| 1 | # Summary |
| 2 | |
| 3 | !!! important |
| 4 | 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! |
| 5 | |
| 6 | vLLM models are specialized [PyTorch](https://pytorch.org/) models that take advantage of various [features](../../features/README.md#compatibility-matrix) to optimize their performance. |
| 7 | |
| 8 | The complexity of integrating a model into vLLM depends heavily on the model's architecture. |
| 9 | The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM. |
| 10 | However, this can be more complex for models that include new operators (e.g., a new attention mechanism). |
| 11 | |
| 12 | Read through these pages for a step-by-step guide: |
| 13 | |
| 14 | - [Basic Model](basic.md) |
| 15 | - [Registering a Model](registration.md) |
| 16 | - [Unit Testing](tests.md) |
| 17 | - [Multi-Modal Support](multimodal.md) |
| 18 | - [Speech-to-Text Support](transcription.md) |
| 19 | |
| 20 | !!! tip |
| 21 | 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) |
| 22 | or ask on our [developer slack](https://slack.vllm.ai). |
| 23 | We will be happy to help you out! |