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@@ -63,7 +63,7 @@ provides built-in composable modules that are packaged into a few key components
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 Component | Description |
 ---- | --- |
-[**physicsnemo.models**](https://docs.nvidia.com/deeplearning/physicsnemo/physicsnemo-core/api/physicsnemo.models.html) | A collection of optimized, customizable, and easy-to-use families of model architectures such as Neural Operators, Graph Neural Networks, Diffusion models, Transformer models, and many more|
+[**physicsnemo.models**](https://docs.nvidia.com/physicsnemo/latest/user-guide/model_architectures.html) ( [More Details](https://docs.nvidia.com/physicsnemo/latest/physicsnemo/api_models.html)) | A collection of optimized, customizable, and easy-to-use families of model architectures such as Neural Operators, Graph Neural Networks, Diffusion models, Transformer models, and many more|
 [**physicsnemo.datapipes**](https://docs.nvidia.com/deeplearning/physicsnemo/physicsnemo-core/api/physicsnemo.datapipes.html) | Optimized and scalable built-in data pipelines fine-tuned to handle engineering and scientific data structures like point clouds, meshes, etc.|
 [**physicsnemo.distributed**](https://docs.nvidia.com/deeplearning/physicsnemo/physicsnemo-core/api/physicsnemo.distributed.html) | A distributed computing sub-module built on top of `torch.distributed` to enable parallel training with just a few steps|
 [**physicsnemo.curator**](https://github.com/NVIDIA/physicsnemo-curator) | A sub-module to streamline and accelerate the process of data curation for engineering datasets|