diff --git a/examples/models/llama/norm.py b/examples/models/llama/norm.py
index 8ad51d4594a..f18cf824b9c 100644
--- a/examples/models/llama/norm.py
+++ b/examples/models/llama/norm.py
@@ -42,16 +42,23 @@ def forward(self, x):
 
 
 class ScalelessRMSNorm(torch.nn.Module):
+    """RMSNorm without learnable scaling.
+
+    Calls F.rms_norm with weight=None so the op composes/decomposes cleanly for
+    backends like QNN instead of being expressed as a hand-rolled decomposition
+    of mean / rsqrt / mul. Semantically equivalent to
+    torch.nn.RMSNorm(elementwise_affine=False), but implemented as a plain
+    Module to preserve the previous parameterless state_dict signature (no
+    `weight` attribute / parameter).
+    """
+
     def __init__(self, dim: int, eps: float = 1e-6):
         super().__init__()
         self.dim = dim
         self.eps = eps
 
     def forward(self, x):
-        x_float = x.float()
-        return (
-            x_float * torch.rsqrt((x_float * x_float).mean(-1, keepdim=True) + self.eps)
-        ).type_as(x)
+        return F.rms_norm(x, (self.dim,), None, self.eps)
 
 
 class RMSNormWithInputScale(torch.nn.Module):
diff --git a/examples/models/llama/source_transformation/sdpa.py b/examples/models/llama/source_transformation/sdpa.py
index b10f684ccc0..0285f3562cb 100644
--- a/examples/models/llama/source_transformation/sdpa.py
+++ b/examples/models/llama/source_transformation/sdpa.py
@@ -69,7 +69,7 @@ def forward(
                 0,  # dropout probability. Ignored by the code
                 True,  # is_causal
             )
-        return output.view(bsz, seqlen, self.dim).to(dtype=input_dtype)
+        return output.reshape(bsz, seqlen, self.dim).to(dtype=input_dtype)
 
 
 def _replace_sdpa_with_custom_op(
@@ -198,7 +198,7 @@ def forward(
                 v_scale_fp32,
             )
 
-        return output.view(bsz, seqlen, self.dim)
+        return output.reshape(bsz, seqlen, self.dim)
 
 
 def _update_attention_module_with_quantized_sdpa(