diff --git a/examples/models/qwen3/convert_weights.py b/examples/models/qwen3/convert_weights.py
index 53a609885d7..6d5254906fb 100644
--- a/examples/models/qwen3/convert_weights.py
+++ b/examples/models/qwen3/convert_weights.py
@@ -13,6 +13,7 @@
 _QWEN_3_FROM_META = {
     "tok_embeddings.weight": "model.embed_tokens.weight",
     "norm.weight": "model.norm.weight",
+    "output.weight": "lm_head.weight",
     "layers.{}.attention.wk.weight": "model.layers.{}.self_attn.k_proj.weight",
     "layers.{}.attention.k_norm_fn.weight": "model.layers.{}.self_attn.k_norm.weight",
     "layers.{}.attention.wq.weight": "model.layers.{}.self_attn.q_proj.weight",
@@ -47,20 +48,19 @@ def qwen_3_tune_to_meta(state_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.
     inverted_mapping_dict = {v: k for k, v in _QWEN_3_FROM_META.items()}
 
     for key, value in state_dict.items():
-        # Tied embeddings for 0.6b and 4b models.
-        if key == "lm_head.weight":
-            continue
         new_key = get_mapped_key(key, inverted_mapping_dict)
         converted_state_dict[new_key] = value
 
-    converted_state_dict["output.weight"] = converted_state_dict[
-        "tok_embeddings.weight"
-    ]
+    # If lm_head.weight is not present in state dict, assume tied embeddings (e.g., 0.6b and 4b models)
+    if "lm_head.weight" not in state_dict:
+        converted_state_dict["output.weight"] = converted_state_dict[
+            "tok_embeddings.weight"
+        ]
 
     return converted_state_dict
 
 
-def load_checkpoint(input_dir: str) -> Dict:
+def load_checkpoint_from_safetensors(input_dir: str) -> Dict:
     index_path = os.path.join(input_dir, "model.safetensors.index.json")
     if os.path.exists(index_path):
         # Sharded checkpoint.
@@ -86,6 +86,15 @@ def load_checkpoint(input_dir: str) -> Dict:
         return state_dict
 
 
+def load_checkpoint(input_dir: str) -> Dict:
+    pytorch_path = os.path.join(input_dir, "pytorch_model.bin")
+    if os.path.exists(pytorch_path):
+        print("Loading checkpoint from PyTorch .bin file")
+        return torch.load(pytorch_path, map_location="cpu", weights_only=True)
+    print("Loading checkpoint from safetensors directory")
+    return load_checkpoint_from_safetensors(input_dir)
+
+
 def convert_weights(input_dir: str, output_file: str) -> None:
     print("Loading checkpoint...")
     sd = load_checkpoint(input_dir)
@@ -103,7 +112,7 @@ def main():
     parser.add_argument(
         "input_dir",
         type=str,
-        help="Path to directory containing checkpoint files",
+        help="Path to directory containing safetensor checkpoint files, or PyTorch checkpoint file.",
     )
     parser.add_argument("output", type=str, help="Path to the output checkpoint")