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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-EfficientFormerModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-EfficientFormerModel: direct link, hf CLI and curl.
- Browser
- Download file 1.84 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-EfficientFormerModel/resolve/a15bcddf504d47c1ce7da80e4ecff26de435564d/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-EfficientFormerModel@a15bcddf504d47c1ce7da80e4ecff26de435564d/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-EfficientFormerModel/resolve/a15bcddf504d47c1ce7da80e4ecff26de435564d/pytorch_model.bin
1.84 MB
- Xet hash:
- dc3067fc5e68faa8e3ece979438b14fb160c921da7f4c8025c46ab62e8dd504a
- Size of remote file:
- 1.84 MB
- SHA256:
- bce24ada110b4b27dbb229765aca435a583f10e0cad1a74862795c67cdaae38c
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