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:
- 590849c339f530702c5e1ce0779436bba247f7cd12ba4f84a165f339add6a8d1
- Size of remote file:
- 1.93 MB
- SHA256:
- ff22c5dfb8e24b6ffc5a6a6432c31f38fd04d2d2ac1f5f23ab1e4b24a8660c9d
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