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:
- 528f4dbedec0054d1994d7769a978ba2287c9b750796abb51047ad8e60a3e47b
- Size of remote file:
- 4.09 kB
- SHA256:
- 4f2c9e6fc08390d24d7ce232d49ea7512b56ea27d805dad0a3c11eddcc1f53b7
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