Instructions to use ongp/Pacc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ongp/Pacc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ongp/Pacc") 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("ongp/Pacc") model = AutoModelForImageClassification.from_pretrained("ongp/Pacc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ongp/Pacc: direct link, hf CLI and curl.
- Browser
- Download file 348 MB
-
https://huggingface.co/ongp/Pacc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ongp/Pacc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ongp/Pacc/resolve/main/pytorch_model.bin
348 MB
- Xet hash:
- 0aa33b8d30f85c55998830c6adcb7ffd9d353130e9070d18aeca057ab0a7be71
- Size of remote file:
- 348 MB
- SHA256:
- 68547fa81d26215f542392b57f19b02f15e3a5233524c82571a4156e93b93e92
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