Instructions to use ProbeX/Model-J__MAE__model_idx_0735 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__MAE__model_idx_0735 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__MAE__model_idx_0735") 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("ProbeX/Model-J__MAE__model_idx_0735") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0735", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProbeX/Model-J__MAE__model_idx_0735: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ProbeX/Model-J__MAE__model_idx_0735/resolve/main/training_args.bin
- Command line
-
hf download hf://ProbeX/Model-J__MAE__model_idx_0735/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProbeX/Model-J__MAE__model_idx_0735/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 7d12c595adb973a50b6ae38af3cf2153ac065c034f3ae56c89a17af511ba4da8
- Size of remote file:
- 5.37 kB
- SHA256:
- 0302aaa4d6352372eb6c8783399f8fe407388923a0fbc9c1c2b7c5730d3e4a3b
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