Instructions to use Sebastianpinar/lora-21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora-21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora-21") 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/lora-21") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora-21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ed7af78ca73e32f4400cffaf5ac83adc4eec3379db18df0813c392e57aa8dea
- Size of remote file:
- 4.03 kB
- SHA256:
- b1773ca1ff5ae39850e9aa317b8340de90e3aa468929c337dc9b4ea34d549a9c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.