This section provides more information on how to integrate a [PyTorch](https://pytorch.org/) model into vLLM.
!!! important
Many decoder language models can now be automatically loaded using the [Transformers backend][transformers-backend] without having to implement them in vLLM. See if `vllm serve <model>` works first!
Contents:
vLLM models are specialized [PyTorch](https://pytorch.org/) models that take advantage of various [features][compatibility-matrix] to optimize their performance.
-[Basic](basic.md)
The complexity of integrating a model into vLLM depends heavily on the model's architecture.
-[Registration](registration.md)
The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM.
-[Tests](tests.md)
However, this can be more complex for models that include new operators (e.g., a new attention mechanism).
-[Multimodal](multimodal.md)
!!! note
Read through these pages for a step-by-step guide:
The complexity of adding a new model depends heavily on the model's architecture.
The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM.
-[Implementing a Basic Model](basic.md)
However, for models that include new operators (e.g., a new attention mechanism), the process can be a bit more complex.
-[Registering a Model to vLLM](registration.md)
-[Writing Unit Tests](tests.md)
-[Multi-Modal Support](multimodal.md)
!!! tip
!!! tip
If you are encountering issues while integrating your model into vLLM, feel free to open a [GitHub issue](https://github.com/vllm-project/vllm/issues)
If you are encountering issues while integrating your model into vLLM, feel free to open a [GitHub issue](https://github.com/vllm-project/vllm/issues)