Instructions to use universalml/ruj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/ruj with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/ruj") 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("universalml/ruj") model = AutoModelForImageClassification.from_pretrained("universalml/ruj", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a72f706746fd953c0bb7e5448aaee4bd2e781723549cc10e7939142de0afab7
- Size of remote file:
- 343 MB
- SHA256:
- 157535e7f494a5528e4d06a2463ba9ab9a4d0a1933ee2e5dbb5bd3b5560aaa92
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