Instructions to use Sebastianpinar/lora-53 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora-53 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora-53") 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-53") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora-53") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ba9d8196de306fca380a95e1dbcbb93f42e262998cd5ec25443771439555109
- Size of remote file:
- 4.03 kB
- SHA256:
- a5dcc65d43a5ce8ba2b346df0811e2eddf650f0908b86094ddc98e1f37b763eb
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