Instructions to use universalml/wf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/wf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/wf") 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/wf") model = AutoModelForImageClassification.from_pretrained("universalml/wf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b0ea063273c9880e73c2534cf63b1d50f7b0efe864f00b6ccf3112711eaa6e2
- Size of remote file:
- 343 MB
- SHA256:
- e390853cce06288df99b609e6853f00aa12832c21d80afac8f37298ab5ac3c24
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