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:
- b33d1e6936cdf5d611e65bcdffbd137e327637e8c8fc8593bd527b84ffe7eeaa
- Size of remote file:
- 1.84 MB
- SHA256:
- d3c9ba0da63f5d5db0c748d841bdb00f0bfcbb1d22eb3af0b472ab2d132b2be0
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