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:
- 33966a4d458eb90e7b6ce4eb7bc93803dfd8b36dce0f05cf1b9506d3ceef1b04
- Size of remote file:
- 1.84 MB
- SHA256:
- a4ed65e5b99849b8f34940fa3469b8c77cc9c4e70fefaf92c8f02ae43a634cae
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