Instructions to use hf-internal-testing/tiny-random-DeiTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DeiTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-DeiTForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DeiTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-DeiTForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-DeiTForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 196 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-DeiTForImageClassification/resolve/7f645d9b88d8bb180e18df353a954cff2fa09281/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-DeiTForImageClassification@7f645d9b88d8bb180e18df353a954cff2fa09281/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-DeiTForImageClassification/resolve/7f645d9b88d8bb180e18df353a954cff2fa09281/pytorch_model.bin
196 kB
- Xet hash:
- f3a1f36798e2308585e87b3009760a96253b593c7b76d00cfa324ecdbdcb50db
- Size of remote file:
- 196 kB
- SHA256:
- 2133f07c68098d400a850e2df537bb0cbc4eeeb33e33f5b5b360ee9eced4dfdf
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