Instructions to use Kibalama/Digit_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kibalama/Digit_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kibalama/Digit_classification_model") 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("Kibalama/Digit_classification_model") model = AutoModelForImageClassification.from_pretrained("Kibalama/Digit_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Kibalama/Digit_classification_model: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Kibalama/Digit_classification_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Kibalama/Digit_classification_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Kibalama/Digit_classification_model/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- cb7572cb93dd18f14af83a91b4de9828bf3a8abb518cea6f5ff41b60ec04d2b8
- Size of remote file:
- 5.3 kB
- SHA256:
- cc67490d3e63f78df932ae17578701d9d5562664a1fa8c1d2ae9ca7e7506d313
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