diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_lora.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_lora.py.swp
index 0b4a6064c..bc626fd43 100644
Binary files a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_lora.py.swp and b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_lora.py.swp differ
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_lora.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_lora.py
index 125d79fbf..aa9197980 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_lora.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_lora.py
@@ -1607,27 +1607,30 @@ def run_adapter(rank, world_size, args, wb=None):
     lora_modules = []
     for modules in model.modules():
         if isinstance(modules, fairseq.modules.multihead_attention.MultiheadAttention):
-            #lora_modules.append(LoRAHook(modules))
-            for module in modules.modules():
+            lora_modules.append(LoRAHook(modules))
+            #for module in modules.modules():
             #    if rank == 0: logging.info(module)
                 #print(module)
-                if isinstance(module, torch.nn.Linear):
+                #if isinstance(module, torch.nn.Linear):
                     #if rank == 0: print(module)
-                    lora_modules.append(LoRAHook(module))
+                    #lora_modules.append(LoRAHook(module))
     
     adapter_names = []
     adapter_param = []
+    '''
     for i, lora in enumerate(lora_modules):
         for n, p in lora.lora.named_parameters():
             new_n = str(i) + n
             adapter_names.append(new_n)
             adapter_param.append(p)
-
-    #for n, p in model.named_parameters():
-        #if 'joiner' in n or 'simple' in n or 'ctc' in n:
-        #    p.requires_grad = True
-        #else:
-        #    p.requires_grad = False
+    '''
+    for n, p in model.named_parameters():
+        if 'joiner' in n or 'simple' in n or 'ctc' in n:
+            adapter_names.append(n)
+            adapter_param.append(p)
+            p.requires_grad = True
+        else:
+            p.requires_grad = False
     
     #for lora in lora_modules:
     #    print(lora.lora.state_dict())