---

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
• llama.cpp
• image-text-to-text
• imatrix
• heretic
• mistral
• reasoning
• uncensored
• multimodal
• vision
• abliterated
• conversational
• iq4_nl
• q4_k_m
• q5_k_m

quantized_by: FadedRedStar

---

🤖 Ministral-3-8B-Reasoning-2512-heretic — Importance Matrix GGUF

This repository hosts importance-matrix (imatrix) optimized GGUF weights, available in multiple quantization formats, 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.

🔄 Sister Repository: Check out the Standard GGUF Sister Repository for uncalibrated and full 8-bit precision options.

🎯 Matrix-Weighted Calibration (Imatrix)

An Importance Matrix (imatrix) calculation tracks activations across network layers using a calibration sequence, then weights the quantization process to preserve the parameters that matter most for output quality — improving fidelity at low bit depths.

➡️ Calibration dataset: Bartowski's calibration_datav5.txt.

> [!NOTE]

> * IQ4_NL is included because the matrix enables a non-linear 4-bit format that outperforms standard linear 4-bit quantization.

> * Q8_0 is absent because 8-bit quantization already introduces near-zero degradation, making calibration unnecessary — see the standard sister repository for that variant.

ℹ️ Model Profile & Core Features

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.

📋 Technical 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 |

🛠️ Heretic Overrides (ARA)

| Property | 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 |

📊 Refusal Bypass Metrics

> [!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 |

🧮 Numerical & Tensor Formats

| Property | Value |

|---|---|

| Text Tensor Types | IQ4_NL, Q4_K_M, Q5_K_M (all with imatrix calibration) |

| Importance Matrix | Bartowski's calibration_datav5.txt |

| Vision Tensors | Q8_0, BF16 |

📦 Available Model Files

Main model weights

| Filename | Quantization | llama.cpp Build | Size | Download |

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

| Ministral-3-8B-Reasoning-2512-heretic-IQ4_NL-imatrix.gguf | IQ4_NL | b9843 | 4.60 GB | 📥 Download |

| Ministral-3-8B-Reasoning-2512-heretic-Q4_K_M-imatrix.gguf | Q4_K_M | b9803 | 4.84 GB | 📥 Download |

| Ministral-3-8B-Reasoning-2512-heretic-Q5_K_M-imatrix.gguf | Q5_K_M | b9870 | 5.64 GB | 📥 Download |

mmproj — vision projector files

| Filename | Quantization | Size | Download |

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

| mmproj-Ministral-3-8B-Reasoning-2512-heretic-Q8_0.gguf | Q8_0 | 448 MB | 📥 Download |

| mmproj-Ministral-3-8B-Reasoning-2512-heretic-BF16.gguf | BF16 | 827 MB | 📥 Download |

🎛️ Component Pairing Guide

Download exactly one main weights file:

• IQ4_NL: Non-linear 4-bit format, best choice for constrained memory when imatrix calibration is present.
• Q4_K_M: Balanced 4-bit format suitable for most everyday use.
• Q5_K_M: Higher-fidelity mid-range format recommended as a general default.

mmproj files (optional): multimodal vision projectors. Pass one via the --mmproj flag in llama.cpp to enable image input.

• BF16 (Recommended): Highest possible image processing accuracy. While older projectors were small, modern vision towers can hover around 1GB. If you are tight on VRAM, it is completely viable to run this on system RAM (CPU) with a minimal performance penalty, saving your precious GPU space for the main model layers.
• Q8_0: Cuts the projector file size and memory footprint in half (~500MB for larger 1GB files). Use this if you prefer to keep the vision tower hosted entirely on your GPU but need to claw back some VRAM to avoid Out-Of-Memory (OOM) crashes.

⚡ Deployment & Execution Commands

> [!IMPORTANT]

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

> [!NOTE]

> Mistral AI recommends the following sampling configuration for best results: temperature=0.7, top_p=0.95.

> [!TIP]

> Swap the -m filename below for either quantized file depending on your size/quality trade-off preference.

llama.cpp CLI (with image)

./llama-cli \
  -m Ministral-3-8B-Reasoning-2512-heretic-IQ4_NL-imatrix.gguf \
  --mmproj mmproj-Ministral-3-8B-Reasoning-2512-heretic-Q8_0.gguf \
  -c 8192 \
  -ngl 99 \
  --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-IQ4_NL-imatrix.gguf \
  --mmproj mmproj-Ministral-3-8B-Reasoning-2512-heretic-Q8_0.gguf \
  -c 16384 \
  -ngl 99 \
  --flash-attn

---

💬 Chat Templates & Prompt Design (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

---

⚠️ Safety & Operational Notes

• 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 or Q8_0 depending on the mmproj variant chosen, to prevent degradation of spatial and symbolic features in diagrams and formulas.
• Imatrix calibration improves perplexity recovery compared to non-imatrix quantization, particularly on low-frequency tokens.
• IQ4_NL produces a smaller file than Q4_K_M and tends to run faster on CPU and ARM devices; imatrix calibration narrows the quality gap between the two formats considerably.