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:
- 40bf5add5cf687c23e32e635af513bf6aacce94119d402dfb1e535b16a86b9fa
- Size of remote file:
- 45.8 MB
- SHA256:
- 7fbe696ff4b341aa5de2509c79c8ad3ef4526edf4d01afb7b3050d5d40fa284b
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