Instructions to use Sebastianpinar/lora2-76 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora2-76 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora2-76") 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/lora2-76") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora2-76", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ec93081f5354c8053cc0da51591ef92e6c6eb5831b17c55e76940b112b7ab1a
- Size of remote file:
- 1.22 GB
- SHA256:
- 6403f213d567aa38b18b732dcccad524707ff2c64270e49cc3399bf475165bbc
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