Instructions to use hf-internal-testing/tiny-random-UnivNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UnivNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-UnivNetModel")# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-UnivNetModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-UnivNetModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91a771d74375b572b5c62b03a29b97c754636423a14c92c76f33e05b78a02521
- Size of remote file:
- 535 kB
- SHA256:
- df55a3147c8013455c9d9a62b9fadbdf3474e6ae2647e1807c118b63db37c531
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