Instructions to use Sebastianpinar/lora2-82 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora2-82 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora2-82") 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/lora2-82") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora2-82", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06fcf3ff6021d09ecaa03b871a7eb1e1e6b90e182d8a9c3e8a5ce19c68e4d156
- Size of remote file:
- 1.22 GB
- SHA256:
- 38ed80ecf11a80695e75274208b7d7be3f126b2cd17b946e039ed9e6d640681f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.