[WIP] support MiniMax M2 model (#12129)

Signed-off-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
Signed-off-by: xuebi <xuebi@minimaxi.com>
Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com>
Co-authored-by: Roger Young <42564206+rogeryoungh@users.noreply.github.com>
Co-authored-by: xuebi <xuebi@minimaxi.com>

docs/supported_models/generative_models.md

@@ -35,6 +35,7 @@ in the GitHub search bar.
 | **MiniCPM** (v3, 4B)               | `openbmb/MiniCPM3-4B`                            | OpenBMB’s series of compact LLMs for edge devices; MiniCPM 3 (4B) achieves GPT-3.5-level results in text tasks. |
 | **OLMo** (2, 3)               | `allenai/OLMo-2-1124-7B-Instruct`                       | Allen AI’s series of Open Language Models designed to enable the science of language models. |
 | **OLMoE** (Open MoE)               | `allenai/OLMoE-1B-7B-0924`                       | Allen AI’s open Mixture-of-Experts model (7B total, 1B active parameters) delivering state-of-the-art results with sparse expert activation. |
+| **MiniMax-M2**                     | `minimax/MiniMax-M2`           | MiniMax’s SOTA LLM for coding & agentic workflows. |
 | **StableLM** (3B, 7B)               | `stabilityai/stablelm-tuned-alpha-7b`            | StabilityAI’s early open-source LLM (3B & 7B) for general text generation; a demonstration model with basic instruction-following ability. |
 | **Command-R** (Cohere)              | `CohereForAI/c4ai-command-r-v01`                 | Cohere’s open conversational LLM (Command series) optimized for long context, retrieval-augmented generation, and tool use. |
 | **DBRX** (Databricks)              | `databricks/dbrx-instruct`                       | Databricks’ 132B-parameter MoE model (36B active) trained on 12T tokens; competes with GPT-3.5 quality as a fully open foundation model. |

python/sglang/srt/function_call/function_call_parser.py

@@ -16,6 +16,7 @@ from sglang.srt.function_call.glm4_moe_detector import Glm4MoeDetector
 from sglang.srt.function_call.gpt_oss_detector import GptOssDetector
 from sglang.srt.function_call.kimik2_detector import KimiK2Detector
 from sglang.srt.function_call.llama32_detector import Llama32Detector
+from sglang.srt.function_call.minimax_m2 import MinimaxM2Detector
 from sglang.srt.function_call.mistral_detector import MistralDetector
 from sglang.srt.function_call.pythonic_detector import PythonicDetector
 from sglang.srt.function_call.qwen3_coder_detector import Qwen3CoderDetector
@@ -49,6 +50,7 @@ class FunctionCallParser:
         "qwen25": Qwen25Detector,
         "qwen3_coder": Qwen3CoderDetector,
         "step3": Step3Detector,
+        "minimax-m2": MinimaxM2Detector,
     }
 
     def __init__(self, tools: List[Tool], tool_call_parser: str):

python/sglang/srt/function_call/minimax_m2.py

+import ast
+import html
+import json
+import logging
+import re
+from typing import Any, Dict, List, Tuple
+
+from sglang.srt.entrypoints.openai.protocol import Tool
+from sglang.srt.function_call.base_format_detector import BaseFormatDetector
+from sglang.srt.function_call.core_types import (
+    StreamingParseResult,
+    ToolCallItem,
+    _GetInfoFunc,
+)
+from sglang.srt.function_call.ebnf_composer import EBNFComposer
+
+logger = logging.getLogger(__name__)
+
+
+def _safe_val(raw: str) -> Any:
+    raw = html.unescape(raw.strip())
+    try:
+        return json.loads(raw)
+    except Exception:
+        try:
+            return ast.literal_eval(raw)
+        except Exception:
+            return raw
+
+
+class MinimaxM2Detector(BaseFormatDetector):
+    """
+    Detector for MiniMax M2 models.
+    Assumes function call format:
+        <minimax:tool_call>
+        <invoke name="func1">
+        <parameter name="param1">value1</parameter>
+        <parameter name="param2">value2</parameter>
+        </invoke>
+        </minimax:tool_call>
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.tool_call_start_token: str = "<minimax:tool_call>"
+        self.tool_call_end_token: str = "</minimax:tool_call>"
+        self.tool_call_prefix: str = '<invoke name="'
+        self.tool_call_function_end_token: str = "</invoke>"
+        self.tool_call_regex = re.compile(
+            r"<minimax:tool_call>(.*?)</minimax:tool_call>|<minimax:tool_call>(.*?)$",
+            re.DOTALL,
+        )
+        self.tool_call_function_regex = re.compile(
+            r"<invoke name=\"(.*?)</invoke>|<invoke name=\"(.*)$", re.DOTALL
+        )
+        self.tool_call_parameter_regex = re.compile(
+            r"<parameter name=\"(.*?)</parameter>|<parameter name=\"(.*?)$", re.DOTALL
+        )
+        self._buf: str = ""
+
+        # Streaming state variables
+        self._current_function_name: str = ""
+        self._current_parameters: Dict[str, Any] = {}
+        self._streamed_parameters: Dict[str, str] = (
+            {}
+        )  # Track what parameter content we've streamed
+        self._in_tool_call: bool = False
+        self._function_name_sent: bool = False
+
+    def has_tool_call(self, text: str) -> bool:
+        return self.tool_call_start_token in text
+
+    def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
+        normal, calls = self._extract(text, tools)
+        return StreamingParseResult(normal_text=normal, calls=calls)
+
+    def parse_streaming_increment(
+        self, new_text: str, tools: List[Tool]
+    ) -> StreamingParseResult:
+        self._buf += new_text
+        normal = ""
+        calls: List[ToolCallItem] = []
+
+        # Build tool indices for validation
+        if not hasattr(self, "_tool_indices"):
+            self._tool_indices = self._get_tool_indices(tools)
+
+        while True:
+            # If we're not in a tool call and don't see a start token, return normal text
+            if not self._in_tool_call and self.tool_call_start_token not in self._buf:
+                normal += self._buf
+                self._buf = ""
+                break
+
+            # Look for tool call start
+            if not self._in_tool_call:
+                s = self._buf.find(self.tool_call_start_token)
+                if s == -1:
+                    normal += self._buf
+                    self._buf = ""
+                    break
+
+                normal += self._buf[:s]
+                self._buf = self._buf[s:]
+
+                self._in_tool_call = True
+                self._function_name_sent = False
+                self._current_function_name = ""
+                self._current_parameters = {}
+                self._streamed_parameters = {}
+
+                # Remove the start token
+                self._buf = self._buf[len(self.tool_call_start_token) :]
+                continue
+
+            # We're in a tool call, try to parse function name if not sent yet
+            if not self._function_name_sent:
+                # Look for function name pattern: <invoke name=name>
+                function_match = re.search(r"<invoke name=\"([^>]+)\">", self._buf)
+                if function_match:
+                    function_name = function_match.group(1).strip()
+
+                    # Validate function name
+                    if function_name in self._tool_indices:
+                        self._current_function_name = function_name
+                        self._function_name_sent = True
+
+                        # Initialize tool call tracking
+                        if self.current_tool_id == -1:
+                            self.current_tool_id = 0
+
+                        # Ensure tracking arrays are large enough
+                        while len(self.prev_tool_call_arr) <= self.current_tool_id:
+                            self.prev_tool_call_arr.append({})
+                        while len(self.streamed_args_for_tool) <= self.current_tool_id:
+                            self.streamed_args_for_tool.append("")
+
+                        # Store tool call info
+                        self.prev_tool_call_arr[self.current_tool_id] = {
+                            "name": function_name,
+                            "arguments": {},
+                        }
+
+                        # Send tool name with empty parameters
+                        calls.append(
+                            ToolCallItem(
+                                tool_index=self.current_tool_id,
+                                name=function_name,
+                                parameters="",
+                            )
+                        )
+
+                        # Remove the processed function declaration
+                        self._buf = self._buf[function_match.end() :]
+                        continue
+                    else:
+                        # Invalid function name, reset state
+                        logger.warning(f"Invalid function name: {function_name}")
+                        self._reset_streaming_state()
+                        normal += self._buf
+                        self._buf = ""
+                        break
+                else:
+                    # Function name not complete yet, wait for more text
+                    break
+
+            # Parse parameters incrementally
+            if self._function_name_sent:
+                # Process parameters and get any calls to emit
+                parameter_calls = self._parse_and_stream_parameters(self._buf)
+                calls.extend(parameter_calls)
+
+                # Check if tool call is complete
+                if self.tool_call_function_end_token in self._buf:
+                    end_pos = self._buf.find(self.tool_call_function_end_token)
+
+                    # Add closing brace to complete the JSON object
+                    current_streamed = self.streamed_args_for_tool[self.current_tool_id]
+                    if current_streamed:
+                        # Count opening and closing braces to check if JSON is complete
+                        open_braces = current_streamed.count("{")
+                        close_braces = current_streamed.count("}")
+                        if open_braces > close_braces:
+                            calls.append(
+                                ToolCallItem(
+                                    tool_index=self.current_tool_id,
+                                    name=None,
+                                    parameters="}",
+                                )
+                            )
+                            self.streamed_args_for_tool[self.current_tool_id] = (
+                                current_streamed + "}"
+                            )
+
+                    # Complete the tool call
+                    self._buf = self._buf[
+                        end_pos + len(self.tool_call_function_end_token) :
+                    ]
+                    self._reset_streaming_state(True)
+                    self.current_tool_id += 1
+                    continue
+                else:
+                    # Tool call not complete yet, wait for more text
+                    break
+
+        return StreamingParseResult(normal_text=normal, calls=calls)
+
+    def _parse_and_stream_parameters(self, text_to_parse: str) -> List[ToolCallItem]:
+        """
+        Parse complete parameter blocks from text and return any tool call items to emit.
+
+        This method:
+        1. Finds all complete <parameter> blocks
+        2. Parses them into a dictionary
+        3. Compares with current parameters and generates diff if needed
+        4. Updates internal state
+
+        Args:
+            text_to_parse: The text to search for parameter blocks
+
+        Returns:
+            List of ToolCallItem objects to emit (may be empty)
+        """
+        calls: List[ToolCallItem] = []
+
+        # Find all complete parameter patterns
+        param_matches = list(
+            re.finditer(
+                r"<parameter name=\"([^>]+)\">(.*?)</parameter>",
+                text_to_parse,
+                re.DOTALL,
+            )
+        )
+
+        # Build new parameters dictionary
+        new_params = {}
+        for match in param_matches:
+            param_name = match.group(1).strip()
+            param_value = match.group(2)
+            new_params[param_name] = _safe_val(param_value)
+
+        # Calculate parameter diff to stream with proper incremental JSON building
+        if new_params != self._current_parameters:
+            previous_args_json = self.streamed_args_for_tool[self.current_tool_id]
+
+            # Build incremental JSON properly
+            if not self._current_parameters:
+                # First parameter(s) - start JSON object but don't close it yet
+                items = []
+                for key, value in new_params.items():
+                    items.append(
+                        f"{json.dumps(key, ensure_ascii=False)}: {json.dumps(value, ensure_ascii=False)}"
+                    )
+                json_fragment = "{" + ", ".join(items)
+
+                calls.append(
+                    ToolCallItem(
+                        tool_index=self.current_tool_id,
+                        name=None,
+                        parameters=json_fragment,
+                    )
+                )
+                self.streamed_args_for_tool[self.current_tool_id] = json_fragment
+
+            else:
+                # Additional parameters - add them incrementally
+                new_keys = set(new_params.keys()) - set(self._current_parameters.keys())
+                if new_keys:
+                    # Build the continuation part (no closing brace yet)
+                    continuation_parts = []
+                    for key in new_keys:
+                        value = new_params[key]
+                        continuation_parts.append(
+                            f"{json.dumps(key, ensure_ascii=False)}: {json.dumps(value, ensure_ascii=False)}"
+                        )
+
+                    json_fragment = ", " + ", ".join(continuation_parts)
+
+                    calls.append(
+                        ToolCallItem(
+                            tool_index=self.current_tool_id,
+                            name=None,
+                            parameters=json_fragment,
+                        )
+                    )
+                    self.streamed_args_for_tool[self.current_tool_id] = (
+                        previous_args_json + json_fragment
+                    )
+
+            # Update current state
+            self._current_parameters = new_params
+            self.prev_tool_call_arr[self.current_tool_id]["arguments"] = new_params
+
+        return calls
+
+    def _reset_streaming_state(self, still_in_tool_call: bool = False):
+        """Reset streaming state for the next tool call"""
+        self._in_tool_call = still_in_tool_call
+        self._function_name_sent = False
+        self._current_function_name = ""
+        self._current_parameters = {}
+        self._streamed_parameters = {}
+        self.current_tool_name_sent = False
+
+    def _extract(self, text: str, tools: List[Tool]) -> Tuple[str, List[ToolCallItem]]:
+        normal_parts: List[str] = []
+        calls: List[ToolCallItem] = []
+        cursor = 0
+        while True:
+            s = text.find(self.tool_call_start_token, cursor)
+            if s == -1:
+                normal_parts.append(text[cursor:])
+                break
+            normal_parts.append(text[cursor:s])
+            e = text.find(self.tool_call_end_token, s)
+            if e == -1:
+                normal_parts.append(text[s:])
+                break
+            block = text[s : e + len(self.tool_call_end_token)]
+            cursor = e + len(self.tool_call_end_token)
+            calls.extend(self._parse_block(block, tools))
+        return "".join(normal_parts), calls
+
+    def _parse_block(self, block: str, tools: List[Tool]) -> List[ToolCallItem]:
+        res: List[ToolCallItem] = []
+        for m in self.tool_call_function_regex.findall(block):
+            txt = m[0] if m[0] else m[1]
+            if '">' not in txt:
+                continue
+            idx = txt.index('">')
+            fname = txt[:idx].strip()
+            body = txt[idx + 2 :]
+            params: Dict[str, Any] = {}
+            for pm in self.tool_call_parameter_regex.findall(body):
+                ptxt = pm[0] if pm[0] else pm[1]
+                if '">' not in ptxt:
+                    continue
+                pidx = ptxt.index('">')
+                pname = ptxt[:pidx].strip()
+                pval = ptxt[pidx + 2 :].lstrip("\n").rstrip("\n")
+                params[pname] = _safe_val(pval)
+            raw = {"name": fname, "arguments": params}
+            try:
+                # TODO: fix idx in function call, the index for a function
+                # call will always be -1 in parse_base_json
+                res.extend(self.parse_base_json(raw, tools))
+            except Exception:
+                logger.warning("invalid tool call for %s dropped", fname)
+        return res
+
+    def supports_structural_tag(self) -> bool:
+        return False
+
+    def structure_info(self) -> _GetInfoFunc:
+        raise NotImplementedError
+
+    def build_ebnf(self, tools: List[Tool]):
+        return EBNFComposer.build_ebnf(
+            tools,
+            individual_call_start_token=self.tool_call_start_token.replace("\n", "\\n"),
+            individual_call_end_token=self.tool_call_end_token.replace("\n", "\\n"),
+            tool_call_separator="\\n",
+            function_format="xml",
+            call_rule_fmt='"<invoke name=\\"{name}\\">\\n" {arguments_rule} "\\n</invoke>"',
+            key_value_rule_fmt='"<parameter name=\\"{key}\\">\\n" {valrule} "\\n</parameter>"',
+            key_value_separator='"\\n"',
+        )

python/sglang/srt/parser/reasoning_parser.py

@@ -249,6 +249,31 @@ class GptOssDetector(BaseReasoningFormatDetector):
         )
 
 
+class MiniMaxAppendThinkDetector(BaseReasoningFormatDetector):
+    """
+    Append `<think>` token to the beginning of the text.
+    """
+
+    def __init__(self, stream_reasoning: bool = True, force_reasoning: bool = False):
+        # scheduler.py need `reasoning_parser.detector.think_end_token`
+        super().__init__(
+            "<think>",
+            "</think>",
+            force_reasoning=force_reasoning,
+            stream_reasoning=stream_reasoning,
+        )
+        self.is_first_chunk = False
+
+    def parse_streaming_increment(self, new_text: str) -> StreamingParseResult:
+        if not self.is_first_chunk:
+            self.is_first_chunk = True
+            new_text = self.think_start_token + new_text
+        return StreamingParseResult(normal_text=new_text)
+
+    def detect_and_parse(self, text: str) -> StreamingParseResult:
+        return StreamingParseResult(normal_text=self.think_start_token + text)
+
+
 class ReasoningParser:
     """
     Parser that handles both streaming and non-streaming scenarios for extracting
@@ -268,6 +293,8 @@ class ReasoningParser:
         "kimi": KimiDetector,
         "qwen3": Qwen3Detector,
         "qwen3-thinking": Qwen3Detector,
+        "minimax": Qwen3Detector,
+        "minimax-append-think": MiniMaxAppendThinkDetector,
         "step3": DeepSeekR1Detector,
     }
 
@@ -285,7 +312,7 @@ class ReasoningParser:
             raise ValueError(f"Unsupported model type: {model_type}")
 
         # Special cases where we override force_reasoning
-        if model_type.lower() in {"qwen3-thinking", "gpt-oss"}:
+        if model_type.lower() in {"qwen3-thinking", "gpt-oss", "minimax"}:
             force_reasoning = True
 
         # Only pass force_reasoning if explicitly set, let detectors use their defaults