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:
- 48688821cc5744b72f9e69193909f0d59dc3228b397791f3acf4c6be4888696a
- Size of remote file:
- 1.93 MB
- SHA256:
- f11242bb08876cea0c8de094555b79df6f72b2ed62e98d6565cc027314c25349
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