diff --git a/python/sglang/srt/models/mixtral_quant.py b/python/sglang/srt/models/mixtral_quant.py
index 5b84c90ddf78..10bfa5068de2 100644
--- a/python/sglang/srt/models/mixtral_quant.py
+++ b/python/sglang/srt/models/mixtral_quant.py
@@ -115,10 +115,10 @@ def __init__(
                 f"the number of experts {self.num_total_experts}."
             )
         # Split experts equally between ranks
-        self.expert_indicies = np.array_split(
+        self.expert_indices = np.array_split(
             range(self.num_total_experts), self.tp_size
         )[self.rank].tolist()
-        if not self.expert_indicies:
+        if not self.expert_indices:
             raise ValueError(f"Rank {self.rank} has no experts assigned to it.")
 
         self.experts = nn.ModuleList(
@@ -131,7 +131,7 @@ def __init__(
                         quant_config=quant_config,
                         prefix=add_prefix(f"experts.{idx}", prefix),
                     )
-                    if idx in self.expert_indicies
+                    if idx in self.expert_indices
                     else None
                 )
                 for idx in range(self.num_total_experts)
@@ -155,7 +155,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
 
         final_hidden_states = None
-        for expert_idx in self.expert_indicies:
+        for expert_idx in self.expert_indices:
             expert_layer = self.experts[expert_idx]
             expert_mask = selected_experts == expert_idx
             expert_weights = (routing_weights * expert_mask).sum(dim=-1, keepdim=True)