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 pytorch_model.bin from Abhiram4/VitTea: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Abhiram4/VitTea/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Abhiram4/VitTea/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Abhiram4/VitTea/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- fb2960ab502feaf87b675c22b1dd2d34fac206e81ba526f11199e1bf9c1b4c6e
- Size of remote file:
- 343 MB
- SHA256:
- 9f7b1ea05bce6add5f416590e8678b6fdbd476513d021305767a0f2337595292
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.