Instructions to use Sebastianpinar/lora2-39 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora2-39 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora2-39") 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/lora2-39") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora2-39", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d37f6a58a93fb91f275349b337e525cce5be4a99273bc8d1c40512ef1bd0bce1
- Size of remote file:
- 1.22 GB
- SHA256:
- 05807e041bf464cc3d504fda19fefec6c11f2c6b5b01233214dd8af42cd87d50
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