If your model is not on this list, you must register it to vLLM.
This page provides detailed instructions on how to do so.
## Built-in models
To add a model directly to the vLLM library, start by forking our [GitHub repository](https://github.com/vllm-project/vllm) and then [build it from source](#build-from-source).
This gives you the ability to modify the codebase and test your model.
After you have implemented your model (see [tutorial](#new-model-basic)), put it into the <gh-dir:vllm/model_executor/models> directory.
Then, add your model class to `_VLLM_MODELS` in <gh-file:vllm/model_executor/models/registry.py> so that it is automatically registered upon importing vLLM.
You should also include an example HuggingFace repository for this model in <gh-file:tests/models/registry.py> to run the unit tests.
If your model imports modules that initialize CUDA, consider lazy-importing it to avoid errors like `RuntimeError: Cannot re-initialize CUDA in forked subprocess`:
If your model is a multimodal model, ensure the model class implements the {class}`~vllm.model_executor.models.interfaces.SupportsMultiModal` interface.
Read more about that [here](#enabling-multimodal-inputs).
```
```{note}
Although you can directly put these code snippets in your script using `vllm.LLM`, the recommended way is to place these snippets in a vLLM plugin. This ensures compatibility with various vLLM features like distributed inference and the API server.