[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

@@ -0,0 +1,367 @@
+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/models/minimax_m2.py

@@ -0,0 +1,922 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Adapted from DeepSeek and Mixtral implementation
+"""Inference-only MiniMax M2 model compatible with HuggingFace weights."""
+
+import logging
+from typing import Iterable, Optional, Set, Tuple, Union
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.distributed import (
+    get_moe_expert_parallel_world_size,
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
+from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.communicator import (
+    LayerCommunicator,
+    LayerScatterModes,
+    ScatterMode,
+)
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
+from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
+from sglang.srt.layers.moe.topk import TopK
+from sglang.srt.layers.moe.utils import get_moe_a2a_backend
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.utils import PPMissingLayer
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from sglang.srt.server_args import get_global_server_args
+from sglang.srt.two_batch_overlap import model_forward_maybe_tbo
+from sglang.srt.utils import (
+    BumpAllocator,
+    add_prefix,
+    get_compiler_backend,
+    is_non_idle_and_non_empty,
+    make_layers,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class MiniMaxM2RMSNormTP(nn.Module):
+    """RMSNorm with Tensor Parallel support for QK normalization."""
+
+    def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.tp_world = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+
+        # Weight parameter is sharded across TP ranks
+        self.weight = nn.Parameter(torch.ones(int(hidden_size / self.tp_world)))
+        self.weight.weight_loader = self.weight_loader
+        self.variance_epsilon = eps
+
+    @staticmethod
+    def weight_loader(
+        param: nn.Parameter,
+        loaded_weight: torch.Tensor,
+    ) -> None:
+        """Custom weight loader that handles TP sharding."""
+        tp_world = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+
+        shard_size = loaded_weight.shape[0] // tp_world
+        shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
+        param.data.copy_(loaded_weight[shard])
+
+    @torch.compile(dynamic=True, backend=get_compiler_backend())
+    def forward(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """Forward pass with TP-aware variance computation."""
+        assert residual is None, "RMSNormTP does not support residual connection."
+
+        orig_dtype = x.dtype
+        x = x.to(torch.float32)
+
+        # Compute variance across the full dimension (not just local shard)
+        variance = x.pow(2).mean(dim=-1, keepdim=True, dtype=torch.float32)
+
+        if self.tp_world > 1:
+            # All-reduce variance across TP ranks to get global variance
+            variance = tensor_model_parallel_all_reduce(variance) / self.tp_world
+
+        # Normalize and apply local weight shard
+        x = x * torch.rsqrt(variance + self.variance_epsilon)
+        x = x.to(orig_dtype) * self.weight
+
+        return x
+
+
+class MiniMaxM2MLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "mlp",
+    ) -> None:
+        super().__init__()
+
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("gate_up_proj", prefix),
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("down_proj", prefix),
+        )
+        self.act_fn = SiluAndMul()
+        return
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class MiniMaxM2MoE(nn.Module):
+    """MiniMax MoE implementation using DeepEP for Expert Parallel support."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        if self.tp_size > config.num_local_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.num_local_experts}."
+            )
+        self.use_routing_bias = getattr(config, "use_routing_bias", False)
+        if self.use_routing_bias:
+            self.e_score_correction_bias = nn.Parameter(
+                torch.empty(config.num_local_experts, dtype=torch.float32)
+            )
+            self.e_score_correction_bias.weight_loader = (
+                MiniMaxM2MoE.ebias_weight_loader
+            )
+        else:
+            self.e_score_correction_bias = None
+
+        self.experts = get_moe_impl_class(quant_config)(
+            num_experts=config.num_local_experts
+            + get_global_server_args().ep_num_redundant_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=add_prefix("experts", prefix),
+        )
+        self.topk = TopK(
+            top_k=config.num_experts_per_tok,
+            renormalize=True,
+            scoring_func=config.scoring_func,
+            use_grouped_topk=True,  # TODO: Use "grouped top-k" flag only for hardcoded sigmoid scoring
+            num_expert_group=1,
+            topk_group=1,
+            correction_bias=self.e_score_correction_bias,
+            routed_scaling_factor=1.0,
+        )
+
+        self.gate = ReplicatedLinear(
+            config.hidden_size,
+            config.num_local_experts,
+            bias=False,
+            params_dtype=torch.float32,
+            quant_config=None,
+            prefix=add_prefix("gate", prefix),
+        )
+
+        self.layer_id = layer_id
+
+        if get_moe_a2a_backend().is_deepep():
+            self.ep_size = get_moe_expert_parallel_world_size()
+            self.top_k = config.num_experts_per_tok
+
+    @staticmethod
+    def ebias_weight_loader(param: nn.Parameter, loaded_weight: torch.Tensor) -> None:
+        assert param.size() == loaded_weight.size()
+        param.data.copy_(loaded_weight.to(torch.float32))
+
+    def forward(
+        self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
+    ) -> torch.Tensor:
+        if get_moe_a2a_backend().is_deepep():
+            return self.forward_deepep(hidden_states, forward_batch)
+        else:
+            return self.forward_normal(hidden_states)
+
+    def forward_normal(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states.to(torch.float32))
+        topk_output = self.topk(hidden_states, router_logits)
+
+        final_hidden_states = self.experts(hidden_states, topk_output)
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
+
+        return final_hidden_states.view(num_tokens, hidden_dim)
+
+    def forward_deepep(
+        self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
+    ) -> torch.Tensor:
+        if hidden_states.shape[0] > 0:
+            # router_logits: (num_tokens, n_experts)
+            router_logits, _ = self.gate(hidden_states.to(torch.float32))
+            topk_weights, topk_idx, _ = self.topk(
+                hidden_states,
+                router_logits,
+                num_token_non_padded=forward_batch.num_token_non_padded,
+                expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
+                    layer_id=self.layer_id,
+                ),
+            )
+        else:
+            topk_weights, topk_idx, _ = self.topk.empty_topk_output(
+                hidden_states.shape[0], self.top_k
+            )
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            topk_idx=topk_idx,
+            topk_weights=topk_weights,
+            forward_batch=forward_batch,
+        )
+
+        return final_hidden_states
+
+    # TBO Operations for MiniMax MoE
+    def op_gate(self, state):
+        """Gate operation for TBO - compute router logits"""
+        if is_non_idle_and_non_empty(
+            state.forward_batch.forward_mode, state.hidden_states_mlp_input
+        ):  # router_logits: (num_tokens, num_experts)
+            state.router_logits, _ = self.gate(state.hidden_states_mlp_input)
+        else:
+            state.router_logits = None
+
+    def op_select_experts(self, state):
+        """Expert selection operation for TBO"""
+        router_logits = state.pop("router_logits")
+        hidden_states = state.hidden_states_mlp_input
+
+        if router_logits is not None:
+            with get_global_expert_distribution_recorder().with_current_layer(
+                self.layer_id
+            ):
+                state.topk_weights_local, state.topk_idx_local, _ = self.topk(
+                    hidden_states=hidden_states,
+                    router_logits=router_logits,
+                    num_token_non_padded=state.forward_batch.num_token_non_padded,
+                    expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
+                        layer_id=self.layer_id,
+                    ),
+                )
+        else:
+            state.topk_idx_local = torch.full(
+                (0, self.top_k), -1, dtype=torch.int, device=hidden_states.device
+            )
+            state.topk_weights_local = torch.empty(
+                (0, self.top_k), dtype=torch.float32, device=hidden_states.device
+            )
+
+    def op_dispatch_a(self, state):
+        """Dispatch A operation for TBO - start async dispatch"""
+        if self.ep_size > 1:
+            self.experts.deepep_dispatcher.dispatch_a(
+                hidden_states=state.pop("hidden_states_mlp_input"),
+                topk_idx=state.pop("topk_idx_local"),
+                topk_weights=state.pop("topk_weights_local"),
+                forward_batch=state.forward_batch,
+                tbo_subbatch_index=state.get("tbo_subbatch_index"),
+            )
+
+    def op_dispatch_b(self, state):
+        """Dispatch B operation for TBO - complete async dispatch"""
+        if self.ep_size > 1:
+            with get_global_expert_distribution_recorder().with_current_layer(
+                self.layer_id
+            ):
+                state.dispatch_output = self.experts.deepep_dispatcher.dispatch_b(
+                    tbo_subbatch_index=state.get("tbo_subbatch_index"),
+                )
+
+    def op_experts(self, state):
+        """Expert computation for TBO"""
+        state.hidden_states_experts_output = self.experts.moe_impl(
+            dispatch_output=state.dispatch_output,
+        )
+
+    def op_combine_a(self, state):
+        """Combine A operation for TBO - start async combine"""
+        if self.ep_size > 1:
+            self.experts.deepep_dispatcher.combine_a(
+                hidden_states=state.pop("hidden_states_experts_output"),
+                topk_idx=state.dispatch_output.topk_idx,
+                topk_weights=state.dispatch_output.topk_weights,
+                forward_batch=state.forward_batch,
+                tbo_subbatch_index=state.get("tbo_subbatch_index"),
+            )
+            state.pop("dispatch_output")
+
+    def op_combine_b(self, state):
+        """Combine B operation for TBO - complete async combine"""
+        if self.ep_size > 1:
+            state.hidden_states_after_combine = (
+                self.experts.deepep_dispatcher.combine_b(
+                    tbo_subbatch_index=state.get("tbo_subbatch_index"),
+                )
+            )
+
+    def op_output(self, state):
+        """Output operation for TBO - final MLP output"""
+        final_hidden_states = state.pop("hidden_states_after_combine")
+        # MiniMax doesn't have shared experts like DeepSeek, so no need to add them
+        state.hidden_states_mlp_output = final_hidden_states
+
+
+class MiniMaxM2Attention(nn.Module):
+    """MiniMax Attention implementation with QK normalization and partial RoPE."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int = 0,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+
+        # Get dimensions from config
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = config.num_key_value_heads
+
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        # Use head_dim from config if available, otherwise calculate
+        self.head_dim = getattr(
+            config, "head_dim", self.hidden_size // self.total_num_heads
+        )
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+
+        # RoPE settings - support partial RoPE
+        self.rope_theta = getattr(config, "rope_theta", 10000)
+        self.max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+        self.rotary_dim = getattr(
+            config, "rotary_dim", self.head_dim
+        )  # MiniMax uses rotary_dim=64
+
+        # QK Normalization settings
+        self.use_qk_norm = getattr(config, "use_qk_norm", False)
+        self.qk_norm_type = getattr(config, "qk_norm_type", "per_layer")
+
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("qkv_proj", prefix),
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=False,
+            reduce_results=False,
+            quant_config=quant_config,
+            prefix=add_prefix("o_proj", prefix),
+        )
+
+        # Setup RoPE with partial rotary dimension
+        rope_scaling = getattr(config, "rope_scaling", None)
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.rotary_dim,  # Use partial rotary dimension
+            max_position=self.max_position_embeddings,
+            base=self.rope_theta,
+            rope_scaling=rope_scaling,
+        )
+
+        # QK Normalization layers
+        if self.use_qk_norm:
+            if self.qk_norm_type == "per_layer":
+                # Use RMSNormTP for proper tensor parallel support
+                # Use total dimensions (before TP sharding) for correct normalization
+                self.q_norm = MiniMaxM2RMSNormTP(
+                    self.total_num_heads * self.head_dim, eps=config.rms_norm_eps
+                )
+                self.k_norm = MiniMaxM2RMSNormTP(
+                    self.total_num_kv_heads * self.head_dim, eps=config.rms_norm_eps
+                )
+            else:
+                raise ValueError(f"Unsupported qk_norm_type: {self.qk_norm_type}")
+
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=add_prefix("attn", prefix),
+        )
+
+    def forward_prepare(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ):
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if self.use_qk_norm:
+            q = self.q_norm(q.contiguous())
+            k = self.k_norm(k.contiguous())
+        else:
+            q, k = q.contiguous(), k.contiguous()
+        q, k = self.rotary_emb(positions, q, k)
+        inner_state = q, k, v, forward_batch
+        return None, forward_batch, inner_state
+
+    def forward_core(self, intermediate_state):
+        _, _, inner_state = intermediate_state
+        attn_output = self.attn(*inner_state)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        s = self.forward_prepare(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+        return self.forward_core(s)
+
+    def op_prepare(self, state):
+        state.attn_intermediate_state = self.forward_prepare(
+            positions=state.positions,
+            hidden_states=state.pop("hidden_states_after_comm_pre_attn"),
+            forward_batch=state.forward_batch,
+        )
+
+    def op_core(self, state):
+        state.hidden_states_after_attn = self.forward_core(
+            state.pop("attn_intermediate_state")
+        )
+
+
+class MiniMaxM2DecoderLayer(nn.Module):
+    """MiniMax Decoder Layer implementation with MoE support."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.layer_id = layer_id
+
+        # TBO support: All MiniMax layers are sparse (MoE)
+        self.is_layer_sparse = True
+
+        self.self_attn = MiniMaxM2Attention(
+            config=config,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=add_prefix("self_attn", prefix),
+        )
+
+        self.block_sparse_moe = MiniMaxM2MoE(
+            config=config,
+            layer_id=layer_id,
+            quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
+        )
+
+        self.input_layernorm = RMSNorm(
+            config.hidden_size, eps=getattr(config, "rms_norm_eps", 1e-6)
+        )
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=getattr(config, "rms_norm_eps", 1e-6)
+        )
+
+        is_previous_layer_sparse = True
+        self.layer_scatter_modes = LayerScatterModes.init_new(
+            layer_id=layer_id,
+            num_layers=config.num_hidden_layers,
+            is_layer_sparse=self.is_layer_sparse,
+            is_previous_layer_sparse=is_previous_layer_sparse,
+        )
+
+        self.layer_communicator = LayerCommunicator(
+            layer_scatter_modes=self.layer_scatter_modes,
+            input_layernorm=self.input_layernorm,
+            post_attention_layernorm=self.post_attention_layernorm,
+            allow_reduce_scatter=True,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        # Self Attention
+        hidden_states, residual = self.layer_communicator.prepare_attn(
+            hidden_states, residual, forward_batch
+        )
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+
+        # Fully Connected (MLP or MoE)
+
+        hidden_states, residual = self.layer_communicator.prepare_mlp(
+            hidden_states, residual, forward_batch
+        )
+
+        hidden_states = self.block_sparse_moe(hidden_states, forward_batch)
+
+        hidden_states, residual = self.layer_communicator.postprocess_layer(
+            hidden_states, residual, forward_batch
+        )
+
+        return hidden_states, residual
+
+    # TBO Operations for MiniMax Decoder Layer
+    def op_comm_prepare_attn(
+        self,
+        state,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        residual: Optional[torch.Tensor],
+        zero_allocator: BumpAllocator,
+        tbo_subbatch_index: Optional[int] = None,
+    ):
+        """Communication prepare for attention - TBO operation"""
+        state.hidden_states_after_comm_pre_attn, state.residual_after_input_ln = (
+            self.layer_communicator.prepare_attn(hidden_states, residual, forward_batch)
+        )
+        state.update(
+            dict(
+                forward_batch=forward_batch,
+                positions=positions,
+                zero_allocator=zero_allocator,
+                tbo_subbatch_index=tbo_subbatch_index,
+            )
+        )
+
+    def op_comm_prepare_mlp(self, state):
+        """Communication prepare for MLP - TBO operation"""
+        state.hidden_states_mlp_input, state.residual_after_comm_pre_mlp = (
+            self.layer_communicator.prepare_mlp(
+                state.pop("hidden_states_after_attn"),
+                state.pop("residual_after_input_ln"),
+                state.forward_batch,
+            )
+        )
+
+    def op_mlp(self, state):
+        hidden_states = state.pop("hidden_states_mlp_input")
+        state.hidden_states_mlp_output = self.block_sparse_moe(
+            hidden_states, state.forward_batch
+        )
+
+    def op_comm_postprocess_layer(self, state):
+        """Communication postprocess for layer - TBO operation"""
+        hidden_states, residual = self.layer_communicator.postprocess_layer(
+            state.pop("hidden_states_mlp_output"),
+            state.pop("residual_after_comm_pre_mlp"),
+            state.forward_batch,
+        )
+
+        output = dict(
+            positions=state.positions,
+            hidden_states=hidden_states,
+            residual=residual,
+            forward_batch=state.forward_batch,
+            zero_allocator=state.zero_allocator,
+            tbo_subbatch_index=state.tbo_subbatch_index,
+        )
+        return output
+
+
+class MiniMaxM2Model(nn.Module):
+    """MiniMax Model implementation."""
+
+    fall_back_to_pt_during_load = False
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.padding_idx = getattr(config, "pad_token_id", 0)
+        self.vocab_size = config.vocab_size
+        self.pp_group = get_pp_group()
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+
+        def layer_fn(idx, prefix: str) -> nn.Module:
+            return MiniMaxM2DecoderLayer(
+                config=config,
+                layer_id=idx,
+                quant_config=quant_config,
+                prefix=prefix,
+            )
+
+        self.layers, self.start_layer, self.end_layer = make_layers(
+            config.num_hidden_layers,
+            layer_fn,
+            pp_rank=self.pp_group.rank_in_group,
+            pp_size=self.pp_group.world_size,
+            prefix=add_prefix("layers", prefix),
+        )
+        if self.pp_group.is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer(return_tuple=True)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+    ) -> Union[torch.Tensor, PPProxyTensors]:
+        if self.pp_group.is_first_rank:
+            if input_embeds is None:
+                hidden_states = self.get_input_embeddings(input_ids)
+            else:
+                hidden_states = input_embeds
+            residual = None
+        else:
+            assert pp_proxy_tensors is not None
+            hidden_states = pp_proxy_tensors["hidden_states"]
+            residual = pp_proxy_tensors["residual"]
+
+        if forward_batch.can_run_tbo:
+            hidden_states, residual = model_forward_maybe_tbo(
+                layers=self.layers,
+                enable_tbo=True,
+                input_data_scatter_mode=ScatterMode.model_input_output(),
+                positions=positions,
+                forward_batch=forward_batch,
+                hidden_states=hidden_states,
+                residual=residual,
+            )
+        else:
+            for i in range(self.start_layer, self.end_layer):
+                with get_global_expert_distribution_recorder().with_current_layer(i):
+                    layer = self.layers[i]
+                    hidden_states, residual = layer(
+                        positions=positions,
+                        forward_batch=forward_batch,
+                        hidden_states=hidden_states,
+                        residual=residual,
+                    )
+
+        if not self.pp_group.is_last_rank:
+            return PPProxyTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+
+        if residual is not None:
+            hidden_states, _ = self.norm(hidden_states, residual)
+        else:
+            hidden_states = self.norm(hidden_states)
+
+        return hidden_states
+
+
+class MiniMaxM2ForCausalLM(nn.Module):
+    """MiniMax M2 model for causal language modeling."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.quant_config = quant_config
+
+        self.model = MiniMaxM2Model(
+            config, quant_config, prefix=add_prefix("model", prefix)
+        )
+
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=None,
+                prefix=add_prefix("lm_head", prefix),
+            )
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.logits_processor = LogitsProcessor(config)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        # _print_tensor_info(input_ids, "input_ids")
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        """Load model weights with proper mapping for MiniMax architecture."""
+
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="w1",
+            ckpt_down_proj_name="w2",
+            ckpt_up_proj_name="w3",
+            num_experts=self.config.num_local_experts,
+        )
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
+            if spec_layer is not None:
+                continue  # skip spec decode layers for main model
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if ("mlp.experts." in name) and name not in params_dict:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                    )
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+    @classmethod
+    def get_model_config_for_expert_location(cls, config):
+        from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
+
+        return ModelConfigForExpertLocation(
+            num_layers=config.num_hidden_layers,
+            num_logical_experts=config.num_local_experts,
+            num_groups=None,
+        )
+
+
+def get_spec_layer_idx_from_weight_name(
+    config: PretrainedConfig, weight_name: str
+) -> Optional[int]:
+    if hasattr(config, "num_mtp_modules") and (config.num_mtp_modules > 0):
+        layer_idx = config.num_hidden_layers
+        for i in range(config.num_mtp_modules):
+            if weight_name.startswith(f"model.layers.{layer_idx + i}."):
+                return layer_idx + i
+    return None
+
+
+# Entry class for model registration
+EntryClass = MiniMaxM2ForCausalLM

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