Instructions to use Abhiram4/VitTea with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhiram4/VitTea with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Abhiram4/VitTea") 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("Abhiram4/VitTea") model = AutoModelForImageClassification.from_pretrained("Abhiram4/VitTea", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Abhiram4/VitTea: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/Abhiram4/VitTea/resolve/main/training_args.bin
- Command line
-
hf download hf://Abhiram4/VitTea/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Abhiram4/VitTea/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 40ed565269555bcc8a7c1be3e809313aec706b298737ddb445be14d198cd749e
- Size of remote file:
- 4.03 kB
- SHA256:
- cb777d150b61f8b588bedd0cd389df0fcfbb2c7e59f2a6e80c67b5f3003b8809
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