diff --git a/nemo/collections/llm/peft/lora.py b/nemo/collections/llm/peft/lora.py
index 18db9b164d6aa..b9fd4be83c61f 100644
--- a/nemo/collections/llm/peft/lora.py
+++ b/nemo/collections/llm/peft/lora.py
@@ -72,7 +72,7 @@ def forward(self, x):
 
 
 class LinearAdapter(nn.Module):
-    def __init__(self, orig_linear, dim=8, alpha=32, dropout=0.1, dropout_position='post'):
+    def __init__(self, orig_linear, dim=8, alpha=32, dropout=0.1, dropout_position='post', lora_A_init_method='xavier'):
         super(LoraLinear, self).__init__()
         assert isinstance(orig_linear, nn.Linear)
 
@@ -91,8 +91,11 @@ def __init__(self, orig_linear, dim=8, alpha=32, dropout=0.1, dropout_position='
         dtype = self.orig_linear.weight.dtype
         self.lora_a = nn.Parameter(torch.zeros((in_features, dim), dtype=dtype, device=device))
         self.lora_b = nn.Parameter(torch.zeros((dim, out_features), dtype=dtype, device=device))
-        nn.init.kaiming_uniform_(self.lora_a, a=math.sqrt(5))
-        nn.init.zeros_(self.lora_b)
+        if lora_A_init_method == 'xavier':
+            torch.nn.init.uniform_(self.lora_a)
+        else:
+            nn.init.kaiming_uniform_(self.lora_a, a=math.sqrt(5))
+
         self.dropout = nn.Dropout(p=dropout)
         self.dropout_position = dropout_position
 
@@ -208,7 +211,7 @@ def wildcard_match(pattern, key):
                 in_features = m.input_size
                 out_features = m.output_size
             elif isinstance(m, nn.Linear):
-                return LinearAdapter(m, dim=self.dim, lora_alpha=self.alpha, lora_dropout=self.dropout)
+                return LinearAdapter(m, dim=self.dim, lora_alpha=self.alpha, lora_dropout=self.dropout, lora_A_init_method=self.lora_A_init_method)
             else:
                 raise NotImplementedError(f"Layer type is unrecognized for LoRA: {type(m)}")