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:
- c2916dc618b01ef15973bd5c07340dce175202ff8dba434883d8ddb804164bce
- Size of remote file:
- 5.18 kB
- SHA256:
- 8ac003b168f2c2bbca2e89270f389c6d84ec193a7e0b1c91d3343a4b6b960fa0
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