Instructions to use nesanchezo/model_handwritenNumbers-nesanchezo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nesanchezo/model_handwritenNumbers-nesanchezo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nesanchezo/model_handwritenNumbers-nesanchezo") 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_handwritenNumbers-nesanchezo") model = AutoModelForImageClassification.from_pretrained("nesanchezo/model_handwritenNumbers-nesanchezo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nesanchezo/model_handwritenNumbers-nesanchezo: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/nesanchezo/model_handwritenNumbers-nesanchezo/resolve/main/training_args.bin
- Command line
-
hf download hf://nesanchezo/model_handwritenNumbers-nesanchezo/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nesanchezo/model_handwritenNumbers-nesanchezo/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 2229f2de96acebee4daab264a55aa7db739b52a3c56b1acce76cc82959dc52f8
- Size of remote file:
- 3.96 kB
- SHA256:
- 2b1104bac082c448ae00514eb76b61bb379e5a033a1ebe24929edf03cbc14c5d
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