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:
- 900524bfc4eaea1e06c4f04f477973230690d9d3b488773b19056fd628c48b67
- Size of remote file:
- 1.84 MB
- SHA256:
- 6a588f5926a6c235661cc8ce591125138481e798d6d3678d2366ee8fdd928630
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