Instructions to use nesanchezo/model_prueba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nesanchezo/model_prueba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nesanchezo/model_prueba") 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("nesanchezo/model_prueba") model = AutoModelForImageClassification.from_pretrained("nesanchezo/model_prueba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nesanchezo/model_prueba: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/nesanchezo/model_prueba/resolve/main/training_args.bin
- Command line
-
hf download hf://nesanchezo/model_prueba/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nesanchezo/model_prueba/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- 135a64b27e0473b2cb13181b7310c7d0c7bdc37f67689541f5537bb90fc92fec
- Size of remote file:
- 3.9 kB
- SHA256:
- 3e4d345aa8513feb52405705d7a7404c1484935bc9b4dda1b51cd868902e9ac1
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