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apple
/
aimv2-large-patch14-native

Image Feature Extraction
Transformers
JAX
Safetensors
MLX
PyTorch
aimv2_vision_model
vision
custom_code
Model card Files Files and versions
xet
Community
4

Instructions to use apple/aimv2-large-patch14-native with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use apple/aimv2-large-patch14-native with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-feature-extraction", model="apple/aimv2-large-patch14-native", trust_remote_code=True)
    # Load model directly
    from transformers import AutoImageProcessor, AutoModel
    
    processor = AutoImageProcessor.from_pretrained("apple/aimv2-large-patch14-native", trust_remote_code=True)
    model = AutoModel.from_pretrained("apple/aimv2-large-patch14-native", trust_remote_code=True)
  • MLX

    How to use apple/aimv2-large-patch14-native with MLX:

    # Download the model from the Hub
    pip install huggingface_hub[hf_xet]
    
    huggingface-cli download --local-dir aimv2-large-patch14-native apple/aimv2-large-patch14-native
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • LM Studio
aimv2-large-patch14-native
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  • 4 contributors
History: 11 commits
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michalk8
Fix missing revision
f733a03 verified 10 months ago
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  • README.md
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  • aimv2_overview_light.png
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  • config.json
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  • configuration_aimv2.py
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  • model.safetensors
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  • modeling_aimv2.py
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  • modeling_flax_aimv2.py
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  • preprocessor_config.json
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    Add HF compatible checkpoints (#4) 10 months ago