diff --git a/fastdeploy/model_executor/models/qwen3moe.py b/fastdeploy/model_executor/models/qwen3moe.py
index bcf9dbe6a..7064ceafc 100644
--- a/fastdeploy/model_executor/models/qwen3moe.py
+++ b/fastdeploy/model_executor/models/qwen3moe.py
@@ -97,29 +97,26 @@ class Qwen3DecoderLayer(nn.Layer):
         prefix: str = "",
     ) -> None:
         super().__init__()
-        layer_id = int(prefix.split(sep=".")[-1])
 
+        layer_id = int(prefix.split(sep=".")[-1])
         self.self_attn = Qwen3Attention(
             fd_config=fd_config,
             layer_id=layer_id,
             prefix=f"{prefix}.self_attn",
         )
+
         weight_key_map = {
             "gate_weight_key": f"{prefix}.mlp.gate.weight",
             "up_gate_proj_expert_weight_key": f"{prefix}.mlp.experts.{{}}.up_gate_proj.weight",
             "down_proj_expert_weight_key": f"{prefix}.mlp.experts.{{}}.down_proj.weight",
         }
 
-        if (
-            fd_config.model_config.moe_num_experts is not None
-            and layer_id >= fd_config.model_config.moe_layer_start_index
-        ):
-
+        if fd_config.model_config.num_experts is not None and layer_id >= fd_config.model_config.moe_layer_start_index:
             self.mlp = FusedMoE(
                 fd_config,
                 moe_intermediate_size=fd_config.model_config.moe_intermediate_size,
-                num_experts=fd_config.model_config.moe_num_experts,
-                top_k=fd_config.model_config.moe_topk,
+                num_experts=fd_config.model_config.num_experts,
+                top_k=fd_config.model_config.num_experts_per_tok,
                 layer_idx=layer_id,
                 weight_key_map=weight_key_map,
             )
@@ -386,12 +383,12 @@ class Qwen3MoePretrainedModel(PretrainedModel):
             return final_actions
 
         num_experts = 0
-        if isinstance(config.moe_num_experts, list):
-            num_experts = sum(config.moe_num_experts)
-        elif isinstance(config.moe_num_experts, int):
-            num_experts = config.moe_num_experts
+        if isinstance(config.num_experts, list):
+            num_experts = sum(config.num_experts)
+        elif isinstance(config.num_experts, int):
+            num_experts = config.num_experts
         else:
-            raise ValueError(f"Not support type of num_experts [{type(config.moe_num_experts)}]")
+            raise ValueError(f"Not support type of num_experts [{type(config.num_experts)}]")
 
         mappings = get_tensor_parallel_split_mappings(config.num_hidden_layers, num_experts)