Instructions to use glopez/cifar-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use glopez/cifar-10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="glopez/cifar-10") 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("glopez/cifar-10") model = AutoModelForImageClassification.from_pretrained("glopez/cifar-10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from glopez/cifar-10: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/glopez/cifar-10/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://glopez/cifar-10/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/glopez/cifar-10/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- f1b2e7dceb51b7b3b19a9b8aab0f416c786701f7ec4c863eb16cb551115c57c6
- Size of remote file:
- 343 MB
- SHA256:
- 92c692e53bf22eeaf5cb06bc59e3d5b7ff8d3a79d38f5f3a17283596f25f06bf
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