Instructions to use KodaPop/begone-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KodaPop/begone-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="KodaPop/begone-model") 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("KodaPop/begone-model") model = AutoModelForImageClassification.from_pretrained("KodaPop/begone-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KodaPop/begone-model: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/KodaPop/begone-model/resolve/main/training_args.bin
- Command line
-
hf download hf://KodaPop/begone-model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KodaPop/begone-model/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- 4be891e2111b206d777f3ec2e6419dad0b18497840aa8ab578d246bc09162f2e
- Size of remote file:
- 5.84 kB
- SHA256:
- f8200b57ef22ccedc7270acd5e2bf750a06e113274f0668903b9deb13d31d5ff
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