Instructions to use tzhao3/vit-test-CIFAR10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tzhao3/vit-test-CIFAR10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tzhao3/vit-test-CIFAR10") 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("tzhao3/vit-test-CIFAR10") model = AutoModelForImageClassification.from_pretrained("tzhao3/vit-test-CIFAR10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from tzhao3/vit-test-CIFAR10: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/tzhao3/vit-test-CIFAR10/resolve/main/optimizer.pt
- Command line
-
hf download hf://tzhao3/vit-test-CIFAR10/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/tzhao3/vit-test-CIFAR10/resolve/main/optimizer.pt
687 MB
- Xet hash:
- 6f8c303aa171836547c2ab138b043266df36429c4b49862d4ab1869f671fd0ed
- Size of remote file:
- 687 MB
- SHA256:
- 52c73cb6378b0568de0d32b182c8a482320407fa46825f43c4ac8b60670f5ef7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.