diff --git a/python/sglang/srt/models/olmo2.py b/python/sglang/srt/models/olmo2.py
index f3e1979f849..a8af7bc1a4e 100644
--- a/python/sglang/srt/models/olmo2.py
+++ b/python/sglang/srt/models/olmo2.py
@@ -64,24 +64,24 @@ def __init__(
         super().__init__()
         self.config = config
         self.hidden_size = config.hidden_size
-        tp_size = get_tensor_model_parallel_world_size()
+        self.tp_size = get_tensor_model_parallel_world_size()
         self.total_num_heads = config.num_attention_heads
 
         assert self.hidden_size % self.total_num_heads == 0
-        assert self.total_num_heads % tp_size == 0
+        assert self.total_num_heads % self.tp_size == 0
 
-        self.num_heads = self.total_num_heads // tp_size
+        self.num_heads = self.total_num_heads // self.tp_size
         self.total_num_kv_heads = self.config.num_key_value_heads
 
-        if self.total_num_kv_heads >= tp_size:
+        if self.total_num_kv_heads >= self.tp_size:
             # Number of KV heads is greater than TP size, so we partition
             # the KV heads across multiple tensor parallel GPUs.
-            assert self.total_num_kv_heads % tp_size == 0
+            assert self.total_num_kv_heads % self.tp_size == 0
         else:
             # Number of KV heads is less than TP size, so we replicate
             # the KV heads across multiple tensor parallel GPUs.
-            assert tp_size % self.total_num_kv_heads == 0
-        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+            assert self.tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
 
         self.head_dim = self.hidden_size // self.total_num_heads
         self.max_position_embeddings = config.max_position_embeddings
@@ -343,7 +343,7 @@ def forward(
             input_embeds=input_embeds,
         )
         return self.logits_processor(
-            input_ids, hidden_states, self.lm_head.weight, forward_batch
+            input_ids, hidden_states, self.lm_head, forward_batch
         )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):