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[Doc] Update docs for New Model Implementation (vllm-project#20115)
Signed-off-by: DarkLight1337 <[email protected]>
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docs/.nav.yml

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- General:
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- glob: contributing/*
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flatten_single_child_sections: true
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- Model Implementation: contributing/model
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- Model Implementation:
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- contributing/model/README.md
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- contributing/model/basic.md
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- contributing/model/registration.md
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- contributing/model/tests.md
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- contributing/model/multimodal.md
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- Design Documents:
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- V0: design
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- V1: design/v1

docs/contributing/model/README.md

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---
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title: Adding a New Model
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title: Summary
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---
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[](){ #new-model }
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This section provides more information on how to integrate a [PyTorch](https://pytorch.org/) model into vLLM.
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!!! important
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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!
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Contents:
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vLLM models are specialized [PyTorch](https://pytorch.org/) models that take advantage of various [features][compatibility-matrix] to optimize their performance.
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- [Basic](basic.md)
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- [Registration](registration.md)
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- [Tests](tests.md)
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- [Multimodal](multimodal.md)
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The complexity of integrating a model into vLLM depends heavily on the model's architecture.
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The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM.
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However, this can be more complex for models that include new operators (e.g., a new attention mechanism).
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!!! note
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The complexity of adding a new model depends heavily on the model's architecture.
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The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM.
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However, for models that include new operators (e.g., a new attention mechanism), the process can be a bit more complex.
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Read through these pages for a step-by-step guide:
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- [Implementing a Basic Model](basic.md)
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- [Registering a Model to vLLM](registration.md)
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- [Writing Unit Tests](tests.md)
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- [Multi-Modal Support](multimodal.md)
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!!! tip
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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)

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