| | --- |
| | library_name: vllm |
| | language: |
| | - en |
| | - fr |
| | - es |
| | - de |
| | - it |
| | - pt |
| | - nl |
| | - zh |
| | - ja |
| | - ko |
| | - ar |
| | license: apache-2.0 |
| | inference: false |
| | base_model: |
| | - mistralai/Ministral-3-8B-Instruct-2512 |
| | extra_gated_description: >- |
| | If you want to learn more about how we process your personal data, please read |
| | our <a href="https://mistral.ai/terms/">Privacy Policy</a>. |
| | tags: |
| | - mistral-common |
| | --- |
| | |
| | # Ministral 3 8B Instruct 2512 GGUF |
| |
|
| | A balanced model in the Ministral 3 family, **Ministral 3 8B** is a powerful, efficient tiny language model with vision capabilities. |
| |
|
| | This model includes different quantization levels of the instruct post-trained version in **GGUF**, 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 12GB of VRAM in FP8, and less if further quantized. |
| |
|
| | Learn more in our [blog post](https://mistral.ai/news/mistral-3) and [paper](https://arxiv.org/abs/2601.08584). |
| |
|
| | ## 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. |
| |
|
| | ### Recommended Settings |
| |
|
| | We recommend deploying with the following best practices: |
| | - System Prompt: Define a clear environment and use case, including guidance on how to effectively leverage tools in agentic systems. |
| | - Sampling Parameters: Use a **temperature below 0.1** for daily-driver and production environments ; Higher temperatures may be explored for creative use cases - developers are encouraged to experiment with alternative settings. |
| | - 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. |
| | - 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. |
| |
|
| | ## License |
| |
|
| | This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.txt). |
| |
|
| | *You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.* |
