Instructions to use Sebastianpinar/lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora") 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") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c04b99a75df3bce3ec7537085e66108beefaab96fcbe308cd60eb7498da1a49f
- Size of remote file:
- 346 MB
- SHA256:
- ed60421b30580b6461721bc90ee9151541c9523d9beee5953ae2b349fde7dfa7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.