Add qwen3_5

docs/models/supported_models.md

@@ -717,6 +717,8 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `Qwen2VLForConditionalGeneration` | QVQ, Qwen2-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/QVQ-72B-Preview`, `Qwen/Qwen2-VL-7B-Instruct`, `Qwen/Qwen2-VL-72B-Instruct`, etc. | ✅︎ | ✅︎ |
 | `Qwen2_5_VLForConditionalGeneration` | Qwen2.5-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen2.5-VL-3B-Instruct`, `Qwen/Qwen2.5-VL-72B-Instruct`, etc. | ✅︎ | ✅︎ |
 | `Qwen2_5OmniThinkerForConditionalGeneration` | Qwen2.5-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `Qwen/Qwen2.5-Omni-3B`, `Qwen/Qwen2.5-Omni-7B` | ✅︎ | ✅︎ |
+| `Qwen3_5ForConditionalGeneration` | Qwen3.5 | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3.5-9B-Instruct`, etc. | ✅︎ | ✅︎ |
+| `Qwen3_5MoeForConditionalGeneration` | Qwen3.5-MOE | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3.5-35B-A3B-Instruct`, etc. | ✅︎ | ✅︎ |
 | `Qwen3VLForConditionalGeneration` | Qwen3-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3-VL-4B-Instruct`, etc. | ✅︎ | ✅︎ |
 | `Qwen3VLMoeForConditionalGeneration` | Qwen3-VL-MOE | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3-VL-30B-A3B-Instruct`, etc. | ✅︎ | ✅︎ |
 | `Qwen3OmniMoeThinkerForConditionalGeneration` | Qwen3-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `Qwen/Qwen3-Omni-30B-A3B-Instruct`, `Qwen/Qwen3-Omni-30B-A3B-Thinking` | ✅︎ | ✅︎ |

tests/models/registry.py

@@ -943,6 +943,26 @@ _MULTIMODAL_EXAMPLE_MODELS = {
         max_model_len=4096,
         min_transformers_version="4.57",
     ),
+    "Qwen3_5ForConditionalGeneration": _HfExamplesInfo(
+        "Qwen/Qwen3.5-9B-Instruct",
+        max_model_len=4096,
+        min_transformers_version="5.1.0",
+    ),
+    "Qwen3_5MoeForConditionalGeneration": _HfExamplesInfo(
+        "Qwen/Qwen3.5-35B-A3B-Instruct",
+        max_model_len=4096,
+        min_transformers_version="5.1.0",
+    ),
+    "Qwen3_5MTP": _HfExamplesInfo(
+        "Qwen/Qwen3.5-9B-Instruct",
+        speculative_model="Qwen/Qwen3.5-9B-Instruct",
+        min_transformers_version="5.1.0",
+    ),
+    "Qwen3_5MoeMTP": _HfExamplesInfo(
+        "Qwen/Qwen3.5-35B-A3B-Instruct",
+        speculative_model="Qwen/Qwen3.5-35B-A3B-Instruct",
+        min_transformers_version="5.1.0",
+    ),
     "Qwen3OmniMoeForConditionalGeneration": _HfExamplesInfo(
         os.path.join(models_path_prefix, "Qwen/Qwen3-Omni-30B-A3B-Instruct"),
         max_model_len=4096,

vllm/config/model.py

@@ -1250,7 +1250,9 @@ class ModelConfig:
                 return sum(t == 1 for t in attn_type_list[start:end])
 
             # Hybrid model Qwen3Next
-            layer_types_value = getattr(self.hf_config, "layer_types", None)
+            # layer_types_value = getattr(self.hf_config, "layer_types", None)
+            # Hybrid model Qwen3Next Qwen3.5 Series
+            layer_types_value = getattr(self.hf_text_config, "layer_types", None)
             if layer_types_value is not None:
                 if block_type == "attention":
                     return sum(

vllm/config/speculative.py

@@ -37,6 +37,7 @@ MTPModelTypes = Literal[
     "ernie_mtp",
     "exaone_moe_mtp",
     "qwen3_next_mtp",
+    "qwen3_5_mtp",
     "longcat_flash_mtp",
     "mtp",
     "pangu_ultra_moe_mtp",
@@ -246,6 +247,16 @@ class SpeculativeConfig:
                 {"n_predict": n_predict, "architectures": ["ExaoneMoeMTP"]}
             )
 
+        if hf_config.model_type in ("qwen3_5", "qwen3_5_moe"):
+            is_moe = hf_config.model_type == "qwen3_5_moe"
+            hf_config.model_type = "qwen3_5_mtp"
+            n_predict = getattr(hf_config, "mtp_num_hidden_layers", None)
+            hf_config.update(
+                {
+                    "n_predict": n_predict,
+                    "architectures": ["Qwen3_5MoeMTP" if is_moe else "Qwen3_5MTP"],
+                }
+            )
         if hf_config.model_type == "longcat_flash":
             hf_config.model_type = "longcat_flash_mtp"
             n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)

vllm/model_executor/layers/mamba/abstract.py

@@ -43,7 +43,8 @@ class MambaBase(AttentionLayerBase):
     def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec | None:
         if (
             vllm_config.speculative_config is not None
-            and vllm_config.model_config.hf_config.model_type not in ["qwen3_next"]
+            and vllm_config.model_config.hf_config.model_type
+            not in ["qwen3_next", "qwen3_5", "qwen3_5_moe"]
         ):
             raise NotImplementedError(
                 "Mamba with speculative decoding is not supported yet."

vllm/model_executor/models/qwen3_5.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 The vLLM team.
+# Copyright 2025 The Qwen Team.
+# Copyright 2025 The HuggingFace Inc. team.
+# All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+"""Inference-only Qwen3.5 Series compatible with HuggingFace weights."""
+
+import typing
+from collections.abc import Callable, Iterable
+
+import torch
+from einops import rearrange
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.models.qwen3_5.configuration_qwen3_5 import (
+    Qwen3_5Config,
+    Qwen3_5TextConfig,
+)
+from transformers.models.qwen3_5_moe.configuration_qwen3_5_moe import (
+    Qwen3_5MoeConfig,
+    Qwen3_5MoeTextConfig,
+)
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import (
+    CacheConfig,
+    ModelConfig,
+    SpeculativeConfig,
+    VllmConfig,
+    get_current_vllm_config,
+)
+from vllm.distributed import (
+    divide,
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.layernorm import (
+    GemmaRMSNorm as Qwen3_5RMSNorm,
+)
+from vllm.model_executor.layers.layernorm import RMSNormGated
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mamba.mamba_mixer2 import (
+    mamba_v2_sharded_weight_loader,
+)
+from vllm.model_executor.layers.mamba.mamba_utils import (
+    MambaStateCopyFunc,
+    MambaStateCopyFuncCalculator,
+    MambaStateDtypeCalculator,
+    MambaStateShapeCalculator,
+)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    sharded_weight_loader,
+)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.platforms import current_platform
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import (
+    HasInnerState,
+    IsHybrid,
+    MixtureOfExperts,
+    MultiModalEmbeddings,
+    SupportsLoRA,
+    SupportsPP,
+    _require_is_multimodal,
+)
+from .qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP
+from .qwen3_next import (
+    Qwen3NextAttention,
+    Qwen3NextDecoderLayer,
+    Qwen3NextGatedDeltaNet,
+    Qwen3NextModel,
+    Qwen3NextSparseMoeBlock,
+    QwenNextMixtureOfExperts,
+)
+from .qwen3_vl import (
+    Qwen3_VisionTransformer,
+    Qwen3VLDummyInputsBuilder,
+    Qwen3VLForConditionalGeneration,
+    Qwen3VLMultiModalProcessor,
+    Qwen3VLProcessingInfo,
+)
+from .utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    _merge_multimodal_embeddings,
+    extract_layer_index,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+class Qwen3_5ProcessingInfo(Qwen3VLProcessingInfo):
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Qwen3_5Config)
+
+
+class Qwen3_5MoeProcessingInfo(Qwen3VLProcessingInfo):
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Qwen3_5MoeConfig)
+
+
+class Qwen3_5GatedDeltaNet(Qwen3NextGatedDeltaNet):
+    def __init__(
+        self,
+        config: Qwen3_5TextConfig | Qwen3_5MoeTextConfig,
+        model_config: ModelConfig | None = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        speculative_config: SpeculativeConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super(Qwen3NextGatedDeltaNet, self).__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+        self.hidden_size = config.hidden_size
+        self.num_v_heads = config.linear_num_value_heads
+        self.num_k_heads = config.linear_num_key_heads
+        self.head_k_dim = config.linear_key_head_dim
+        self.head_v_dim = config.linear_value_head_dim
+        self.key_dim = self.head_k_dim * self.num_k_heads
+        self.value_dim = self.head_v_dim * self.num_v_heads
+
+        self.conv_kernel_size = config.linear_conv_kernel_dim
+        self.layer_idx = extract_layer_index(prefix)
+        self.activation = config.hidden_act
+        self.act = ACT2FN[config.hidden_act]
+        self.layer_norm_epsilon = config.rms_norm_eps
+        self.prefix = prefix
+
+        self.config = config
+        self.model_config = model_config
+        self.cache_config = cache_config
+        self.quant_config = quant_config
+        self.speculative_config = speculative_config
+        self.num_spec = (
+            self.speculative_config.num_speculative_tokens
+            if self.speculative_config
+            else 0
+        )
+
+        # QKV
+        self.conv_dim = self.key_dim * 2 + self.value_dim
+        self.conv1d = ColumnParallelLinear(
+            input_size=self.conv_kernel_size,
+            output_size=self.conv_dim,
+            bias=False,
+            prefix=f"{prefix}.conv1d",
+        )
+        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
+
+        self.in_proj_qkv = MergedColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_sizes=[self.key_dim, self.key_dim, self.value_dim],
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.in_proj_qkv",
+        )
+        self.in_proj_z = ColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_size=self.value_dim,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.in_proj_z",
+        )
+        self.in_proj_b = ColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_size=self.num_v_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.in_proj_ba",
+        )
+        self.in_proj_a = ColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_size=self.num_v_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.in_proj_a",
+        )
+
+        query_key_settings = (self.key_dim, 0, False)
+        value_settings = (self.value_dim, 0, False)
+
+        delattr(self.conv1d.weight, "weight_loader")
+        set_weight_attrs(
+            self.conv1d.weight,
+            {
+                "weight_loader": mamba_v2_sharded_weight_loader(
+                    [
+                        query_key_settings,
+                        query_key_settings,
+                        value_settings,
+                    ],
+                    self.tp_size,
+                    self.tp_rank,
+                )
+            },
+        )
+
+        # selective projection used to make dt, B and C input dependant
+
+        # time step projection (discretization)
+        # instantiate once and copy inv_dt in init_weights of PretrainedModel
+        self.dt_bias = nn.Parameter(
+            torch.ones(self.num_v_heads // self.tp_size),
+        )
+        self.A_log = nn.Parameter(
+            torch.empty(
+                divide(self.num_v_heads, self.tp_size),
+            )
+        )
+
+        set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(0)})
+        set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
+
+        self.norm = RMSNormGated(
+            self.head_v_dim,
+            eps=self.layer_norm_epsilon,
+            group_size=None,
+            norm_before_gate=True,
+            device=current_platform.current_device(),
+            dtype=config.dtype,
+        )
+
+        self.out_proj = RowParallelLinear(
+            self.value_dim,
+            self.hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+        compilation_config = get_current_vllm_config().compilation_config
+        if prefix in compilation_config.static_forward_context:
+            raise ValueError(f"Duplicate layer name: {prefix}")
+        compilation_config.static_forward_context[prefix] = self
+
+    def fix_query_key_value_ordering(
+        self,
+        mixed_qkv,
+        z,
+        b,
+        a,
+    ):
+        raise NotImplementedError(
+            "Qwen3.5 Series dont need to fix query key value ordering"
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        output: torch.Tensor,
+    ):
+        """
+        Forward pass with three parts:
+        1. Input projection
+        2. Core attention (custom op)
+        3. Output projection
+        """
+        num_tokens = hidden_states.size(0)
+
+        # ============================================================
+        # Part 1: Input Projection
+        # ============================================================
+        mixed_qkv, _ = self.in_proj_qkv(hidden_states)
+        z, _ = self.in_proj_z(hidden_states)
+        z = z.reshape(z.size(0), -1, self.head_v_dim)
+        b, _ = self.in_proj_b(hidden_states)
+        a, _ = self.in_proj_a(hidden_states)
+
+        b = b.contiguous()
+        a = a.contiguous()
+
+        # ============================================================
+        # Part 2: Core Attention (Custom Op)
+        # ============================================================
+        # Note: we should not use torch.empty here like other attention backends,
+        # see discussions in https://github.com/vllm-project/vllm/pull/28182
+        core_attn_out = torch.zeros(
+            (num_tokens, self.num_v_heads // self.tp_size, self.head_v_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+
+        torch.ops.vllm.gdn_attention_core(
+            mixed_qkv,
+            b,
+            a,
+            core_attn_out,
+            self.prefix,
+        )
+
+        # ============================================================
+        # Part 3: Output Projection
+        # ============================================================
+        z_shape_og = z.shape
+        # Reshape input data into 2D tensor
+        core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
+        z = z.reshape(-1, z.shape[-1])
+        core_attn_out = self.norm(core_attn_out, z)
+        core_attn_out = core_attn_out.reshape(z_shape_og)
+        core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
+        output[:num_tokens], _ = self.out_proj(core_attn_out)
+
+
+class Qwen3_5DecoderLayer(Qwen3NextDecoderLayer):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        layer_type: str,
+        prefix: str = "",
+    ) -> None:
+        super(Qwen3NextDecoderLayer, self).__init__()
+
+        config = vllm_config.model_config.hf_text_config
+        model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        speculative_config = vllm_config.speculative_config
+
+        self.layer_type = layer_type
+        self.layer_idx = extract_layer_index(prefix)
+
+        if self.layer_type == "linear_attention":
+            self.linear_attn = Qwen3_5GatedDeltaNet(
+                config,
+                model_config=model_config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                speculative_config=speculative_config,
+                prefix=f"{prefix}.linear_attn",
+            )
+        elif self.layer_type == "full_attention":
+            self.self_attn = Qwen3NextAttention(
+                config,
+                model_config=model_config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.self_attn",
+            )
+        else:
+            raise ValueError(f"Invalid layer_type {self.layer_type}")
+
+        # NOTE: Determine the MLP type based on the model type
+        # Qwen3.5 use all layers for MLP / Qwen3.5-MoE use sparse MoE blocks
+        if config.model_type == "qwen3_5_moe_text":
+            self.mlp = Qwen3NextSparseMoeBlock(
+                vllm_config=vllm_config,
+                prefix=f"{prefix}.mlp",
+            )
+        elif config.model_type == "qwen3_5_text":
+            self.mlp = Qwen3NextMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+        else:
+            raise ValueError(f"Invalid model_type {config.model_type}")
+
+        self.input_layernorm = Qwen3_5RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = Qwen3_5RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+        self.layer_scale = getattr(config, "layer_scale", False)
+        if self.layer_scale:
+            self.attn_layer_scale = torch.nn.Parameter(
+                torch.zeros(
+                    1,
+                    1,
+                    config.hidden_size,
+                    dtype=config.dtype,
+                ),
+            )
+            self.ffn_layer_scale = torch.nn.Parameter(
+                torch.zeros(
+                    1,
+                    1,
+                    config.hidden_size,
+                    dtype=config.dtype,
+                ),
+            )
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
+        # otherwise (seq_len, ).
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+    }
+)
+class Qwen3_5Model(Qwen3NextModel):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super(Qwen3NextModel, self).__init__()
+
+        config: Qwen3_5TextConfig | Qwen3_5MoeTextConfig = (
+            vllm_config.model_config.hf_text_config
+        )
+        parallel_config = vllm_config.parallel_config
+
+        eplb_config = parallel_config.eplb_config
+        self.num_redundant_experts = eplb_config.num_redundant_experts
+
+        self.config = config
+
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+        )
+
+        def get_layer(prefix: str):
+            return Qwen3_5DecoderLayer(
+                vllm_config,
+                layer_type=config.layer_types[extract_layer_index(prefix)],
+                prefix=prefix,
+            )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers"
+        )
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+        if get_pp_group().is_last_rank:
+            self.norm = Qwen3_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+    def load_fused_expert_weights(
+        self,
+        name: str,
+        params_dict: dict,
+        loaded_weight: torch.Tensor,
+        shard_id: str,
+        num_experts: int,
+    ) -> bool:
+        param = params_dict[name]
+        weight_loader = typing.cast(Callable[..., bool], param.weight_loader)
+        loaded_local_expert = False
+        for expert_id in range(num_experts):
+            curr_expert_weight = loaded_weight[expert_id]
+            success = weight_loader(
+                param,
+                curr_expert_weight,
+                name,
+                shard_id,
+                expert_id,
+                return_success=True,
+            )
+            if success:
+                loaded_local_expert = True
+
+        return loaded_local_expert
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        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_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+        is_fused_expert = False
+        fused_expert_params_mapping = [
+            ("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
+            ("experts.w2_weight", "experts.down_proj", 0, "w2"),
+        ]
+        num_experts = (
+            self.config.num_experts if hasattr(self.config, "num_experts") else 0
+        )
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            if name.startswith("mtp."):
+                continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if "experts.gate_up_proj" in name or "experts.down_proj" in name:
+                    is_fused_expert = True
+                    expert_params_mapping = fused_expert_params_mapping
+
+                if weight_name not in name:
+                    continue
+
+                if "mlp.experts" in name:
+                    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
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+                # name = apply_attn_prefix(name, params_dict)
+                if name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                is_expert_weight = False
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    is_expert_weight = True
+                    name_mapped = name.replace(weight_name, param_name)
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name_mapped, self):
+                        continue
+                    if is_fused_expert:
+                        # qwen3.5 no need to transpose
+                        # loaded_weight = loaded_weight.transpose(-1, -2)
+                        if "experts.gate_up_proj" in name:
+                            loaded_weight = loaded_weight.chunk(2, dim=-2)
+                            success_w1 = self.load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_weight[0],
+                                "w1",
+                                num_experts,
+                            )
+                            success_w3 = self.load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_weight[1],
+                                "w3",
+                                num_experts,
+                            )
+                            success = success_w1 and success_w3
+                        else:
+                            # down_proj
+                            success = self.load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_weight,
+                                shard_id,
+                                num_experts,
+                            )
+                        if success:
+                            name = name_mapped
+                            break
+                    else:
+                        # Skip loading extra bias for GPTQ models.
+                        if (
+                            name_mapped.endswith(".bias")
+                            or name_mapped.endswith("_bias")
+                        ) and name_mapped not in params_dict:
+                            continue
+                        param = params_dict[name_mapped]
+                        weight_loader = param.weight_loader
+                        success = weight_loader(
+                            param,
+                            loaded_weight,
+                            name_mapped,
+                            shard_id=shard_id,
+                            expert_id=expert_id,
+                            return_success=True,
+                        )
+                    if success:
+                        name = name_mapped
+                        break
+                else:
+                    if is_expert_weight:
+                        # We've checked that this is an expert weight
+                        # However it's not mapped locally to this rank
+                        # So we simply skip it
+                        continue
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    if name not in params_dict:
+                        logger.warning_once(
+                            f"Parameter {name} not found in params_dict, skip loading"
+                        )
+                        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
+
+
+class Qwen3_5ForCausalLMBase(
+    nn.Module,
+    HasInnerState,
+    SupportsLoRA,
+    SupportsPP,
+):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_text_config
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
+
+        scheduler_config = vllm_config.scheduler_config
+        if cache_config.mamba_cache_mode == "all":
+            raise NotImplementedError(
+                "Qwen3.5 currently does not support 'all' prefix caching, "
+                "please use '--mamba-cache-mode=align' instead"
+            )
+        self.quant_config = vllm_config.quant_config
+
+        super().__init__()
+        self.config = config
+        self.scheduler_config = scheduler_config
+        self.model = Qwen3_5Model(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+
+        if get_pp_group().is_last_rank:
+            if config.tie_word_embeddings:
+                self.lm_head = self.model.embed_tokens
+            else:
+                self.lm_head = ParallelLMHead(
+                    config.vocab_size,
+                    config.hidden_size,
+                    prefix=maybe_prefix(prefix, "lm_head"),
+                )
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs: object,
+    ):
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        return self.logits_processor(self.lm_head, hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=["mtp."],
+        )
+        return loader.load_weights(weights)
+
+
+class Qwen3_5ForCausalLM(Qwen3_5ForCausalLMBase):
+    pass
+
+
+class Qwen3_5MoeForCausalLM(Qwen3_5ForCausalLMBase, QwenNextMixtureOfExperts):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        # set MoE hyperparameters
+        self.set_moe_parameters()
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
+
+
+########################################################
+# Qwen3_5-Dense
+########################################################
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Qwen3VLMultiModalProcessor,
+    info=Qwen3_5ProcessingInfo,
+    dummy_inputs=Qwen3VLDummyInputsBuilder,
+)
+class Qwen3_5ForConditionalGeneration(Qwen3VLForConditionalGeneration, IsHybrid):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        # protocols have not __init__ method, so we need to use nn.Module.__init__
+        nn.Module.__init__(self)
+        config: Qwen3_5Config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
+        self.video_pruning_rate = multimodal_config.video_pruning_rate
+        self.is_multimodal_pruning_enabled = (
+            multimodal_config.is_multimodal_pruning_enabled()
+        )
+
+        with self._mark_tower_model(vllm_config, {"image", "video"}):
+            self.visual = Qwen3_VisionTransformer(
+                config.vision_config,
+                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "visual"),
+            )
+
+        with self._mark_language_model(vllm_config):
+            self.language_model = Qwen3_5ForCausalLM(
+                vllm_config=vllm_config, prefix=maybe_prefix(prefix, "language_model")
+            )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: MultiModalEmbeddings | None = None,
+        *,
+        is_multimodal: torch.Tensor | None = None,
+        handle_oov_mm_token: bool = False,
+    ) -> torch.Tensor:
+        inputs_embeds = self._embed_text_input_ids(
+            input_ids,
+            self.language_model.embed_input_ids,
+            is_multimodal=is_multimodal,
+            handle_oov_mm_token=handle_oov_mm_token,
+        )
+
+        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
+            return inputs_embeds
+
+        is_multimodal = _require_is_multimodal(is_multimodal)
+
+        inputs_embeds = _merge_multimodal_embeddings(
+            inputs_embeds=inputs_embeds,
+            multimodal_embeddings=multimodal_embeddings,
+            is_multimodal=is_multimodal,
+        )
+
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs: object,
+    ) -> torch.Tensor | IntermediateTensors:
+        """Run forward pass for Qwen3.5.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            positions: Flattened (concatenated) position ids corresponding to a
+                batch.
+                **NOTE**: If mrope is enabled (default setting for Qwen3VL
+                opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+            intermediate_tensors: Intermediate tensors from previous pipeline
+                stages.
+            inputs_embeds: Pre-computed input embeddings.
+            **kwargs: Additional keyword arguments including:
+                - pixel_values: Pixel values to be fed to a model.
+                    `None` if no images are passed.
+                - image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in
+                    LLM. `None` if no images are passed.
+                - pixel_values_videos: Pixel values of videos to be fed to a
+                    model. `None` if no videos are passed.
+                - video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in
+                    LLM. `None` if no videos are passed.
+        """
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=["mtp."],
+        )
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+        return MambaStateDtypeCalculator.gated_delta_net_state_dtype(
+            vllm_config.model_config.dtype, vllm_config.cache_config.mamba_cache_dtype
+        )
+
+    @classmethod
+    def get_mamba_state_shape_from_config(
+        cls, vllm_config: "VllmConfig"
+    ) -> tuple[tuple[int, int], tuple[int, int]]:
+        parallel_config = vllm_config.parallel_config
+        hf_config = vllm_config.model_config.hf_text_config
+        tp_size = parallel_config.tensor_parallel_size
+        num_spec = (
+            vllm_config.speculative_config.num_speculative_tokens
+            if vllm_config.speculative_config
+            else 0
+        )
+        return MambaStateShapeCalculator.gated_delta_net_state_shape(
+            tp_size,
+            hf_config.linear_num_key_heads,
+            hf_config.linear_num_value_heads,
+            hf_config.linear_key_head_dim,
+            hf_config.linear_value_head_dim,
+            hf_config.linear_conv_kernel_dim,
+            num_spec,
+        )
+
+    @classmethod
+    def get_mamba_state_copy_func(cls) -> tuple[MambaStateCopyFunc, MambaStateCopyFunc]:
+        return MambaStateCopyFuncCalculator.gated_delta_net_state_copy_func()
+
+
+########################################################
+# Qwen3_5-MoE
+########################################################
+
+
+class Qwen3_5_MoeMixtureOfExperts(MixtureOfExperts):
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        assert self.num_local_physical_experts == num_local_physical_experts
+        self.num_physical_experts = num_physical_experts
+        self.num_local_physical_experts = num_local_physical_experts
+        self.num_redundant_experts = num_physical_experts - self.num_logical_experts
+        for layer in self.language_model.model.layers:
+            if isinstance(layer.mlp, Qwen3NextSparseMoeBlock):
+                moe = layer.mlp
+                moe.n_local_physical_experts = num_local_physical_experts
+                moe.n_physical_experts = num_physical_experts
+                moe.n_redundant_experts = self.num_redundant_experts
+                moe.experts.update_expert_map()
+
+    def set_moe_parameters(self):
+        self.expert_weights = []
+
+        self.moe_layers = []
+        example_moe = None
+        for layer in self.language_model.model.layers:
+            if isinstance(layer, Qwen3_5DecoderLayer) and isinstance(
+                layer.mlp, Qwen3NextSparseMoeBlock
+            ):
+                example_moe = layer.mlp
+                self.moe_layers.append(layer.mlp.experts)
+
+        if example_moe is None:
+            raise RuntimeError(
+                "No Qwen3_5 layer found in the language_model.model.layers."
+            )
+
+        # Set MoE hyperparameters
+        self.num_moe_layers = len(self.moe_layers)
+        self.num_expert_groups = 1
+        self.num_shared_experts = 0
+        self.num_logical_experts = example_moe.n_logical_experts
+        self.num_physical_experts = example_moe.n_physical_experts
+        self.num_local_physical_experts = example_moe.n_local_physical_experts
+        self.num_routed_experts = example_moe.n_routed_experts
+        self.num_redundant_experts = example_moe.n_redundant_experts
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Qwen3VLMultiModalProcessor,
+    info=Qwen3_5MoeProcessingInfo,
+    dummy_inputs=Qwen3VLDummyInputsBuilder,
+)
+class Qwen3_5MoeForConditionalGeneration(
+    Qwen3_5ForConditionalGeneration, Qwen3_5_MoeMixtureOfExperts
+):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        # protocols have not __init__ method, so we need to use nn.Module.__init__
+        nn.Module.__init__(self)
+        config: Qwen3_5MoeConfig = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
+        self.video_pruning_rate = multimodal_config.video_pruning_rate
+        self.is_multimodal_pruning_enabled = (
+            multimodal_config.is_multimodal_pruning_enabled()
+        )
+
+        with self._mark_tower_model(vllm_config, {"image", "video"}):
+            self.visual = Qwen3_VisionTransformer(
+                config.vision_config,
+                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "visual"),
+            )
+
+        with self._mark_language_model(vllm_config):
+            self.language_model = Qwen3_5MoeForCausalLM(
+                vllm_config=vllm_config, prefix=maybe_prefix(prefix, "language_model")
+            )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+
+        # set MoE hyperparameters
+        self.set_moe_parameters()

vllm/model_executor/models/qwen3_5_mtp.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only Qwen3_5 MTP model."""
+
+import typing
+from collections.abc import Callable, Iterable
+
+import torch
+from torch import nn
+from transformers.models.qwen3_5.configuration_qwen3_5 import Qwen3_5TextConfig
+from transformers.models.qwen3_5_moe.configuration_qwen3_5_moe import (
+    Qwen3_5MoeTextConfig,
+)
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import VllmConfig
+from vllm.distributed.parallel_state import get_pp_group
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.linear import ColumnParallelLinear
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.qwen3_5 import Qwen3_5DecoderLayer, Qwen3_5RMSNorm
+from vllm.model_executor.models.qwen3_next import QwenNextMixtureOfExperts
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import (
+    MultiModalEmbeddings,
+    SupportsMultiModal,
+    _require_is_multimodal,
+)
+from .utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    _merge_multimodal_embeddings,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    maybe_prefix,
+)
+
+logger = init_logger(__name__)
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
+        # otherwise (seq_len, ).
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+        "hidden_states": 0,
+    }
+)
+class Qwen3_5MultiTokenPredictor(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        model_config = vllm_config.model_config
+        quant_config = vllm_config.quant_config
+
+        config: Qwen3_5TextConfig | Qwen3_5MoeTextConfig = model_config.hf_text_config
+
+        self.config = config
+
+        self.vocab_size = config.vocab_size
+
+        self.mtp_start_layer_idx = config.num_hidden_layers
+        self.num_mtp_layers = getattr(config, "mtp_num_hidden_layers", 1)
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+        )
+
+        self.fc = ColumnParallelLinear(
+            self.config.hidden_size * 2,
+            self.config.hidden_size,
+            gather_output=True,
+            bias=False,
+            return_bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc",
+        )
+
+        self.layers = torch.nn.ModuleList(
+            Qwen3_5DecoderLayer(
+                vllm_config,
+                layer_type="full_attention",
+                prefix=f"{prefix}.layers.{idx}",
+            )
+            for idx in range(self.num_mtp_layers)
+        )
+
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+        self.norm = Qwen3_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.pre_fc_norm_hidden = Qwen3_5RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.pre_fc_norm_embedding = Qwen3_5RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is None:
+                inputs_embeds = self.embed_input_ids(input_ids)
+            assert hidden_states.shape[-1] == inputs_embeds.shape[-1]
+            inputs_embeds = self.pre_fc_norm_embedding(inputs_embeds)
+            hidden_states = self.pre_fc_norm_hidden(hidden_states)
+            hidden_states = torch.cat([inputs_embeds, hidden_states], dim=-1)
+            hidden_states = self.fc(hidden_states)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        current_step_idx = spec_step_idx % self.num_mtp_layers
+        hidden_states, residual = self.layers[current_step_idx](
+            positions=positions,
+            hidden_states=hidden_states,
+            residual=residual,
+        )
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_fused_expert_weights(
+        self,
+        name: str,
+        params_dict: dict,
+        loaded_weight: torch.Tensor,
+        shard_id: str,
+        num_experts: int,
+    ) -> bool:
+        param = params_dict[name]
+        weight_loader = typing.cast(Callable[..., bool], param.weight_loader)
+        loaded_local_expert = False
+        for expert_id in range(num_experts):
+            curr_expert_weight = loaded_weight[expert_id]
+            success = weight_loader(
+                param,
+                curr_expert_weight,
+                name,
+                shard_id,
+                expert_id,
+                return_success=True,
+            )
+            if success:
+                loaded_local_expert = True
+
+        return loaded_local_expert
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        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(
+            self,
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts
+            if hasattr(self.config, "num_experts")
+            else 0,
+        )
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        is_fused_expert = False
+        fused_expert_params_mapping = [
+            ("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
+            ("experts.w2_weight", "experts.down_proj", 0, "w2"),
+        ]
+        num_experts = (
+            self.config.num_experts if hasattr(self.config, "num_experts") else 0
+        )
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if "experts.gate_up_proj" in name or "experts.down_proj" in name:
+                    is_fused_expert = True
+                    expert_params_mapping = fused_expert_params_mapping
+
+                if weight_name not in name:
+                    continue
+
+                if "mlp.experts" in name:
+                    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
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+                if name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                is_expert_weight = False
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    is_expert_weight = True
+                    name_mapped = name.replace(weight_name, param_name)
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name_mapped, self):
+                        continue
+                    if is_fused_expert:
+                        # qwen3.5 no need to transpose
+                        # loaded_weight = loaded_weight.transpose(-1, -2)
+                        if "experts.gate_up_proj" in name:
+                            loaded_weight = loaded_weight.chunk(2, dim=-2)
+                            success_w1 = self.load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_weight[0],
+                                "w1",
+                                num_experts,
+                            )
+                            success_w3 = self.load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_weight[1],
+                                "w3",
+                                num_experts,
+                            )
+                            success = success_w1 and success_w3
+                        else:
+                            # down_proj
+                            success = self.load_fused_expert_weights(
+                                name_mapped,
+                                params_dict,
+                                loaded_weight,
+                                shard_id,
+                                num_experts,
+                            )
+                        if success:
+                            name = name_mapped
+                            break
+                    else:
+                        # Skip loading extra bias for GPTQ models.
+                        if (
+                            name_mapped.endswith(".bias")
+                            or name_mapped.endswith("_bias")
+                        ) and name_mapped not in params_dict:
+                            continue
+                        param = params_dict[name_mapped]
+                        weight_loader = param.weight_loader
+                        success = weight_loader(
+                            param,
+                            loaded_weight,
+                            name_mapped,
+                            shard_id=shard_id,
+                            expert_id=expert_id,
+                            return_success=True,
+                        )
+                    if success:
+                        name = name_mapped
+                        break
+                else:
+                    if is_expert_weight:
+                        # We've checked that this is an expert weight
+                        # However it's not mapped locally to this rank
+                        # So we simply skip it
+                        continue
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    if name not in params_dict:
+                        logger.warning_once(
+                            f"Parameter {name} not found in params_dict, skip loading"
+                        )
+                        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
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
+        # otherwise (seq_len, ).
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+        "hidden_states": 0,
+    }
+)
+class Qwen3_5MTP(nn.Module, SupportsMultiModal):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": ["up_proj", "down_proj"],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_text_config
+        self.vllm_config = vllm_config
+        cache_config = vllm_config.cache_config
+        if cache_config.mamba_cache_mode == "all":
+            raise NotImplementedError(
+                "Qwen3_5MTP currently does not support 'all' prefix caching, "
+                "please use '--mamba-cache-mode=align' instead"
+            )
+
+        self.quant_config = vllm_config.quant_config
+
+        super().__init__()
+        self.config = config
+        self.model = Qwen3_5MultiTokenPredictor(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "mtp")
+        )
+
+        if get_pp_group().is_last_rank:
+            if config.tie_word_embeddings:
+                self.lm_head = self.model.embed_tokens
+            else:
+                self.lm_head = ParallelLMHead(
+                    config.vocab_size,
+                    config.hidden_size,
+                    prefix=maybe_prefix(prefix, "lm_head"),
+                )
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+    def embed_input_ids(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: MultiModalEmbeddings | None = None,
+        *,
+        is_multimodal: torch.Tensor | None = None,
+        handle_oov_mm_token: bool = False,
+    ) -> torch.Tensor:
+        inputs_embeds = self._embed_text_input_ids(
+            input_ids,
+            self.model.embed_input_ids,
+            is_multimodal=is_multimodal,
+            handle_oov_mm_token=handle_oov_mm_token,
+        )
+
+        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
+            return inputs_embeds
+
+        is_multimodal = _require_is_multimodal(is_multimodal)
+
+        inputs_embeds = _merge_multimodal_embeddings(
+            inputs_embeds=inputs_embeds,
+            multimodal_embeddings=multimodal_embeddings,
+            is_multimodal=is_multimodal,
+        )
+
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs: object,
+    ):
+        hidden_states = self.model(
+            input_ids, positions, hidden_states, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor | None:
+        return self.logits_processor(self.lm_head, hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        def remap_weight_names(weights):
+            for name, weight in weights:
+                if name.startswith("mtp."):
+                    name = name.replace("mtp.", "model.")
+                elif any(key in name for key in ["embed_tokens", "lm_head"]):
+                    if "embed_tokens" in name:
+                        name = name.replace("language_model.", "")
+                else:
+                    continue
+                yield name, weight
+
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(remap_weight_names(weights))
+
+
+class Qwen3_5MoeMTP(Qwen3_5MTP, QwenNextMixtureOfExperts):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        self.set_moe_parameters()

vllm/model_executor/models/qwen3_next.py

@@ -106,7 +106,7 @@ class Qwen3NextSparseMoeBlock(nn.Module):
     def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
 
-        config = vllm_config.model_config.hf_config
+        config = vllm_config.model_config.hf_text_config
         parallel_config = vllm_config.parallel_config
         quant_config = vllm_config.quant_config
 
@@ -177,7 +177,7 @@ class Qwen3NextSparseMoeBlock(nn.Module):
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
             reduce_results=False,
-            renormalize=config.norm_topk_prob,
+            renormalize=getattr(config, "norm_topk_prob", True),
             quant_config=quant_config,
             prefix=f"{prefix}.experts",
             enable_eplb=self.enable_eplb,
@@ -970,7 +970,7 @@ class Qwen3NextModel(nn.Module):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
 
-        config: Qwen3NextConfig = vllm_config.model_config.hf_config
+        config: Qwen3NextConfig = vllm_config.model_config.hf_text_config
         parallel_config = vllm_config.parallel_config
 
         eplb_config = parallel_config.eplb_config
@@ -1047,7 +1047,7 @@ class Qwen3NextModel(nn.Module):
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
             ckpt_up_proj_name="up_proj",
-            num_experts=self.config.num_experts,
+            num_experts=getattr(self.config, "num_experts", 0),
             num_redundant_experts=self.num_redundant_experts,
         )
 
@@ -1206,7 +1206,7 @@ class Qwen3NextForCausalLM(
     }
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        config = vllm_config.model_config.hf_config
+        config = vllm_config.model_config.hf_text_config
         self.vllm_config = vllm_config
         self.model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
@@ -1270,7 +1270,7 @@ class Qwen3NextForCausalLM(
         cls, vllm_config: "VllmConfig"
     ) -> tuple[tuple[int, int], tuple[int, int]]:
         parallel_config = vllm_config.parallel_config
-        hf_config = vllm_config.model_config.hf_config
+        hf_config = vllm_config.model_config.hf_text_config
         tp_size = parallel_config.tensor_parallel_size
         num_spec = (
             vllm_config.speculative_config.num_speculative_tokens

vllm/model_executor/models/registry.py

@@ -438,6 +438,14 @@ _MULTIMODAL_MODELS = {
         "qwen3_vl_moe",
         "Qwen3VLMoeForConditionalGeneration",
     ),
+    "Qwen3_5ForConditionalGeneration": (
+        "qwen3_5",
+        "Qwen3_5ForConditionalGeneration",
+    ),
+    "Qwen3_5MoeForConditionalGeneration": (
+        "qwen3_5",
+        "Qwen3_5MoeForConditionalGeneration",
+    ),
     "SkyworkR1VChatModel": ("skyworkr1v", "SkyworkR1VChatModel"),
     "Step3VLForConditionalGeneration": ("step3_vl", "Step3VLForConditionalGeneration"),  # noqa: E501
     "TarsierForConditionalGeneration": ("tarsier", "TarsierForConditionalGeneration"),  # noqa: E501
@@ -480,6 +488,8 @@ _SPECULATIVE_DECODING_MODELS = {
     "OpenPanguMTPModel": ("openpangu_mtp", "OpenPanguMTP"),
     "Qwen3NextMTP": ("qwen3_next_mtp", "Qwen3NextMTP"),
     "Step3p5MTP": ("step3p5_mtp", "Step3p5MTP"),
+    "Qwen3_5MTP": ("qwen3_5_mtp", "Qwen3_5MTP"),
+    "Qwen3_5MoeMTP": ("qwen3_5_mtp", "Qwen3_5MoeMTP"),
     # Temporarily disabled.
     # # TODO(woosuk): Re-enable this once the MLP Speculator is supported in V1.
     # "MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"),

vllm/transformers_utils/model_arch_config_convertor.py

@@ -371,6 +371,11 @@ class Qwen3NextMTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
         return getattr(self.hf_text_config, "num_nextn_predict_layers", 0)
 
 
+class Qwen3_5MTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
+    def get_num_hidden_layers(self) -> int:
+        return getattr(self.hf_text_config, "mtp_num_hidden_layers", 0)
+
+
 class PanguUltraMoeMTPModelArchConfigConvertor(ModelArchConfigConvertorBase):
     def get_num_hidden_layers(self) -> int:
         return getattr(self.hf_text_config, "num_nextn_predict_layers", 0)
@@ -396,6 +401,7 @@ MODEL_ARCH_CONFIG_CONVERTORS = {
     "nemotron-nas": NemotronNasModelArchConfigConvertor,
     "deepseek_mtp": DeepSeekMTPModelArchConfigConvertor,
     "qwen3_next_mtp": Qwen3NextMTPModelArchConfigConvertor,
+    "qwen3_5_mtp": Qwen3_5MTPModelArchConfigConvertor,
     "mimo_mtp": MimoMTPModelArchConfigConvertor,
     "glm4_moe_mtp": GLM4MoeMTPModelArchConfigConvertor,
     "ernie_mtp": ErnieMTPModelArchConfigConvertor,

vllm/v1/spec_decode/eagle.py

@@ -1147,6 +1147,8 @@ class SpecDecodeBaseProposer:
                 "Qwen3VLForConditionalGeneration",
                 "Qwen3VLMoeForConditionalGeneration",
                 "GlmOcrForConditionalGeneration",
+                "Qwen3_5ForConditionalGeneration",
+                "Qwen3_5MoeForConditionalGeneration",
             ]:
                 self.model.config.image_token_index = target_model.config.image_token_id
             elif self.get_model_name(target_model) == "PixtralForConditionalGeneration":