Instructions to use hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hf-transformers-bot HF Staff
Update tiny models for EfficientFormerForImageClassificationWithTeacher
d60a30b Download pytorch_model.bin from hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher: direct link, hf CLI and curl.
- Browser
- Download file 1.84 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher/resolve/d60a30bc20ffa413f2ca122975886e60c5edd086/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher@d60a30bc20ffa413f2ca122975886e60c5edd086/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher/resolve/d60a30bc20ffa413f2ca122975886e60c5edd086/pytorch_model.bin
1.84 MB
- Xet hash:
- 45ada91082bfbbe012179dc1b6a52c21dd71ff2c51842668484b57429c6fca0e
- Size of remote file:
- 1.84 MB
- SHA256:
- 93a07c437bdad0b6ad5aa9f5d1d75080b80087c98ecff20427ec4ea0a7c9e303
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.