[fix] add visual.pos_embed to Qwen3-VL visual model keys (#10139)

src/llamafactory/model/model_utils/visual.py

@@ -356,7 +356,7 @@ _register_composite_model(
 _register_composite_model(
     model_type="qwen3_vl",
     projector_key="visual.merger",
-    vision_model_keys=["visual.patch_embed", "visual.blocks", "visual.deepstack_merger_list"],
+    vision_model_keys=["visual.pos_embed", "visual.patch_embed", "visual.blocks", "visual.deepstack_merger_list"],
     language_model_keys=["language_model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
 )
@@ -365,7 +365,7 @@ _register_composite_model(
 _register_composite_model(
     model_type="qwen3_vl_moe",
     projector_key="visual.merger",
-    vision_model_keys=["visual.patch_embed", "visual.blocks", "visual.deepstack_merger_list"],
+    vision_model_keys=["visual.pos_embed", "visual.patch_embed", "visual.blocks", "visual.deepstack_merger_list"],
     language_model_keys=["language_model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
 )
@@ -374,7 +374,7 @@ _register_composite_model(
 _register_composite_model(
     model_type="qwen3_omni_moe_thinker",
     projector_key="visual.merger",
-    vision_model_keys=["visual.patch_embed", "visual.blocks", "visual.deepstack_merger_list", "audio_tower"],
+    vision_model_keys=["visual.pos_embed", "visual.patch_embed", "visual.blocks", "visual.deepstack_merger_list", "audio_tower"],
     language_model_keys=["model", "lm_head"],
     lora_conflict_keys=["patch_embed"],
 )

src/llamafactory/train/rm/trainer.py

@@ -109,6 +109,27 @@ class PairwiseTrainer(Trainer):
         else:
             return loss
 
+    @override
+    def _save(self, output_dir: Optional[str] = None, state_dict=None):
+        if state_dict is None:
+            state_dict = self.model.state_dict()
+
+        if self.args.save_safetensors:
+            from collections import defaultdict
+
+            ptrs = defaultdict(list)
+            for name, tensor in state_dict.items():
+                if isinstance(tensor, torch.Tensor):
+                    ptrs[id(tensor)].append(name)
+
+            for names in ptrs.values():
+                if len(names) > 1:
+                    names.sort()
+                    for name in names[1:]:
+                        state_dict.pop(name, None)
+
+        super()._save(output_dir, state_dict)
+
     def save_predictions(self, predict_results: "PredictionOutput") -> None:
         r"""Save model predictions to `output_dir`.