Image Feature Extraction
Transformers
JAX
Safetensors
MLX
PyTorch
aimv2_vision_model
vision
custom_code
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
- Xet hash:
- 43c9e80a081ab9f4ea1635c4f8f6732bd2daf1fad8ea2080fac6b78ba448b369
- Size of remote file:
- 1.24 GB
- SHA256:
- d52bb8cda1854e55348e9e6046046cc8a8b6218167d8b8580a340f6f4b172ca4
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