Instructions to use Sebastianpinar/lora2-13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora2-13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora2-13") 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/lora2-13") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora2-13", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28986a18f794a8417aef2af80940d67b9519b2b0bf0b81061be517febed80cbb
- Size of remote file:
- 1.22 GB
- SHA256:
- 6d214df9159cf35d34a2ab8ee72ea5f16ebc8352d88897574503fedb886b5516
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