Instructions to use Pamreth/deit-ena24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pamreth/deit-ena24 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Pamreth/deit-ena24") 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("Pamreth/deit-ena24") model = AutoModelForImageClassification.from_pretrained("Pamreth/deit-ena24", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Pamreth/deit-ena24: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Pamreth/deit-ena24/resolve/main/training_args.bin
- Command line
-
hf download hf://Pamreth/deit-ena24/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Pamreth/deit-ena24/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 0b52bb3922f1a03378eb4e827a267cc8b3746dc3a065aeefffbcc1df3a17bf24
- Size of remote file:
- 5.3 kB
- SHA256:
- 39a01b999eaeb1eb1fec3b8b15ab403768f5c177f01635360674f5b735cb6348
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