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:
- 9d0b4f778f6160f8f76c5c39c7bc6d7f6df848de7e1badcfe05d84ea5536d787
- Size of remote file:
- 1.93 MB
- SHA256:
- bbb12edfa4ed55af4f75d9029d6008a3ceba790e5397368ae67f09c9702bb2bb
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