diff --git a/swift/megatron/arguments/megatron_args.py b/swift/megatron/arguments/megatron_args.py
index 10cf90aed6..8cc7c6c180 100644
--- a/swift/megatron/arguments/megatron_args.py
+++ b/swift/megatron/arguments/megatron_args.py
@@ -706,10 +706,6 @@ def __post_init__(self):
         if self.tuner_type == 'lora_llm':
             if not self.is_multimodal:
                 raise ValueError('`tuner_type="lora_llm"` is only supported for multimodal models.')
-            if not self.merge_lora:
-                raise ValueError('`merge_lora` must be True when using `--tuner_type lora_llm`')
-            if not self.no_save_optim:
-                raise ValueError('`no_save_optim` must be True when using `--tuner_type lora_llm`')
         if self.adapters or self.ref_adapters or self.mcore_adapter or self.mcore_ref_adapter:
             if self.tuner_type == 'full':
                 self.tuner_type = 'lora'
diff --git a/swift/megatron/trainers/base.py b/swift/megatron/trainers/base.py
index 25400c2d48..e0530e5a5f 100644
--- a/swift/megatron/trainers/base.py
+++ b/swift/megatron/trainers/base.py
@@ -195,7 +195,7 @@ def _prepare_peft_model(self, models):
         if args.mcore_model is None:
             self.bridge.load_weights(models, args.model_dir)
         peft_models = [prepare_mcore_model(args, model) for model in models]
-        if args.tuner_type == 'lora' and args.adapters and args.mcore_adapter is None:
+        if args.tuner_type in {'lora', 'lora_llm'} and args.adapters and args.mcore_adapter is None:
             assert len(args.adapters) == 1, 'Currently only support one adapter.'
             self.bridge.load_weights(models, args.adapters[0], peft_format=True, adapter_name='default')
         return peft_models
@@ -727,7 +727,7 @@ def save_checkpoint(self):
         os.makedirs(output_dir, exist_ok=True)
         args_path = os.path.join(os.path.dirname(output_dir), 'args.json')
         self.copy_path(args_path, os.path.join(output_dir, 'args.json'))
-        save_peft_format = args.tuner_type == 'lora' and not args.merge_lora
+        save_peft_format = args.tuner_type in {'lora', 'lora_llm'} and not args.merge_lora
         if args.save_safetensors and args.no_save_optim:
             model = []
         else:
@@ -739,7 +739,7 @@ def save_checkpoint(self):
             self.optimizer,
             self.opt_param_scheduler,
             iteration=iteration,
-            peft_format=args.tuner_type == 'lora',
+            peft_format=args.tuner_type in {'lora', 'lora_llm'},
             output_dir=output_dir)
         state.last_model_checkpoint = output_dir
         if state.best_global_step is not None: