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:
- 0404adaa34bdbc5c53928551ff72c0fa926424c0b85c866ee68d69a3d8f1bf1b
- Size of remote file:
- 1.84 MB
- SHA256:
- 947c9e4ec9d0045fbcfe4820c8447aa5fdc9f403cd50a647a6a38525f36c0c14
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