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:
- 37b31c5da64e4c2c1f6064e7cf1ac51670fa3702b16da3b9fd6f37aefcd28b65
- Size of remote file:
- 1.84 MB
- SHA256:
- 8c156200ee13f6813a447cbf79b0f3e64ae6d6e012c4fd2ad871f36ad2d8d412
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