Instructions to use Sebastianpinar/lora-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora-2") 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-2") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5641db7a8e0f321def33326606a124d57b8c4a11f87dc5da3e254e46071c7ee
- Size of remote file:
- 346 MB
- SHA256:
- 3de20d41e68550a7e26dc14e113902c7ddb2612b6d47fa17749d784e6dfac492
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