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:
- 6e2179639467a105278b941e2ecaff1474dc6513a572cea1baa50dd76883aa5d
- Size of remote file:
- 4.16 kB
- SHA256:
- 0f3878d9d854ef3c89fd0dce393280fc9056b1b974cc374ed30a2467c9106ca6
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