Instructions to use Aymann/Anomal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aymann/Anomal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Aymann/Anomal") 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("Aymann/Anomal") model = AutoModelForImageClassification.from_pretrained("Aymann/Anomal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Aymann/Anomal: direct link, hf CLI and curl.
- Browser
- Download file 111 MB
-
https://huggingface.co/Aymann/Anomal/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Aymann/Anomal@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Aymann/Anomal/resolve/refs%2Fpr%2F1/pytorch_model.bin
111 MB
- Xet hash:
- e53e2edde4ba9776a572cc256d53468195646a3396bba0de63e8337f1f6fd449
- Size of remote file:
- 111 MB
- SHA256:
- 30c4b90a4bd20dd029afc1489acc921d52ff6a378b222149d18beabde5db09e7
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