diff --git a/python/sglang/srt/models/minicpmo.py b/python/sglang/srt/models/minicpmo.py
index ad89dcea2..3a0edf52b 100644
--- a/python/sglang/srt/models/minicpmo.py
+++ b/python/sglang/srt/models/minicpmo.py
@@ -25,7 +25,7 @@ import torch.nn.functional as F
 import torch.nn.utils.parametrize as P
 import torch.types
 from torch import nn
-from torch.nn.utils import weight_norm
+from torch.nn.utils import parametrizations
 from tqdm import tqdm
 from transformers import LlamaConfig, LlamaModel, PretrainedConfig, PreTrainedModel
 from transformers.activations import ACT2FN
@@ -585,7 +585,7 @@ class ConditionalChatTTS(PreTrainedModel):
         self.emb_text = nn.Embedding(config.num_text_tokens, config.hidden_size)
         self.head_code = nn.ModuleList(
             [
-                weight_norm(
+                parametrizations.weight_norm(
                     nn.Linear(config.hidden_size, config.num_audio_tokens, bias=False),
                     name="weight",
                 )
@@ -1859,11 +1859,22 @@ class MiniCPMO(MiniCPMBaseModel):
                 # the checkpoint. Skip them.
                 continue
 
-            # adapt to parametrization
+            # For weight_norm parametrization, handle both old and new formats
             if self.config.init_tts and "tts" in name:
-                name = name.replace(".parametrizations", "")
-                name = name.replace(".weight.original0", ".weight_g")
-                name = name.replace(".weight.original1", ".weight_v")
+                # Handle loading from older checkpoints with weight_g/weight_v format
+                if ".weight_g" in name or ".weight_v" in name:
+                    name = name.replace(
+                        ".weight_g", ".parametrizations.weight.original0"
+                    )
+                    name = name.replace(
+                        ".weight_v", ".parametrizations.weight.original1"
+                    )
+                elif ".weight" in name and name not in params_dict:
+                    param_name = name.replace(
+                        ".weight", ".parametrizations.weight.original0"
+                    )
+                    if param_name in params_dict:
+                        name = param_name
 
             # adapt to VisionAttention
             if "vpm" in name: