diff --git a/vllm/model_executor/models/llama_eagle3.py b/vllm/model_executor/models/llama_eagle3.py
index 5f66716d5..462d18c98 100644
--- a/vllm/model_executor/models/llama_eagle3.py
+++ b/vllm/model_executor/models/llama_eagle3.py
@@ -150,6 +150,7 @@ class LlamaModel(nn.Module):
             self.use_aux_hidden_state = eagle_config["use_aux_hidden_state"]
         else:
             self.use_aux_hidden_state = True
+        self.norm_before_fc = getattr(self.config, "norm_before_fc", False)
 
         current_vllm_config = get_current_vllm_config()
 
@@ -175,6 +176,13 @@ class LlamaModel(nn.Module):
                 fc_input_size = self.config.target_hidden_size * 3
             else:
                 fc_input_size = self.config.hidden_size * 3
+            if self.norm_before_fc:
+                self.input_norm = RMSNorm(
+                    fc_input_size,
+                    eps=self.config.rms_norm_eps,
+                )
+            else:
+                self.input_norm = None
             self.fc = ReplicatedLinear(
                 input_size=fc_input_size,
                 output_size=self.config.hidden_size,
@@ -357,6 +365,9 @@ class Eagle3LlamaForCausalLM(LlamaForCausalLM):
         if not self.model.use_aux_hidden_state:
             return hidden_states
         # combine multiple auxiliary hidden states returned by eagle3
+
+        if self.model.norm_before_fc:
+            hidden_states = self.model.input_norm(hidden_states)
         return self.model.fc(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
@@ -403,6 +414,8 @@ class Eagle3LlamaForCausalLM(LlamaForCausalLM):
             skip_substrs.append("embed_tokens")
         if not self.model.use_aux_hidden_state:
             skip_substrs.append("fc.")
+        if not self.model.norm_before_fc:
+            skip_substrs.append("input_norm.")
         loader = AutoWeightsLoader(
             self,
             skip_prefixes=None,