Instructions to use Skullly/Testing_purposes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Skullly/Testing_purposes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Skullly/Testing_purposes") 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("Skullly/Testing_purposes") model = AutoModelForImageClassification.from_pretrained("Skullly/Testing_purposes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26d0132f01f28291cd692522a00baecf94df8fc73055c4088ce4877daf428b4f
- Size of remote file:
- 344 MB
- SHA256:
- 363a0cba9879c0536e384a6941fa055e6cac74030a8ef2cbcb3c0f2505c8a80d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.