Instructions to use Sebastianpinar/lora-67 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora-67 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora-67") 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("Sebastianpinar/lora-67") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora-67") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5cbc4c485c52a5d75aa15ffefda997ddb9c90a2402e1f09203af52a6ab1fcf2f
- Size of remote file:
- 4.03 kB
- SHA256:
- 2148137b530f8cdf6f946ed0acc76fb8bbd20802c928f9a507bd0fcfd3515737
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