Instructions to use frncscp/dinotron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frncscp/dinotron with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frncscp/dinotron") 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("frncscp/dinotron") model = AutoModelForImageClassification.from_pretrained("frncscp/dinotron", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from frncscp/dinotron: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/frncscp/dinotron/resolve/main/training_args.bin
- Command line
-
hf download hf://frncscp/dinotron/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/frncscp/dinotron/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 62686c1b19224b641f053704067a9343dbe4c809bf0a9d3335c83e398ba6bbd3
- Size of remote file:
- 4.03 kB
- SHA256:
- 2c85f2d969349e88780a34b6db8b90f9674d7976ea872256dec6a5424af93b61
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.