Instructions to use tzhao3/vit-MNIST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tzhao3/vit-MNIST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tzhao3/vit-MNIST") 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-MNIST") model = AutoModelForImageClassification.from_pretrained("tzhao3/vit-MNIST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tzhao3/vit-MNIST: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/tzhao3/vit-MNIST/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tzhao3/vit-MNIST/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tzhao3/vit-MNIST/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 7de0a51f20ecedc0a98bc64f946952dd862ef588cce2672f6ef4fb016d865840
- Size of remote file:
- 343 MB
- SHA256:
- 9ae27c0f45c978a4cf49ae76339604dd76bd0fc754acde62ba141349f21c823a
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