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Version: v0.7.x

Custom Model Integration

Using the KAITO base image​

The KAITO base image includes both HuggingFace and vLLM runtime libraries along with corresponding FastAPI server scripts. This provides a convenient way to run any HuggingFace model using the HuggingFace runtime.

Note that the vLLM runtime is not supported for arbitrary custom model deployment.

Here is a sample deployment YAML. To use it:

  1. Specify the HuggingFace model ID in the container command.
  2. For models that require a HuggingFace token to download, users need to add the token to the specified secret.

The script downloads model weights during server bootstrap, eliminating the need to pre-bake them into the container image.

Limitations​

  • Hugging Face runtime only: Only HuggingFace runtime is supported for custom models. Inference performance may be slower compared to vLLM runtime if the model supports both.
  • No multi-node inference: Distributed inference across multiple nodes is not supported for custom model.
  • No autoscaling: KAITO autoscaling relies on metrics exposed by vLLM runtime, they are unavailable in HuggingFace runtime.
  • No presets: Users must manually modify the command line in the pod template for parameter changes.