Instructions to use mbiarreta/ButterflyClasifModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbiarreta/ButterflyClasifModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mbiarreta/ButterflyClasifModel") 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("mbiarreta/ButterflyClasifModel") model = AutoModelForImageClassification.from_pretrained("mbiarreta/ButterflyClasifModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mbiarreta/ButterflyClasifModel: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/mbiarreta/ButterflyClasifModel/resolve/main/training_args.bin
- Command line
-
hf download hf://mbiarreta/ButterflyClasifModel/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mbiarreta/ButterflyClasifModel/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- 05b4b258bc9f96761dc5ac3a3e4a3ec2a3fc70fd8c86642c54b680d315c8a20f
- Size of remote file:
- 5.05 kB
- SHA256:
- 6a868e27f6803d75d8624b34c320cc33b06574c78a3c143da8cd48657ba9d3cb
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