I want to replace my OpenAI API calls with a self-hosted vLLM instance to save on costs. Does the current version support the tools and tool_choice parameters for function calling? Also, how does it handle JSON mode compared to the official GPT-4 implementation?
3 answers
As of the latest 2024 updates, vLLM has significantly improved its OpenAI compatibility. While it doesn't support every single edge case of the tools API yet, you can achieve similar results using "Guided Decoding." By passing a JSON schema or a Regex pattern through the extra_body parameter, you can force the model to output structured data. This is often more reliable than OpenAI's native tool calling because you have direct control over the sampling constraints. I've successfully ported several LangChain projects to a vLLM backend by simply updating the base_url to my local server.
Are you using a model that has a built-in chat template for function calling, like the latest Hermes or Command R series?
You can use the --enable-auto-tool-choice flag in the newer releases to make the transition from OpenAI much smoother.
That flag is a lifesaver, Pamela. It allows vLLM to act as a true drop-in replacement without requiring major changes to the application's prompting logic or the client-side SDK.
I’m using Mistral-7B, Justin. I noticed that without a proper Jinja2 chat template configured in vLLM, the model fails to understand the specialized tokens required for tool calling, even if the API server technically supports the parameter.