Instructions to use Sebastianpinar/lora-22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora-22 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora-22") 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-22") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora-22") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 58c2848e8d0e911910c809ddfec654113bad80a7717dd2f7917d5225058ef03d
- Size of remote file:
- 346 MB
- SHA256:
- ba9904521cc9904448f86f8a3dba0136011f324639dc11203c515be7593cd638
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