Image-Text-to-Text
MLX
Safetensors
English
cohere_compass
mlx-vlm
vision-language
multimodal
cohere
north
quantized
conversational
4-bit precision
Instructions to use mlx-community/North-Micro-Vision-Instruct-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/North-Micro-Vision-Instruct-4bit 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/North-Micro-Vision-Instruct-4bit") config = load_config("mlx-community/North-Micro-Vision-Instruct-4bit") # 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
- Atomic Chat
File size: 747 Bytes
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"data_format": "channels_first",
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"device": null,
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"do_center_crop": null,
"do_convert_rgb": true,
"do_normalize": true,
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"do_rescale": true,
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"image_mean": [
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],
"image_std": [
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"input_data_format": null,
"merge_size": 2,
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"patch_size": 16,
"processor_class": "CohereCompassProcessor",
"resample": 3,
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"return_tensors": null,
"size": {
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},
"temporal_patch_size": 2,
"image_processor_type": "CohereCompassImageProcessor"
}
|