Instructions to use pthpth/ViTune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pthpth/ViTune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pthpth/ViTune") 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("pthpth/ViTune") model = AutoModelForImageClassification.from_pretrained("pthpth/ViTune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pthpth/ViTune: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/pthpth/ViTune/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://pthpth/ViTune@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pthpth/ViTune/resolve/refs%2Fpr%2F1/pytorch_model.bin
343 MB
- Xet hash:
- 1db8ead9ceab96129bd5a426e4a84f419ddfda98f4ef6f8373a1a00c7cd72f42
- Size of remote file:
- 343 MB
- SHA256:
- 5821a3391ae66dc4bfaf5cc94e0d64cffae117434bffedef3b3f73b8143d815c
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