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Signed-off-by: reidliu41 <reid201711@gmail.com> Co-authored-by: reidliu41 <reid201711@gmail.com>
56 lines
2.2 KiB
Markdown
56 lines
2.2 KiB
Markdown
---
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title: Registering a Model to vLLM
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---
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[](){ #new-model-registration }
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vLLM relies on a model registry to determine how to run each model.
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A list of pre-registered architectures can be found [here][supported-models].
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If your model is not on this list, you must register it to vLLM.
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This page provides detailed instructions on how to do so.
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## Built-in models
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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].
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This gives you the ability to modify the codebase and test your model.
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After you have implemented your model (see [tutorial][new-model-basic]), put it into the <gh-dir:vllm/model_executor/models> directory.
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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.
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Finally, update our [list of supported models][supported-models] to promote your model!
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!!! warning
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The list of models in each section should be maintained in alphabetical order.
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## Out-of-tree models
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You can load an external model using a plugin without modifying the vLLM codebase.
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!!! info
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[vLLM's Plugin System][plugin-system]
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To register the model, use the following code:
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```python
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from vllm import ModelRegistry
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from your_code import YourModelForCausalLM
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ModelRegistry.register_model("YourModelForCausalLM", YourModelForCausalLM)
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```
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If your model imports modules that initialize CUDA, consider lazy-importing it to avoid errors like `RuntimeError: Cannot re-initialize CUDA in forked subprocess`:
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```python
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from vllm import ModelRegistry
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ModelRegistry.register_model(
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"YourModelForCausalLM",
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"your_code:YourModelForCausalLM"
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)
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```
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!!! warning
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If your model is a multimodal model, ensure the model class implements the [SupportsMultiModal][vllm.model_executor.models.interfaces.SupportsMultiModal] interface.
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Read more about that [here][supports-multimodal].
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!!! note
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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.
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