Instructions to use Sebastianpinar/lora-86 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora-86 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora-86") 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-86") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora-86") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c590fcd8e894c2e186427894e23446a67e21c29b1b232f4c185e93de5ae279e
- Size of remote file:
- 346 MB
- SHA256:
- da346456533266a45ca9dda090b1a96752af97fdc9e8ea5e8703d48168e9c2a3
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