Instructions to use hf-tiny-model-private/tiny-random-EfficientFormerModel 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-EfficientFormerModel 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-EfficientFormerModel")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-EfficientFormerModel", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56f2c19acc53b0159ff7a4a6153e15c6cb5f821eee3c8ff4263583553b24ba99
- Size of remote file:
- 45.8 MB
- SHA256:
- 95682f81e1af996a03c3b8f350d40b364b8c4cc3aea6871f08d9731547fd48eb
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