update models tutorial
"* **`Benchmarks`**: This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major CL benchmarks \\(similar to what has been done for [torchvision](https://pytorch.org/docs/stable/torchvision/index.html)\\).\n",
"* **`Training`**: This module provides all the necessary utilities concerning model training. This includes simple and efficient ways of implement new _continual learning_ strategies as well as a set pre-implemented CL baselines and state-of-the-art algorithms you will be able to use for comparison!\n",
"* **`Evaluation`**: This module provides all the utilities and metrics that can help evaluate a CL algorithm with respect to all the factors we believe to be important for a continually learning system. It also includes advanced logging and plotting features, including native [Tensorboard](https://www.tensorflow.org/tensorboard) support.\n",
- "* **`Models`**: In this module you'll be able to find several model architectures and pre-trained models that can be used for your continual learning experiment \\(similar to what has been done in [torchvision.models](https://pytorch.org/docs/stable/torchvision/index.html)\\).\n",
+ "* **`Models`**: In this module you'll find several model architectures and pre-trained models that can be used for your continual learning experiment \\(similar to what has been done in [torchvision.models](https://pytorch.org/docs/stable/torchvision/index.html)\\). Furthermore, we provide everything you need to implement architectural strategies, task-aware models, and dynamic model expansion.\n",
"* **`Logging`**: It includes advanced logging and plotting features, including native _stdout_, _file_ and [Tensorboard](https://www.tensorflow.org/tensorboard) support \\(How cool it is to have a complete, interactive dashboard, tracking your experiment metrics in real-time with a single line of code?\\)\n",
"\n",
"{% code title=\"Avalanche Main Modules and Sub-Modules\" %}\n",
