Image Feature Extraction
Transformers
JAX
Safetensors
MLX
PyTorch
aimv2_vision_model
vision
custom_code
Instructions to use apple/aimv2-large-patch14-336-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/aimv2-large-patch14-336-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="apple/aimv2-large-patch14-336-distilled", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("apple/aimv2-large-patch14-336-distilled", trust_remote_code=True) model = AutoModel.from_pretrained("apple/aimv2-large-patch14-336-distilled", trust_remote_code=True, device_map="auto") - MLX
How to use apple/aimv2-large-patch14-336-distilled with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download apple/aimv2-large-patch14-336-distilled --local-dir aimv2-large-patch14-336-distilled
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Commit History
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Fix missing revision
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Add HF compatible checkpoint (#2)
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AMLR license (#1)
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Add LICENSE
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Add `_no_split_modules`
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Update README
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initial commit
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