diff --git a/unsloth/kernels/fast_lora.py b/unsloth/kernels/fast_lora.py
index b3a1098355..3f6d4d56c2 100644
--- a/unsloth/kernels/fast_lora.py
+++ b/unsloth/kernels/fast_lora.py
@@ -125,15 +125,15 @@ class LoRA_MLP(torch.autograd.Function):
         dtype = X.dtype
 
         DW = matmul_lora(dY, downW.t(), downW_quant, downB, downA, downS)
-        # e = e.float()
-        # se = 1.0 / (1.0 + torch.exp(-e))
-        # f = (se * e).to(dtype)
-        # h = f * g
-        # df = DW * f
-        # dg = DW * g
-        # de = (dg.float() * se * (1.0 + e * (1.0 - se))).to(dtype)
-        DW, e, g = swiglu_DWf_DW_dfg_kernel(DW, e, g)
-        h, df, de = DW, e, g
+        e = e.float()
+        se = 1.0 / (1.0 + torch.exp(-e))
+        f = (se * e).to(dtype)
+        h = f * g
+        df = DW * f
+        dg = DW * g
+        de = (dg.float() * se * (1.0 + e * (1.0 - se))).to(dtype)
+        # DW, e, g = swiglu_DWf_DW_dfg_kernel(DW, e, g)
+        # h, df, de = DW, e, g
 
         # Down projection LoRA weights
         d_downA = h.t() @ (dY @ downB.t())
@@ -148,8 +148,8 @@ class LoRA_MLP(torch.autograd.Function):
         d_upB  *= upS
 
         # Gate projection LoRA weights
-        d_gateA = X.t() @ (de @ gateB.t())
-        d_gateB = (gateA.t() @ X.t()) @ de
+        d_gateA = X.t() @ (dg @ gateB.t())
+        d_gateB = (gateA.t() @ X.t()) @ dg
         d_gateA *= gateS
         d_gateB *= gateS