Image-Text-to-Text
MLX
Safetensors
mistral3
mistral-common
ministral
ministral-3
vision-language
multimodal
quantized
edge
6-bit
base-model
Instructions to use mlx-community/Ministral-3-3B-Base-2512-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Ministral-3-3B-Base-2512-6bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("mlx-community/Ministral-3-3B-Base-2512-6bit") config = load_config("mlx-community/Ministral-3-3B-Base-2512-6bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Upload README.md with huggingface_hub
Browse files
README.md
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- Source: [`mistralai/Ministral-3-3B-Base-2512`](https://huggingface.co/mistralai/Ministral-3-3B-Base-2512) (BF16)
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- Language model layers: **6-bit** affine quantization, group_size=64
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- Vision tower + multimodal projector: kept at full precision (not quantized)
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- Blended average: **
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## Ministral 3 family
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- Source: [`mistralai/Ministral-3-3B-Base-2512`](https://huggingface.co/mistralai/Ministral-3-3B-Base-2512) (BF16)
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- Language model layers: **6-bit** affine quantization, group_size=64
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- Vision tower + multimodal projector: kept at full precision (not quantized)
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- Blended average: **7.537 bits per weight** across all parameters
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## Ministral 3 family
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