diff --git a/src/slam_llm/utils/train_utils.py b/src/slam_llm/utils/train_utils.py
index 849f7870..8f5c34e6 100644
--- a/src/slam_llm/utils/train_utils.py
+++ b/src/slam_llm/utils/train_utils.py
@@ -109,31 +109,17 @@ def train(model, train_dataloader,eval_dataloader, tokenizer, optimizer, lr_sche
                 with autocast():
                     outputs, *rest = model(**batch)
                 acc = rest[0] if rest else -1
-                audio_acc = rest[1] if rest else -1   # seven layers of audio acc
-                layer_loss = rest[2] if rest else -1  # eight layers of loss (seven audio and one text)
                 loss = outputs.loss
 
                 loss = loss / gradient_accumulation_steps
-                layer_loss = [l / gradient_accumulation_steps for l in layer_loss]
                 acc = acc / gradient_accumulation_steps
-                audio_acc = [a / gradient_accumulation_steps for a in audio_acc]
 
                 if log_config.use_wandb and step % log_config.log_interval == 0:
                     if train_config.enable_fsdp or train_config.enable_ddp:
                         if rank==0:
-                            wandb.log({"train_inner/train_inner_loss":loss, "train_inner/train_inner_text_accuracy":acc}, step=(epoch * total_length + step))
-                            for layer, acc in enumerate(audio_acc):
-                                wandb.log({f"train_inner/train_inner_audio_accuracy_layer{layer}":acc}, step=(epoch * total_length + step))
-                            for layer, l in enumerate(layer_loss[:-1]):
-                                wandb.log({f"train_inner/train_inner_audio_loss_layer{layer}":l}, step=(epoch * total_length + step))
-                            wandb.log({f"train_inner/train_inner_text_loss":layer_loss[-1]}, step=(epoch * total_length + step))
+                            wandb.log({"train_inner/train_inner_loss":loss, "train_inner/train_inner_accuracy":acc}, step=(epoch * total_length + step))
                     else:
                         wandb.log({"train_inner/train_inner_loss":loss, "train_inner/train_inner_accuracy":acc}, step=(epoch * total_length + step))
-                        for layer, acc in enumerate(audio_acc):
-                            wandb.log({f"train_inner/train_inner_audio_accuracy_layer{layer}":acc}, step=(epoch * total_length + step))
-                        for layer, l in enumerate(layer_loss[:-1]):
-                            wandb.log({f"train_inner/train_inner_audio_loss_layer{layer}":l}, step=(epoch * total_length + step))
-                        wandb.log({f"train_inner/train_inner_text_loss":layer_loss[-1]}, step=(epoch * total_length + step))
                     
                 total_loss += loss.detach().float()
                 total_acc += acc