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--- |
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library_name: vllm |
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language: |
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- en |
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- fr |
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- es |
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- de |
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- it |
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- pt |
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- nl |
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- zh |
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- ja |
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- ko |
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- ar |
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license: apache-2.0 |
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inference: false |
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base_model: |
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- mistralai/Ministral-3-3B-Reasoning-2512 |
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extra_gated_description: >- |
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If you want to learn more about how we process your personal data, please read |
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our <a href="https://mistral.ai/terms/">Privacy Policy</a>. |
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tags: |
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- mistral-common |
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--- |
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# Ministral 3 3B Reasoning 2512 GGUF |
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The smallest model in the Ministral 3 family, **Ministral 3 3B** is a powerful, efficient tiny language model with vision capabilities. |
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This model includes different quantization levels of the reasoning post-trained version in **GGUF**, trained for reasoning tasks, making it ideal for math, coding and stem related use cases. |
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The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 3B can even be deployed locally, fitting in 16GB of VRAM in BF16, and less than 8GB of RAM/VRAM when quantized. |
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## Key Features |
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Ministral 3 3B consists of two main architectural components: |
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- **3.4B Language Model** |
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- **0.4B Vision Encoder** |
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The Ministral 3 3B Reasoning model offers the following capabilities: |
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- **Vision**: Enables the model to analyze images and provide insights based on visual content, in addition to text. |
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- **Multilingual**: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic. |
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- **System Prompt**: Maintains strong adherence and support for system prompts. |
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- **Agentic**: Offers best-in-class agentic capabilities with native function calling and JSON outputting. |
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- **Reasoning**: Excels at complex, multi-step reasoning and dynamic problem-solving. |
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- **Edge-Optimized**: Delivers best-in-class performance at a small scale, deployable anywhere. |
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- **Apache 2.0 License**: Open-source license allowing usage and modification for both commercial and non-commercial purposes. |
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- **Large Context Window**: Supports a 256k context window. |
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### Recommended Settings |
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We recommend deploying with the following best practices: |
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- System Prompt: Use our provided [system prompt](https://huggingface.co/mistralai/Ministral-3-3B-Reasoning-2512/blob/main/SYSTEM_PROMPT.txt), and append it to your custom system prompt to define a clear environment and use case, including guidance on how to effectively leverage tools in agentic systems. |
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- Multi-turn Traces: We highly recommend keeping the reasoning traces in context. |
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- Sampling Parameters: Use a **temperature of 0.7** for most environments ; Different temperatures may be explored for different use cases - developers are encouraged to experiment with alternative settings. |
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- Tools: Keep the set of tools well-defined and limit their number to the minimum required for the use case - Avoiding overloading the model with an excessive number of tools. |
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- Vision: When deploying with vision capabilities, we recommend maintaining an aspect ratio close to 1:1 (width-to-height) for images. Avoiding the use of overly thin or wide images - crop them as needed to ensure optimal performance. |
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## License |
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This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.txt). |
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*You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.* |
