Instructions to use universalml/hi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/hi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/hi") 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/hi") model = AutoModelForImageClassification.from_pretrained("universalml/hi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d837386ad13dfaabccddbd4313ba8c747ec29ef84904538d3dfe9c22b099061
- Size of remote file:
- 343 MB
- SHA256:
- e71afbb59bb8cdb270fcf0dde387b00a0037ff75bdf5ada03594d4f84c8fe9f4
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