Instructions to use facebook/levit-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/levit-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/levit-256") 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("facebook/levit-256") model = AutoModelForImageClassification.from_pretrained("facebook/levit-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/levit-256: direct link, hf CLI and curl.
- Browser
- Download file 77.4 MB
-
https://huggingface.co/facebook/levit-256/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/levit-256/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/levit-256/resolve/main/pytorch_model.bin
77.4 MB
- Xet hash:
- 4676988a97c9e6257645d6d967115fb9a947ede68fc69976468ee2e32f3150b9
- Size of remote file:
- 77.4 MB
- SHA256:
- 9866db931557e409253601939528ec1523bee585e69d96163975ffb3470b009f
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