Instructions to use maurice-fp/SACBenchmark-train.vgg_model.CIFAR10.base-probit_extraction.vgg13.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use maurice-fp/SACBenchmark-train.vgg_model.CIFAR10.base-probit_extraction.vgg13.1 with timm:
import timm model = timm.create_model("hf-hub:maurice-fp/SACBenchmark-train.vgg_model.CIFAR10.base-probit_extraction.vgg13.1", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from maurice-fp/SACBenchmark-train.vgg_model.CIFAR10.base-probit_extraction.vgg13.1: direct link, hf CLI and curl.
- Browser
- Download file 516 MB
-
https://huggingface.co/maurice-fp/SACBenchmark-train.vgg_model.CIFAR10.base-probit_extraction.vgg13.1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://maurice-fp/SACBenchmark-train.vgg_model.CIFAR10.base-probit_extraction.vgg13.1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/maurice-fp/SACBenchmark-train.vgg_model.CIFAR10.base-probit_extraction.vgg13.1/resolve/main/pytorch_model.bin
516 MB
- Xet hash:
- d6826e80b81293da58c675c5dab6a9309b6a0ea5fb1bfbdcb3ee23cb81b422bd
- Size of remote file:
- 516 MB
- SHA256:
- b89d5c61f3a44c3d35525379c01bcecc30d5fa5dbd7fa46f1e31f262b49794db
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