diff --git a/egs/librispeech/ASR/zipformer_lora/export.py b/egs/librispeech/ASR/zipformer_lora/export.py
index 2b8d1aaf3..6223fff14 100755
--- a/egs/librispeech/ASR/zipformer_lora/export.py
+++ b/egs/librispeech/ASR/zipformer_lora/export.py
@@ -31,8 +31,8 @@ dataset, you should change the argument values according to your dataset.
 
 - For non-streaming model:
 
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
+./zipformer_lora/export.py \
+  --exp-dir ./zipformer_lora/exp \
   --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9 \
@@ -48,8 +48,8 @@ for how to use the exported models outside of icefall.
 
 - For streaming model:
 
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
+./zipformer_lora/export.py \
+  --exp-dir ./zipformer_lora/exp \
   --causal 1 \
   --chunk-size 16 \
   --left-context-frames 128 \
@@ -70,16 +70,16 @@ for how to use the exported models outside of icefall.
 
 - For non-streaming model:
 
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
+./zipformer_lora/export.py \
+  --exp-dir ./zipformer_lora/exp \
   --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9
 
 - For streaming model:
 
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
+./zipformer_lora/export.py \
+  --exp-dir ./zipformer_lora/exp \
   --causal 1 \
   --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
@@ -90,15 +90,15 @@ load it by `icefall.checkpoint.load_checkpoint()`.
 
 - For non-streaming model:
 
-To use the generated file with `zipformer/decode.py`,
+To use the generated file with `zipformer_lora/decode.py`,
 you can do:
 
     cd /path/to/exp_dir
     ln -s pretrained.pt epoch-9999.pt
 
     cd /path/to/egs/librispeech/ASR
-    ./zipformer/decode.py \
-        --exp-dir ./zipformer/exp \
+    ./zipformer_lora/decode.py \
+        --exp-dir ./zipformer_lora/exp \
         --epoch 9999 \
         --avg 1 \
         --max-duration 600 \
@@ -107,7 +107,7 @@ you can do:
 
 - For streaming model:
 
-To use the generated file with `zipformer/decode.py` and `zipformer/streaming_decode.py`, you can do:
+To use the generated file with `zipformer_lora/decode.py` and `zipformer_lora/streaming_decode.py`, you can do:
 
     cd /path/to/exp_dir
     ln -s pretrained.pt epoch-9999.pt
@@ -115,8 +115,8 @@ To use the generated file with `zipformer/decode.py` and `zipformer/streaming_de
     cd /path/to/egs/librispeech/ASR
 
     # simulated streaming decoding
-    ./zipformer/decode.py \
-        --exp-dir ./zipformer/exp \
+    ./zipformer_lora/decode.py \
+        --exp-dir ./zipformer_lora/exp \
         --epoch 9999 \
         --avg 1 \
         --max-duration 600 \
@@ -127,8 +127,8 @@ To use the generated file with `zipformer/decode.py` and `zipformer/streaming_de
         --bpe-model data/lang_bpe_500/bpe.model
 
     # chunk-wise streaming decoding
-    ./zipformer/streaming_decode.py \
-        --exp-dir ./zipformer/exp \
+    ./zipformer_lora/streaming_decode.py \
+        --exp-dir ./zipformer_lora/exp \
         --epoch 9999 \
         --avg 1 \
         --max-duration 600 \
@@ -167,7 +167,7 @@ import k2
 import torch
 from scaling_converter import convert_scaled_to_non_scaled
 from torch import Tensor, nn
-from train import add_model_arguments, get_model, get_params
+from finetune import add_model_arguments, add_finetune_arguments, get_model, get_params
 
 from icefall.checkpoint import (
     average_checkpoints,
@@ -225,7 +225,7 @@ def get_parser():
     parser.add_argument(
         "--exp-dir",
         type=str,
-        default="zipformer/exp",
+        default="zipformer_lora/exp",
         help="""It specifies the directory where all training related
         files, e.g., checkpoints, log, etc, are saved
         """,
@@ -256,6 +256,7 @@ def get_parser():
     )
 
     add_model_arguments(parser)
+    add_finetune_arguments(parser)
 
     return parser
 
@@ -486,6 +487,22 @@ def main():
                 )
             )
 
+    # merge the LoRA weights
+    model.eval()
+
+    params.use_lora = False
+    base_model = get_model(params)
+
+    new_state_dict = {}
+    state_dict = model.state_dict()
+    param_names = base_model.state_dict().keys()
+    for k in param_names:
+        assert k in state_dict.keys()
+        new_state_dict[k] = state_dict[k]
+        
+    base_model.load_state_dict(new_state_dict, strict=True)
+
+    model = base_model
     model.eval()
 
     if params.jit is True: