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:
- 71a21f8b536785a32a8768d8ed384b756a94dc98823274c4a7490a7f1c16d723
- Size of remote file:
- 346 MB
- SHA256:
- 5e118cd37da21763572d011fb261e035862510481e8f5fffaeb477e7ad307184
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