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 Torch-Pruning (TP) is a library for structural pruning with the following features:
 
-* **General-purpose Pruning Toolkit:** TP enables structural pruning for a wide range of deep neural networks, including *[Large Language Models (LLMs)](https://github.com/horseee/LLM-Pruner), [Diffusion Models](https://github.com/VainF/Diff-Pruning), [Yolov7](examples/yolov7/), [yolov8](examples/yolov8/), [ViT](examples/hf_transformers/), [Bert](examples/hf_transformers) FasterRCNN, SSD, ResNe(X)t, ConvNext, DenseNet, ConvNext, RegNet, DeepLab, etc*. Different from [torch.nn.utils.prune](https://pytorch.org/tutorials/intermediate/pruning_tutorial.html) that zeroizes parameters through masking, Torch-Pruning deploys a (non-deep) graph algorithm called **DepGraph** to remove parameters physically. Currently, TP is able to prune approximately **81/85=95.3%** of the models from Torchvision 0.13.1. Try this [Colab Demo](https://colab.research.google.com/drive/1TRvELQDNj9PwM-EERWbF3IQOyxZeDepp?usp=sharing) for a quick start.
+* **General-purpose Pruning Toolkit:** TP enables structural pruning for a wide range of deep neural networks, including *[Large Language Models (LLMs)](https://github.com/horseee/LLM-Pruner), [Diffusion Models](https://github.com/VainF/Diff-Pruning), [Yolov7](examples/yolov7/), [yolov8](examples/yolov8/), [ViT](examples/hf_transformers/), [Bert](examples/hf_transformers), FasterRCNN, SSD, ResNe(X)t, ConvNext, DenseNet, ConvNext, RegNet, DeepLab, etc*. Different from [torch.nn.utils.prune](https://pytorch.org/tutorials/intermediate/pruning_tutorial.html) that zeroizes parameters through masking, Torch-Pruning deploys a (non-deep) graph algorithm called **DepGraph** to remove parameters physically. Currently, TP is able to prune approximately **81/85=95.3%** of the models from Torchvision 0.13.1. Try this [Colab Demo](https://colab.research.google.com/drive/1TRvELQDNj9PwM-EERWbF3IQOyxZeDepp?usp=sharing) for a quick start.
 * **[Performance Benchmark](benchmarks)**: Reproduce the our results in the DepGraph paper.
 
 For more technical details, please refer to our CVPR'23 paper: