diff --git a/tests/utils/lora/test_merge_lora.py b/tests/utils/lora/test_merge_lora.py
index b66ee8bf4..ea20c391f 100644
--- a/tests/utils/lora/test_merge_lora.py
+++ b/tests/utils/lora/test_merge_lora.py
@@ -491,7 +491,8 @@ class TestEfficientMerge:
         out_features = 4
         alpha = 4
 
-        base = torch.randn(num_experts, in_features, out_features)
+        # PEFT ParamWrapper treats non-transposed 3D weights as (experts, out, in)
+        base = torch.randn(num_experts, out_features, in_features)
         lora_a = torch.randn(r * num_experts, in_features)
         lora_b = torch.randn(out_features, r * num_experts)
 
@@ -507,7 +508,7 @@ class TestEfficientMerge:
         scale = alpha / r
         wa = lora_a.reshape(num_experts, r, in_features)
         wb = lora_b.reshape(out_features, r, num_experts)
-        manual_delta = torch.einsum("o r e, e r i -> e i o", wb, wa) * scale
+        manual_delta = torch.einsum("o r e, e r i -> e o i", wb, wa) * scale
         for e in range(num_experts):
             assert torch.allclose(merged[e], base[e] + manual_delta[e], atol=1e-5), (
                 f"Expert {e} mismatch"