Instructions to use Sebastianpinar/lora2-89 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora2-89 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora2-89", device_map="auto") 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/lora2-89") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora2-89", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b97b31661c0921d9f326757af7ea69f32e9aab4e300500a5d06b1e3b0fd927fa
- Size of remote file:
- 4.09 kB
- SHA256:
- 0f3e21d24e603591e321d4a40a4e5153f2e0eedd0f56d1feed2c1e29b7602629
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