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:
- 07f205facdf9ff1cd67ebdc10021cef4c8dbcc809646dd24e27905d91af6d26e
- Size of remote file:
- 1.84 MB
- SHA256:
- 7746ee862e610ee4408a688ff9b61d16163b23aef88d2c6b3122f3dd3c61780b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.