This is a
Ministral-3-8B-Instruct-2512 fine-tune, produced through P-E-W's
Heretic (v1.2.0) abliteration engine with
Magnitude-Preserving Orthogonal Ablation enabled.
Note: Results from previous attempts:
Click Here Heretication Results
Score Metric Value Parameter Value Refusals 8/100 direction_index per layer KL Divergence 0.0509 attn.o_proj.max_weight 1.97 Initial Refusals 91/100 attn.o_proj.max_weight_position 17.48 attn.o_proj.min_weight 1.90 attn.o_proj.min_weight_distance 10.79 mlp.down_proj.max_weight 0.19 mlp.down_proj.max_weight_position 8.56 mlp.down_proj.min_weight 0.04 mlp.down_proj.min_weight_distance 15.62
Appendix
PaCMAP projection » [Trial 407] Refusals: 8/100, KL divergence: 0.0509
[Trial 318] Refusals: 11/100, KL divergence: 0.0314
[Trial 253] Refusals: 14/100, KL divergence: 0.0278
[Trial 216] Refusals: 15/100, KL divergence: 0.0276
[Trial 401] Refusals: 19/100, KL divergence: 0.0255
[Trial 405] Refusals: 21/100, KL divergence: 0.0240
[Trial 149] Refusals: 31/100, KL divergence: 0.0232
[Trial 249] Refusals: 33/100, KL divergence: 0.0221
[Trial 244] Refusals: 38/100, KL divergence: 0.0214
[Trial 230] Refusals: 44/100, KL divergence: 0.0207
[Trial 153] Refusals: 46/100, KL divergence: 0.0198
[Trial 347] Refusals: 52/100, KL divergence: 0.0175
[Trial 154] Refusals: 62/100, KL divergence: 0.0160
[Trial 138] Refusals: 64/100, KL divergence: 0.0154
[Trial 392] Refusals: 65/100, KL divergence: 0.0134
[Trial 480] Refusals: 66/100, KL divergence: 0.0120
[Trial 29] Refusals: 73/100, KL divergence: 0.0113
[Trial 240] Refusals: 74/100, KL divergence: 0.0109
[Trial 612] Refusals: 75/100, KL divergence: 0.0102
[Trial 255] Refusals: 77/100, KL divergence: 0.0073
[Trial 378] Refusals: 79/100, KL divergence: 0.0059
[Trial 605] Refusals: 81/100, KL divergence: 0.0046
[Trial 1] Refusals: 82/100, KL divergence: 0.0042
[Trial 443] Refusals: 83/100, KL divergence: 0.0040
[Trial 486] Refusals: 84/100, KL divergence: 0.0038
[Trial 450] Refusals: 85/100, KL divergence: 0.0026
[Trial 343] Refusals: 86/100, KL divergence: 0.0022
[Trial 14] Refusals: 87/100, KL divergence: 0.0009
[Trial 336] Refusals: 88/100, KL divergence: 0.0008
[Trial 274] Refusals: 89/100, KL divergence: 0.0005
[Trial 418] Refusals: 90/100, KL divergence: 0.0004
[Trial 688] Refusals: 91/100, KL divergence: 0.0000Ministral 3 8B Instruct 2512 BF16
A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
This model is the instruct post-trained version, fine-tuned for instruction tasks, making it ideal for chat and instruction based use cases.
The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 8B can even be deployed locally, capable of fitting in 24GB of VRAM in BF16, and less than 12GB of RAM/VRAM when quantized.
We provide a no-loss FP8 version
here , you can find other formats and quantizations in the
Ministral 3 - Additional Checkpoints collection.
Learn more in our
blog post and
paper .
Key Features
Ministral 3 8B consists of two main architectural components:
8.4B Language Model 0.4B Vision Encoder
The Ministral 3 8B Instruct model offers the following capabilities:
Vision : Enables the model to analyze images and provide insights based on visual content, in addition to text.Multilingual : Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.System Prompt : Maintains strong adherence and support for system prompts.Agentic : Offers best-in-class agentic capabilities with native function calling and JSON outputting.Edge-Optimized : Delivers best-in-class performance at a small scale, deployable anywhere.Apache 2.0 License : Open-source license allowing usage and modification for both commercial and non-commercial purposes.Large Context Window : Supports a 256k context window.
Use Cases
Perfect for balanced performance in local or embedded systems, combining versatility with efficiency.
Chat interfaces in constrained environments
Local daily-driver AI assistant
Image/document description and understanding
Translation and content generation
Specialized agentic use cases
Fine-tuning and specialization
And more...
Bringing advanced AI capabilities to resource-constrained environments.
Ministral 3 Family
Model Name Type Precision Link Ministral 3 3B Base 2512 Base pre-trained BF16 Hugging Face Ministral 3 3B Instruct 2512 Instruct post-trained BF16 Hugging Face Ministral 3 3B Reasoning 2512 Reasoning capable BF16 Hugging Face Ministral 3 8B Base 2512 Base pre-trained BF16 Hugging Face Ministral 3 8B Instruct 2512 Instruct post-trained BF16 Hugging Face Ministral 3 8B Reasoning 2512 Reasoning capable BF16 Hugging Face Ministral 3 14B Base 2512 Base pre-trained BF16 Hugging Face Ministral 3 14B Instruct 2512 Instruct post-trained BF16 Hugging Face Ministral 3 14B Reasoning 2512 Reasoning capable BF16 Hugging Face
Other formats available
here .
Benchmark Results
We compare Ministral 3 to similar sized models.
Reasoning
Model AIME25 AIME24 GPQA Diamond LiveCodeBench Ministral 3 14B 0.850 0.898 0.712 0.646 Qwen3-14B (Thinking) 0.737 0.837 0.663 0.593 Ministral 3 8B 0.787 0.860 0.668 0.616 Qwen3-VL-8B-Thinking 0.798 0.860 0.671 0.580 Ministral 3 3B 0.721 0.775 0.534 0.548 Qwen3-VL-4B-Thinking 0.697 0.729 0.601 0.513
Instruct
Model Arena Hard WildBench MATH Maj@1 MM MTBench Ministral 3 14B 0.551 68.5 0.904 8.49 Qwen3 14B (Non-Thinking) 0.427 65.1 0.870 NOT MULTIMODAL Gemma3-12B-Instruct 0.436 63.2 0.854 6.70 Ministral 3 8B 0.509 66.8 0.876 8.08 Qwen3-VL-8B-Instruct 0.528 66.3 0.946 8.00 Ministral 3 3B 0.305 56.8 0.830 7.83 Qwen3-VL-4B-Instruct 0.438 56.8 0.900 8.01 Qwen3-VL-2B-Instruct 0.163 42.2 0.786 6.36 Gemma3-4B-Instruct 0.318 49.1 0.759 5.23
Base
Model Multilingual MMLU MATH CoT 2-Shot AGIEval 5-shot MMLU Redux 5-shot MMLU 5-shot TriviaQA 5-shot Ministral 3 14B 0.742 0.676 0.648 0.820 0.794 0.749 Qwen3 14B Base 0.754 0.620 0.661 0.837 0.804 0.703 Gemma 3 12B Base 0.690 0.487 0.587 0.766 0.745 0.788 Ministral 3 8B 0.706 0.626 0.591 0.793 0.761 0.681 Qwen 3 8B Base 0.700 0.576 0.596 0.794 0.760 0.639 Ministral 3 3B 0.652 0.601 0.511 0.735 0.707 0.592 Qwen 3 4B Base 0.677 0.405 0.570 0.759 0.713 0.530 Gemma 3 4B Base 0.516 0.294 0.430 0.626 0.589 0.640
License
This model is licensed under the
Apache 2.0 License .
You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.
