[example] add bash usage (#7794)

src/llamafactory/train/trainer_utils.py

@@ -489,16 +489,14 @@ def _create_adam_mini_optimizer(
     logger.info_rank0("Using Adam-mini optimizer.")
     return optimizer
 
+
 def _create_muon_optimizer(
     model: "PreTrainedModel",
     training_args: "TrainingArguments",
 ) -> "torch.optim.Optimizer":
-    from llamafactory.third_party.muon import Muon  # type: ignore
-    
-    # Separate parameters for Muon (2D parameters) and AdamW (others)
-    muon_params = []
-    adamw_params = []
-    
+    from ..third_party.muon import Muon
+
+    muon_params, adamw_params = [], []
     for name, param in model.named_parameters():
         if param.requires_grad:
             # Use Muon for 2D parameters that aren't embeddings or heads
@@ -506,34 +504,26 @@ def _create_muon_optimizer(
                 muon_params.append(param)
             else:
                 adamw_params.append(param)
-    
-    # Get optimizer settings from training_args
-    ns_steps = getattr(training_args, "ns_steps", 5)
-    
-    # Create Muon optimizer
+
     optimizer = Muon(
         lr=training_args.learning_rate,
         wd=training_args.weight_decay,
         muon_params=muon_params,
-        momentum=0.95,  # default momentum for Muon
-        nesterov=True,  # default nesterov for Muon
-        ns_steps=ns_steps,
         adamw_params=adamw_params,
         adamw_betas=(training_args.adam_beta1, training_args.adam_beta2),
         adamw_eps=training_args.adam_epsilon,
     )
-    
-    logger.info_rank0(f"Using Muon optimizer with {len(muon_params)} Muon params and {len(adamw_params)} AdamW params.")
+    logger.info_rank0(
+        f"Using Muon optimizer with {len(muon_params)} Muon params and {len(adamw_params)} AdamW params."
+    )
     return optimizer
 
+
 def create_custom_optimizer(
     model: "PreTrainedModel",
     training_args: "TrainingArguments",
     finetuning_args: "FinetuningArguments",
 ) -> Optional["torch.optim.Optimizer"]:
-    if finetuning_args.use_muon:
-        return _create_muon_optimizer(model, training_args)
-    
     if finetuning_args.use_galore:
         return _create_galore_optimizer(model, training_args, finetuning_args)
 
@@ -549,6 +539,9 @@ def create_custom_optimizer(
     if finetuning_args.use_adam_mini:
         return _create_adam_mini_optimizer(model, training_args)
 
+    if finetuning_args.use_muon:
+        return _create_muon_optimizer(model, training_args)
+
 
 def create_custom_scheduler(
     training_args: "TrainingArguments",