Instructions to use optimum-intel-internal-testing/tiny-random-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum-intel-internal-testing/tiny-random-vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="optimum-intel-internal-testing/tiny-random-vit") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("optimum-intel-internal-testing/tiny-random-vit") model = AutoModelForImageClassification.from_pretrained("optimum-intel-internal-testing/tiny-random-vit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88f3a0c7781dbef99e65d6839090f92cfd90ab0e3f27f9147fda6809b3cbd7d0
- Size of remote file:
- 292 kB
- SHA256:
- f335ce3ad15e6d1aa6cae1f86d2ec579373286b1e4a40b92c51751355b6d6d95
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.