Instructions to use HorcruxNo13/bit-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HorcruxNo13/bit-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HorcruxNo13/bit-50") 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("HorcruxNo13/bit-50") model = AutoModelForImageClassification.from_pretrained("HorcruxNo13/bit-50") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e9428083933b6390cf8261c5b09f959382d2ccd0f67e61185d69d25572c1a97
- Size of remote file:
- 4.03 kB
- SHA256:
- 4275bea43a0c1ca3b32b9ff0bb80a107b1f10e2e44409c0b0c073960d9a88136
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