Instructions to use hf-internal-testing/tiny-random-EfficientFormerModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EfficientFormerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-EfficientFormerModel")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-EfficientFormerModel", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea50c3462af7bd71d724f4b1717dc28d9d3ef011c663ed3e7b6c0f4916c2c88b
- Size of remote file:
- 45.8 MB
- SHA256:
- 1061088937bbdfb0834d82a3301830f29a5f808d677fc51eae082bac15443365
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