Instructions to use Sebastianpinar/lora-70 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora-70 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora-70") 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-70") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora-70") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5cbe4283c3dd8e4b9109c45321d9978c046b72e8af8cc0388b09a4a0b3763b03
- Size of remote file:
- 346 MB
- SHA256:
- 421f0cbf37cae2b717aed12b8b9b6275338dfea09d7d50819a472081ddc38250
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