Instructions to use hf-internal-testing/tiny-random-LevitForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LevitForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-LevitForImageClassification") 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-LevitForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-LevitForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-LevitForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 28.2 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-LevitForImageClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-LevitForImageClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-LevitForImageClassification/resolve/refs%2Fpr%2F1/model.safetensors
28.2 MB
- Xet hash:
- ea08086a4bc2abf75633c60ffaf958485778e1c8401cdf25b799f7cec4c9f4cd
- Size of remote file:
- 28.2 MB
- SHA256:
- c3ec844539b4478af1fdbecd69d9f28b6275efbf84f3d4d6d112ca35399cc9e8
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