Instructions to use Sebastianpinar/lora2-21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora2-21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora2-21") 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/lora2-21") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora2-21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70ca1aa59a61b5d1ed61bb8ec28b0d42952371fdd0314b46b87b688005fd3ccd
- Size of remote file:
- 1.22 GB
- SHA256:
- abe46368ff0470f2d1fa979531b2b4a58ed38e9678bf948ab1e291e8456a90aa
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