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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0f047baa7e461ab90eb05bc68dab001167e36c9735f56b284f0dc333eef8f70
- Size of remote file:
- 1.84 MB
- SHA256:
- a2ddeffd47dcb47b8c7e83bbb6f9556752f7a05e44ccc5b6ff538a5d2c409e89
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