Instructions to use jmarshall/rare-puppers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmarshall/rare-puppers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jmarshall/rare-puppers") 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("jmarshall/rare-puppers") model = AutoModelForImageClassification.from_pretrained("jmarshall/rare-puppers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jmarshall/rare-puppers: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/jmarshall/rare-puppers/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jmarshall/rare-puppers/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jmarshall/rare-puppers/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- ae74aef1f5a11e08556e41f716551383191d43a4e87d03435bb2795f4d1a8ee0
- Size of remote file:
- 343 MB
- SHA256:
- 5dae6961b2e0744647f86ada550f9630e1d0f988cdaa0bdeaf2135b8b8459d84
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