Instructions to use frncscp/patacoswin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frncscp/patacoswin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frncscp/patacoswin") 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("frncscp/patacoswin") model = AutoModelForImageClassification.from_pretrained("frncscp/patacoswin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from frncscp/patacoswin: direct link, hf CLI and curl.
- Browser
- Download file 110 MB
-
https://huggingface.co/frncscp/patacoswin/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://frncscp/patacoswin/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/frncscp/patacoswin/resolve/main/pytorch_model.bin
110 MB
- Xet hash:
- b09b1fad06daa73b81d9ad612e4f5e44a096f6933e2963a8609365c0fa49cdff
- Size of remote file:
- 110 MB
- SHA256:
- 5bb79c1b915bc7bef0ad13b77ebe300f963c01725a5c1ec428a12f837649671e
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