Instructions to use Sebastianpinar/lora2-36 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora2-36 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora2-36") 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/lora2-36") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora2-36") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 353dfe935a1404a8c387141cb0e642fac1133abd31f9c9d6c26b041ea6eef483
- Size of remote file:
- 1.22 GB
- SHA256:
- 421b1e0458772950700f6d013d415be8a58cbbf5922ddbfa4fb342d131537b2a
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