Image Feature Extraction
Transformers
JAX
Safetensors
MLX
PyTorch
aimv2_vision_model
vision
custom_code
Eval Results (legacy)
Instructions to use apple/aimv2-3B-patch14-448 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/aimv2-3B-patch14-448 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="apple/aimv2-3B-patch14-448", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("apple/aimv2-3B-patch14-448", trust_remote_code=True) model = AutoModel.from_pretrained("apple/aimv2-3B-patch14-448", trust_remote_code=True, device_map="auto") - MLX
How to use apple/aimv2-3B-patch14-448 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download apple/aimv2-3B-patch14-448 --local-dir aimv2-3B-patch14-448
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download flax_model.msgpack from apple/aimv2-3B-patch14-448: direct link, hf CLI and curl.
- Browser
- Download file 10.9 GB
-
https://huggingface.co/apple/aimv2-3B-patch14-448/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://apple/aimv2-3B-patch14-448/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/apple/aimv2-3B-patch14-448/resolve/main/flax_model.msgpack
10.9 GB
- Xet hash:
- 0be32a98ff66fb39ed7efedc4106cb839fa71f63625ac1f4654ab3461a692ad9
- Size of remote file:
- 10.9 GB
- SHA256:
- 60e1e3c5f9d05e69d93dd78b383281e18a0994524ddfaf85f70ef77fcb6846f3
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