Instructions to use howdyaendra/xblock-large-patch3-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use howdyaendra/xblock-large-patch3-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="howdyaendra/xblock-large-patch3-224") 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("howdyaendra/xblock-large-patch3-224") model = AutoModelForImageClassification.from_pretrained("howdyaendra/xblock-large-patch3-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from howdyaendra/xblock-large-patch3-224: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/howdyaendra/xblock-large-patch3-224/resolve/main/training_args.bin
- Command line
-
hf download hf://howdyaendra/xblock-large-patch3-224/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/howdyaendra/xblock-large-patch3-224/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 423c8d325329110c28c6f32082125bdac291a48b0f72aaf172b86ee101c370a7
- Size of remote file:
- 4.98 kB
- SHA256:
- db92048ee3b95441a547e425f5207697cec5ea7a83eaed11cf7a1e8f1fd35070
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