Instructions to use Sebastianpinar/lora-34 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebastianpinar/lora-34 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sebastianpinar/lora-34") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Sebastianpinar/lora-34") model = AutoModelForImageClassification.from_pretrained("Sebastianpinar/lora-34", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sebastianpinar/lora-34: direct link, hf CLI and curl.
- Browser
- Download file 350 MB
-
https://huggingface.co/Sebastianpinar/lora-34/resolve/3e57e5b729e1fda17035ed0c61a8f6da7c2ef55b/pytorch_model.bin
- Command line
-
hf download hf://Sebastianpinar/lora-34@3e57e5b729e1fda17035ed0c61a8f6da7c2ef55b/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sebastianpinar/lora-34/resolve/3e57e5b729e1fda17035ed0c61a8f6da7c2ef55b/pytorch_model.bin
350 MB
- Xet hash:
- d953420380148789cc506ce8247c5c8f76f51cd652b57a3116c72424fb273803
- Size of remote file:
- 350 MB
- SHA256:
- b4ffd98ef3e960a3f587a8a062938a1c8bae6eee054f9d342f9b331f77d9ae37
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