Instructions to use Eveready/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eveready/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Eveready/tmp_trainer") 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("Eveready/tmp_trainer") model = AutoModelForImageClassification.from_pretrained("Eveready/tmp_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Eveready/tmp_trainer: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/Eveready/tmp_trainer/resolve/main/training_args.bin
- Command line
-
hf download hf://Eveready/tmp_trainer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Eveready/tmp_trainer/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 706172f9e9064a23f56373aa2542eae7c95581119d85f65440e42b021cc7eaed
- Size of remote file:
- 4.92 kB
- SHA256:
- 4d9a47a247fa65f0ad617fb603dfef3c87f6a81b4456cfa4e375761809f05e4e
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