diff --git a/egs/librispeech/ASR/zipformer/finetune.py b/egs/librispeech/ASR/zipformer/finetune.py
index cf62b33c6..843d103cc 100755
--- a/egs/librispeech/ASR/zipformer/finetune.py
+++ b/egs/librispeech/ASR/zipformer/finetune.py
@@ -24,31 +24,32 @@ Usage:
 
 export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
-# For non-streaming model training:
+# Fine-tune without mux (i.e not mixing with original training data):
 ./zipformer/finetune.py \
   --world-size 4 \
   --num-epochs 30 \
   --start-epoch 1 \
   --use-fp16 1 \
-  --exp-dir zipformer/exp \
-  --full-libri 1 \
+  --do-finetune 1 \
+  --finetune-ckpt path/to/ckpt \
+  --base-lr 0.0045 \
+  --use-mux 0 \
+  --exp-dir zipformer/exp_finetune \
   --max-duration 1000
 
-# For streaming model training:
+# Fine-tune without mux (i.e mixing with original training data):
 ./zipformer/finetune.py \
   --world-size 4 \
   --num-epochs 30 \
   --start-epoch 1 \
   --use-fp16 1 \
-  --exp-dir zipformer/exp \
-  --causal 1 \
-  --full-libri 1 \
+  --do-finetune 1 \
+  --finetune-ckpt path/to/ckpt \
+  --base-lr 0.0045 \
+  --use-mux 1 \
+  --exp-dir zipformer/exp_finetune \
   --max-duration 1000
 
-It supports training with:
-  - transducer loss (default), with `--use-transducer True --use-ctc False`
-  - ctc loss (not recommended), with `--use-transducer False --use-ctc True`
-  - transducer loss & ctc loss, with `--use-transducer True --use-ctc True`
 """
 
 
@@ -106,11 +107,13 @@ LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
 def get_adjusted_batch_count(params: AttributeDict) -> float:
     # returns the number of batches we would have used so far if we had used the reference
     # duration.  This is for purposes of set_batch_count().
+    # Note that we add a very large constant here to make the ScheduledFloat
+    # variable as their end value.
     return (
         params.batch_idx_train
         * (params.max_duration * params.world_size)
         / params.ref_duration
-    )
+    ) + 100000
 
 
 def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None:
@@ -370,22 +373,29 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--base-lr", type=float, default=0.045, help="The base learning rate."
+        "--base-lr",
+        type=float,
+        default=0.0045,
+        help="""The base learning rate.
+        It is set to a very small value as we are doing fine-tuning""",
     )
 
     parser.add_argument(
         "--lr-batches",
         type=float,
-        default=7500,
+        default=100000.0,
         help="""Number of steps that affects how rapidly the learning rate
-        decreases. We suggest not to change this.""",
+        decreases. It is set to a very large value here to prevent the lr from decaying too fast
+        during fine-tuning.""",
     )
 
     parser.add_argument(
         "--lr-epochs",
         type=float,
-        default=3.5,
+        default=100.0,
         help="""Number of epochs that affects how rapidly the learning rate decreases.
+        It is set to a very large value here to prevent the lr from decaying too fast
+        during fine-tuning.
         """,
     )