Instructions to use Sebastianpinar/lora2-49 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora2-49 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora2-49") 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/lora2-49") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora2-49", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9cad1b25b0234cda8e16b84d96f690d8ec40aa4c6debe46a88f815d41646a5a3
- Size of remote file:
- 1.22 GB
- SHA256:
- 511c2571a4969276c11a5030e970dcd4549a6818c602cef0f2e4536dfa881f3a
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