| # Summary | |
| !!! important | |
| Many decoder language models can now be automatically loaded using the [Transformers modeling backend](../../models/supported_models.md#transformers) without having to implement them in vLLM. See if `vllm serve <model>` works first! | |
| vLLM models are specialized [PyTorch](https://pytorch.org/) models that take advantage of various [features](../../features/README.md#compatibility-matrix) to optimize their performance. | |
| The complexity of integrating a model into vLLM 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. | |
| However, this can be more complex for models that include new operators (e.g., a new attention mechanism). | |
| Read through these pages for a step-by-step guide: | |
| - [Basic Model](basic.md) | |
| - [Registering a Model](registration.md) | |
| - [Unit Testing](tests.md) | |
| - [Multi-Modal Support](multimodal.md) | |
| - [Speech-to-Text Support](transcription.md) | |
| !!! 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) | |
| or ask on our [developer slack](https://slack.vllm.ai). | |
| We will be happy to help you out! | |