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:
- 3f02309950f3d7066d1ed2bb456e8c7c4b6d7cdc81f78c6aaef5bfcc1aef271f
- Size of remote file:
- 1.84 MB
- SHA256:
- 10ede6cc724b955e3b8bb501e89b54cfb148874b09820c20ce8b4afd03cf7dc0
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