|---|---|---|

| KL divergence | 0.0712 | 0 (by definition) |

| Refusals | 4/100 | 96/100 |

---

Quantization Details

| Property | Value |

|---|---|

| Text Tensors | Q4_K_M |

| Vision Tensors | BF16 (unquantized, preserves visual feature quality) |

Quantization Command

./llama-quantize /content/model-bf16.gguf \
  /content/Ministral-3-8B-Reasoning-2512-heretic-Q4_K_M.gguf q4_k_m

---

Repository Files

| Filename | Format | Size | llama.cpp Build | Description |

|---|---|---|---|---|

| Ministral-3-8B-Reasoning-2512-heretic-Q4_K_M.gguf | Q4_K_M | 5.20 GB | b9803 | Main model weights |

| mmproj-Ministral-3-8B-Reasoning-2512-heretic-BF16.gguf | BF16 | 867 MB | b9803 | Vision projector — required for all image inputs |

---

Inference

> [!IMPORTANT]

> The vision projector (--mmproj) must be supplied at runtime whenever image inputs are used. Omitting it disables multimodal capability entirely.

> [!NOTE]

> Mistral AI's recommended sampling temperature for this model is 0.7.

llama.cpp CLI (with image)

./llama-cli \
  -m Ministral-3-8B-Reasoning-2512-heretic-Q4_K_M.gguf \
  --mmproj mmproj-Ministral-3-8B-Reasoning-2512-heretic-BF16.gguf \
  -c 8192 \
  -ngl 99 \
  --temp 0.7 \
  --image "path/to/image.jpg" \
  -p "<|im_start|>user\nSolve the problem shown in the image step by step.<|im_end|>\n<|im_start|>assistant\n"

OpenAI-Compatible API Server

./llama-server \
  --host 0.0.0.0 \
  --port 8080 \
  -m Ministral-3-8B-Reasoning-2512-heretic-Q4_K_M.gguf \
  --mmproj mmproj-Ministral-3-8B-Reasoning-2512-heretic-BF16.gguf \
  -c 16384 \
  -ngl 99 \
  --flash-attn

---

Prompt Format (ChatML)

<|im_start|>system
You are an expert reasoning assistant. Think step by step.<|im_end|>
<|im_start|>user
Your question or image payload here.<|im_end|>
<|im_start|>assistant

---

Notes & Limitations

  • This model is abliterated and will generate content that standard aligned models refuse. Use responsibly and in compliance with applicable laws.
  • Designed for edge deployment; fits in 24 GB VRAM at BF16 and under 12 GB when quantized.
  • Vision tensors are kept at BF16 to prevent degradation of spatial and symbolic features in diagrams and formulas.
  • For better output quality at this quantization level, consider the imatrix variant in the companion repository.

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