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:
- 5898362505598a83ae4f1a8614c561d8f1d25f50d69bdfc920024360b8cb2b1f
- Size of remote file:
- 1.93 MB
- SHA256:
- fe8e5ef6477b863cfa74141c2cd83f08a6501ecb4f08c143a4bdb93bdc63ce97
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