From ecc2aca00b2b41f6663156623365304e331da68c Mon Sep 17 00:00:00 2001
From: Mohammad Miadh Angkad <176301910+mmangkad@users.noreply.github.com>
Date: Tue, 31 Mar 2026 11:36:40 +0800
Subject: [PATCH] upd

Signed-off-by: Mohammad Miadh Angkad <176301910+mmangkad@users.noreply.github.com>
---
 vllm/model_executor/models/qwen3_5_mtp.py | 34 +++++++++++++++++------
 1 file changed, 25 insertions(+), 9 deletions(-)

diff --git a/vllm/model_executor/models/qwen3_5_mtp.py b/vllm/model_executor/models/qwen3_5_mtp.py
index 0eca47492c91..6f33505e69c4 100644
--- a/vllm/model_executor/models/qwen3_5_mtp.py
+++ b/vllm/model_executor/models/qwen3_5_mtp.py
@@ -15,6 +15,7 @@
 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.quantization import QuantizationConfig
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
@@ -43,6 +44,15 @@
 logger = init_logger(__name__)
 
 
+def _get_qwen3_5_mtp_quant_config(
+    quant_config: QuantizationConfig | None,
+) -> QuantizationConfig | None:
+    # Qwen3.5 NVFP4 checkpoints keep the entire MTP branch in bf16 weights.
+    if quant_config is not None and quant_config.get_name() == "modelopt_fp4":
+        return None
+    return quant_config
+
+
 @support_torch_compile(
     dynamic_arg_dims={
         "input_ids": 0,
@@ -59,7 +69,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
 
         model_config = vllm_config.model_config
-        quant_config = vllm_config.quant_config
+        quant_config = _get_qwen3_5_mtp_quant_config(vllm_config.quant_config)
 
         config: Qwen3_5TextConfig | Qwen3_5MoeTextConfig = model_config.hf_text_config
 
@@ -85,14 +95,20 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             prefix=f"{prefix}.fc",
         )
 
-        self.layers = torch.nn.ModuleList(
-            Qwen3_5DecoderLayer(
-                vllm_config,
-                layer_type="full_attention",
-                prefix=f"{prefix}.layers.{idx}",
+        # Qwen3_5DecoderLayer reads quantization from vllm_config.
+        original_quant_config = vllm_config.quant_config
+        vllm_config.quant_config = quant_config
+        try:
+            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)
             )
-            for idx in range(self.num_mtp_layers)
-        )
+        finally:
+            vllm_config.quant_config = original_quant_config
 
         self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
             ["hidden_states", "residual"], config.hidden_size
@@ -352,7 +368,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
                 "please use '--mamba-cache-mode=align' instead"
             )
 
-        self.quant_config = vllm_config.quant_config
+        self.quant_config = _get_qwen3_5_mtp_quant_config(vllm_config.quant_config)
 
         super().__init__()
         self.config = config