Instructions to use ivensamdh/beit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivensamdh/beit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ivensamdh/beit") 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("ivensamdh/beit") model = AutoModelForImageClassification.from_pretrained("ivensamdh/beit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ivensamdh/beit: direct link, hf CLI and curl.
- Browser
- Download file 348 MB
-
https://huggingface.co/ivensamdh/beit/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ivensamdh/beit/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ivensamdh/beit/resolve/main/pytorch_model.bin
348 MB
- Xet hash:
- 3565d94c34c4bbf6e1cebc640ce10d3c55cf351a5a2310fe275d50e6e1ffbbcc
- Size of remote file:
- 348 MB
- SHA256:
- 82b0e9c2554319f9fdf0177b6de892e13bcc57fbb5dbec4f1aa304703b966756
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