Instructions to use ProbeX/Model-J__MAE__model_idx_0990 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_0990 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_0990") 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_0990") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0990", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from ProbeX/Model-J__MAE__model_idx_0990: direct link, hf CLI and curl.
- Browser
- Download file 4.3 kB
-
https://huggingface.co/ProbeX/Model-J__MAE__model_idx_0990/resolve/main/trainer_state.json
- Command line
-
hf download hf://ProbeX/Model-J__MAE__model_idx_0990/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/ProbeX/Model-J__MAE__model_idx_0990/resolve/main/trainer_state.json
4.3 kB
Invalid JSON:Unexpected token 'I', ..."ad_norm": Infinity,
"... is not valid JSON
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