vllm.parser.qwen3

Qwen3 parser for tool calls and reasoning.

Qwen3 XML tool call format::

<tool_call>
<function=func_name>
<parameter=key>value</parameter>
</function>
</tool_call>

The argument body consists of <parameter=NAME>VALUE</parameter> tags. The _qwen3_arg_converter parses these into a JSON object.

Classes:

• Qwen3Parser –

  Qwen3 parser: <think>/</think> reasoning +

Qwen3Parser

Bases: ParserEngine

Qwen3 parser: <think>/</think> reasoning + <tool_call> XML tool calls in a single engine.

• <tool_call> as implicit reasoning end (a grammar transition, so it also feeds is_reasoning_end and the structured-output gate)

Subclasses that share the grammar but differ only in the four wrapper token strings (reasoning + tool-call) override the class attributes below; everything else is inherited unchanged.

Source code in vllm/parser/qwen3.py

class Qwen3Parser(ParserEngine):
    """Qwen3 parser: ``<think>``/``</think>`` reasoning +
    ``<tool_call>`` XML tool calls in a single engine.

    - ``<tool_call>`` as implicit reasoning end (a grammar transition, so it
      also feeds ``is_reasoning_end`` and the structured-output gate)

    Subclasses that share the grammar but differ only in the four wrapper
    token strings (reasoning + tool-call) override the class attributes
    below; everything else is inherited unchanged.
    """

    CONFIG_NAME = "qwen3"
    THINK_START = THINK_START
    THINK_END = THINK_END
    TOOL_START = TOOL_CALL_START
    TOOL_END = TOOL_CALL_END
    TURN_BOUNDARIES: frozenset[str] = CHATML_TURN_BOUNDARIES

    def __init__(
        self,
        tokenizer: TokenizerLike,
        tools: list[Tool] | None = None,
        **kwargs,
    ) -> None:
        chat_kwargs = kwargs.get("chat_template_kwargs", {}) or {}
        self.thinking_enabled = chat_kwargs.get("enable_thinking", True)
        kwargs.setdefault(
            "parser_engine_config",
            qwen3_config(
                thinking=self.thinking_enabled,
                name=self.CONFIG_NAME,
                think_start=self.THINK_START,
                think_end=self.THINK_END,
                tool_start=self.TOOL_START,
                tool_end=self.TOOL_END,
                turn_boundary_tokens=self.TURN_BOUNDARIES,
            ),
        )
        super().__init__(
            tokenizer,
            tools,
            **kwargs,
        )

    def extract_reasoning(
        self,
        model_output: str,
        request: ChatCompletionRequest | ResponsesRequest,
    ) -> tuple[str | None, str | None]:
        if not self.thinking_enabled:
            return None, model_output
        return super().extract_reasoning(model_output, request)

_trim_wrapping_newlines(value)

Strip one leading and one trailing newline (the Qwen3 template markup).

Source code in vllm/parser/qwen3.py

def _trim_wrapping_newlines(value: str) -> str:
    """Strip one leading and one trailing newline (the Qwen3 template markup)."""
    if value.startswith("\n"):
        value = value[1:]
    if value.endswith("\n"):
        value = value[:-1]
    return value