Instructions to use Sebastianpinar/lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora") 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") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 312c6f7b27fd9fbf4aee2a58de8f5863d23ee3401a4d560769b29da7afeb537f
- Size of remote file:
- 4.03 kB
- SHA256:
- 5f4274cf4b21db8318f2350aae84f9baa3b16096ce74c2d32f6417d5727c4355
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