diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp
index 817e39bf5..2d84528ca 100644
Binary files a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp and b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp differ
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.prompt_tuning.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.prompt_tuning.py.swp
index 04617ff08..38691021a 100644
Binary files a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.prompt_tuning.py.swp and b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.prompt_tuning.py.swp differ
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/prompt_tuning.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/prompt_tuning.py
index 4ad3dc93a..ed8f12988 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/prompt_tuning.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/prompt_tuning.py
@@ -1579,6 +1579,7 @@ def run_adapter(rank, world_size, args, wb=None):
         logging.info("Using DDP")
         model = DDP(model, device_ids=[rank], find_unused_parameters=True)
     
+    '''
     adapter_names = []
     adapter_param = []
     for n, p  in model.named_parameters():
@@ -1593,8 +1594,6 @@ def run_adapter(rank, world_size, args, wb=None):
     for n, p in model.named_parameters():
         p.requires_grad = False
     
-    prompt = torch.nn.Parameter(torch.randn(50, 512)).to(device)
-    
     optimizer_adapter = ScaledAdam(
             adapter_param,
             lr=params.adapter_lr,
@@ -1602,13 +1601,14 @@ def run_adapter(rank, world_size, args, wb=None):
             parameters_names=[adapter_names],
         )
     '''
+
+    prompt = torch.nn.Parameter(torch.randn(50, 512)).to(device)
     optimizer_adapter = ScaledAdam(
             [prompt],
             lr=params.adapter_lr,
             clipping_scale=5.0,
             parameters_names=['prompt'],
     )
-    '''
     scheduler_adapter = Eden(optimizer_adapter, 10000, 7) #params.lr_batche, params.lr_epochs)
 
     optimizer, scheduler = optimizer_adapter, scheduler_adapter