Instructions to use Sebastianpinar/lora-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora-16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora-16") 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-16") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora-16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1694926c372973653905b8acb7c0a8c9e94a78bf0c88718c6f933ddc580233e
- Size of remote file:
- 4.03 kB
- SHA256:
- 2f3a3330e6012f2458f6d991abe34b5fe3fcb84ac85f4f8d22ec2fd44817346c
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