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-3-8B-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-3-8B-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-3-8B-Base-2512-6bit") config = load_config("mlx-community/Ministral-3-8B-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
- Xet hash:
- 1c7a0e4b95949bfea382b97ed32deb8422476e9aebb0977037a3e26ef104b751
- Size of remote file:
- 3.03 GB
- SHA256:
- a333cd2fda9c8eae8d57f7053065420397f67759dac0ca8251b4dec45311fc0b
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