Instructions to use ProbeX/Model-J__SupViT__model_idx_0195 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__SupViT__model_idx_0195 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__SupViT__model_idx_0195") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0195") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0195", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProbeX/Model-J__SupViT__model_idx_0195: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ProbeX/Model-J__SupViT__model_idx_0195/resolve/main/training_args.bin
- Command line
-
hf download hf://ProbeX/Model-J__SupViT__model_idx_0195/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProbeX/Model-J__SupViT__model_idx_0195/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 5a032bd44f605843eb8702417b062c3f8e4f3c0be10ddc9a101735d6478d061c
- Size of remote file:
- 5.37 kB
- SHA256:
- 3984d066563ab434c4ae666976dfd9c34d5fedf3911a11bb5b8411ab00f3bfa9
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