Instructions to use Sebastianpinar/lora2-49 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora2-49 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora2-49") 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/lora2-49") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora2-49", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48bac316f6f9b2cf6c88278b3060bf50fb69afeddc7c99d3ca286acf3a4c2665
- Size of remote file:
- 4.09 kB
- SHA256:
- a23256a5ac7db865e7e9939163207bfd5d99512f033b8f905fa044abd1fca8ed
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