Instructions to use ezoa/vitezoa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ezoa/vitezoa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ezoa/vitezoa") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ezoa/vitezoa") model = AutoModelForImageClassification.from_pretrained("ezoa/vitezoa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ezoa/vitezoa: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/ezoa/vitezoa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ezoa/vitezoa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ezoa/vitezoa/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 6c31ed632c34ce6b38c986e028a081b904fbb3a3a6985a2b17f29fd08c6b8556
- Size of remote file:
- 343 MB
- SHA256:
- 257d8980c7bb3829645774f52dcc65d3f4fc9b30f7f7883dcdf086e37e9abfeb
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