diff --git a/egs/speech_llm/ASR_LLM/zipformer_llm_zh/train.py b/egs/speech_llm/ASR_LLM/zipformer_llm_zh/train.py
index 2ace5c532..eaae4a33e 100755
--- a/egs/speech_llm/ASR_LLM/zipformer_llm_zh/train.py
+++ b/egs/speech_llm/ASR_LLM/zipformer_llm_zh/train.py
@@ -500,10 +500,10 @@ def compute_loss(
 
     device = next(model.parameters()).device
 
-    feature = batch["inputs"]
-    assert feature.ndim == 3
+    features = batch["inputs"]
+    assert features.ndim == 3
     if params.use_fp16:
-        feature = feature.half()
+        features = features.half()
 
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"]
@@ -526,7 +526,7 @@ def compute_loss(
 
     with torch.set_grad_enabled(is_training):
         model_outputs, acc = model(
-            fbank=feature.to(device),
+            fbank=features.to(device),
             fbank_lens=feature_lens.to(device),
             input_ids=input_ids.to(device),
             attention_mask=attention_mask.to(device),
@@ -663,30 +663,6 @@ def train_one_epoch(
                 valid_info.write_summary(
                     tb_writer, "train/valid_", params.batch_idx_train
                 )
-            if batch_idx != 0:
-                model.save_checkpoint(
-                    save_dir=params.exp_dir,
-                    tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}",
-                    client_state={},
-                    exclude_frozen_parameters=True,
-                )
-
-                if rank == 0:
-                    convert_zero_checkpoint_to_fp32_state_dict(
-                        params.exp_dir,
-                        f"{params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}.pt",
-                        tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}",
-                        exclude_frozen_parameters=True,
-                    )
-                    # save sampler state dict into checkpoint
-                    sampler_state_dict = train_dl.sampler.state_dict()
-                    torch.save(
-                        sampler_state_dict,
-                        f"{params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}-sampler.pt",
-                    )
-                    os.system(
-                        f"rm -rf {params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}"
-                    )
         try:
             loss, loss_info = compute_loss(
                 params=params,