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:
- 979d81ee4b9e40d9abdf8cd9d2b158e466a168310b34b482e797e45f3483c06f
- Size of remote file:
- 1.84 MB
- SHA256:
- 57ea2bfe93e1dc353fcd0e21927e19e48b3c2dbef71c0748d3900293a9a90dc3
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