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 training_args.bin from Sebastianpinar/lora-34: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/Sebastianpinar/lora-34/resolve/3e57e5b729e1fda17035ed0c61a8f6da7c2ef55b/training_args.bin
- Command line
-
hf download hf://Sebastianpinar/lora-34@3e57e5b729e1fda17035ed0c61a8f6da7c2ef55b/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sebastianpinar/lora-34/resolve/3e57e5b729e1fda17035ed0c61a8f6da7c2ef55b/training_args.bin
4.03 kB
- Xet hash:
- 4c1e3bb8f45d8727af440534ce142f83c44eb84ee48827dd922f812b6d1d377a
- Size of remote file:
- 4.03 kB
- SHA256:
- 5f76bc58a2524c3fb741f20ab8b6d46316a9a4a2b01c979766ca5dd542f04413
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