Instructions to use hf-tiny-model-private/tiny-random-ResNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ResNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-ResNetModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ResNetModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ResNetModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9e82ce28857e4d9b8a24ab6830d64a1f18cbedf2c56ab8315cf4acaa69fd5b2
- Size of remote file:
- 189 kB
- SHA256:
- 6deef0f305ec61480fec494f8b3b50c144bbdd9e8971a5b2849be43e06bce4b0
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