Instructions to use solve/vit-zigzag-attribute-768dim-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use solve/vit-zigzag-attribute-768dim-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="solve/vit-zigzag-attribute-768dim-patch16-224")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("solve/vit-zigzag-attribute-768dim-patch16-224") model = AutoModel.from_pretrained("solve/vit-zigzag-attribute-768dim-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from solve/vit-zigzag-attribute-768dim-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/solve/vit-zigzag-attribute-768dim-patch16-224/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://solve/vit-zigzag-attribute-768dim-patch16-224@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/solve/vit-zigzag-attribute-768dim-patch16-224/resolve/refs%2Fpr%2F1/pytorch_model.bin
346 MB
- Xet hash:
- b415a48332a232d432f31c6f964a04c49ffd9d65d0fa3e6cbd76c6b375399433
- Size of remote file:
- 346 MB
- SHA256:
- 5c1752d132372a257f9ad9752ae172207abe9ad38c539bc9869ff809c79f2d56
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