# Models *Avalanche* offers basic support for defining your own models or adapt existing PyTorch models with a particular emphasis on model adaptation over time. You can find **examples** related to the models here: * [Using PyTorchCV pre-trained models](https://github.com/ContinualAI/avalanche/blob/master/examples/pytorchcv_models.py): *This example shows how to train models provided by pytorchcv with the rehearsal strategy.* * [Use a Multi-Head model](https://github.com/ContinualAI/avalanche/blob/6dbabb2ab787a53b59b9cbcb245ad500e984f671/examples/multihead.py#L27): *This example trains a Multi-head model on Split MNIST with Elastich Weight Consolidation. Each experience has a different task label, which is used at test time to select the appropriate head.* --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://avalanche.continualai.org/examples/models.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.