Instructions to use Docty/solacies with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Docty/solacies with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Docty/solacies") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Docty/solacies") model = AutoModelForImageClassification.from_pretrained("Docty/solacies", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Docty/solacies: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/Docty/solacies/resolve/main/training_args.bin
- Command line
-
hf download hf://Docty/solacies/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Docty/solacies/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- 0221505b8fa62fb5b676b18d7de356ae528c589f02df5fdbb239bed437227fe2
- Size of remote file:
- 5.78 kB
- SHA256:
- 2749b476100f1d5f9cc9322da668b6a0c25d1a2cdcaa80ee3927191cd1610b37
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.