Instructions to use Sebastianpinar/lora2-89 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora2-89 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora2-89") 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/lora2-89") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora2-89", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4466b9cda04da91cc53d2c947f80b689e991f7d438cf09311c255c5b5ad64e60
- Size of remote file:
- 1.22 GB
- SHA256:
- 5444f479ddfe5c099d03bcaa699e3835d9db1a5bd6eebbb7728f110ca4197ab0
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