Instructions to use Sebastianpinar/lora2-76 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora2-76 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora2-76") 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/lora2-76") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora2-76", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 778516eaba7837831b89ba19ac182920fb0e944d0d93efdb13d84ecada3858ed
- Size of remote file:
- 4.09 kB
- SHA256:
- 75677b8549405a921ca4306ade251b3ae2803860d7c80f83b1564b0d2ebaba12
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