Instructions to use pollux435/ViT_model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pollux435/ViT_model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pollux435/ViT_model_output") 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("pollux435/ViT_model_output") model = AutoModelForImageClassification.from_pretrained("pollux435/ViT_model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pollux435/ViT_model_output: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/pollux435/ViT_model_output/resolve/main/training_args.bin
- Command line
-
hf download hf://pollux435/ViT_model_output/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pollux435/ViT_model_output/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- f3550aec2646761a9a3f1786c312dc78df4f357ecb547ffd8705f6339670b4d3
- Size of remote file:
- 5.37 kB
- SHA256:
- 1ac1a64be732c881817adc21deeed170198cec4fb027c31520f6ef2ea90fb358
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