diff --git a/unsloth/kernels/fast_lora.py b/unsloth/kernels/fast_lora.py
index 3ed0d3c914..6568bba681 100644
--- a/unsloth/kernels/fast_lora.py
+++ b/unsloth/kernels/fast_lora.py
@@ -183,8 +183,8 @@ def apply_lora_mlp_swiglu(self, X):
 pass
 
 
-from .geglu import geglu_forward_kernel, geglu_backward_kernel
-def apply_lora_mlp_geglu(self, X):
+from .geglu import geglu_exact_forward_kernel, geglu_exact_backward_kernel
+def apply_lora_mlp_geglu_exact(self, X):
     gateW, gateW_quant, gateA, gateB, gateS = get_lora_parameters(self.gate_proj)
     upW,     upW_quant,   upA,   upB,   upS = get_lora_parameters(self.  up_proj)
     downW, downW_quant, downA, downB, downS = get_lora_parameters(self.down_proj)
@@ -192,7 +192,21 @@ def apply_lora_mlp_geglu(self, X):
                          gateW, gateW_quant, gateA, gateB, gateS,
                          upW,     upW_quant, upA,   upB,   upS,
                          downW, downW_quant, downA, downB, downS,
-                         geglu_forward_kernel, geglu_backward_kernel,)
+                         geglu_exact_forward_kernel, geglu_exact_backward_kernel,)
+    return out
+pass
+
+
+from .geglu import geglu_approx_forward_kernel, geglu_approx_backward_kernel
+def apply_lora_mlp_geglu_approx(self, X):
+    gateW, gateW_quant, gateA, gateB, gateS = get_lora_parameters(self.gate_proj)
+    upW,     upW_quant,   upA,   upB,   upS = get_lora_parameters(self.  up_proj)
+    downW, downW_quant, downA, downB, downS = get_lora_parameters(self.down_proj)
+    out = LoRA_MLP.apply(X,
+                         gateW, gateW_quant, gateA, gateB, gateS,
+                         upW,     upW_quant, upA,   upB,   upS,
+                         downW, downW_quant, downA, downB, downS,
+                         geglu_approx_forward_kernel, geglu_approx_backward_kernel,)
     return out
 pass
 
diff --git a/unsloth/kernels/geglu.py b/unsloth/kernels/geglu.py
index 0a952a10a9..1d29db8f5b 100644
--- a/unsloth/kernels/geglu.py
+++ b/unsloth/kernels/geglu.py
@@ -38,7 +38,7 @@ def _exact_forward_kernel(e, g, h, n_elements, BLOCK_SIZE : tl.constexpr,):
 pass
 
 
-def geglu_forward_kernel(gate, up):
+def geglu_exact_forward_kernel(gate, up):
     batch, seq_len, hd = gate.shape
     n_elements = gate.numel()
     out = torch.empty((batch, seq_len, hd), dtype = gate.dtype, device = "cuda")
@@ -95,7 +95,7 @@ def _exact_backward_kernel(DW, e, g, n_elements, BLOCK_SIZE : tl.constexpr,):
 pass
 
 
-def geglu_backward_kernel(DW, e, g):
+def geglu_exact_backward_kernel(DW, e, g):
     batch_seq_len, hd = e.shape
     n_elements = e.numel()
     grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
@@ -171,7 +171,7 @@ def _approx_backward_kernel(DW, e, g, n_elements, BLOCK_SIZE : tl.constexpr,):
     T = 1.0 + tl.math.tanh(a + b)
     T2 = 0.5 * T
     # Q = -T * (T - 2.0) * (a + 3.0 * b)
-    Q2 = -T2 * (T - 2.0) * (a + 3.0 * b)
+    Q2 = -T2 * (T - 2.0) * (a + 3.0 * b) 
     df_de = T2 + Q2 # 1/2 * (T + Q)
 
     # f = 1/2 * e * (1 + tanh( sqrt(2/pi) * (x + 0.044715 * x^3 ) ))
@@ -192,3 +192,12 @@ def _approx_backward_kernel(DW, e, g, n_elements, BLOCK_SIZE : tl.constexpr,):
     tl.store(e  + offsets, df_row, mask = mask) # df = DW * f
     tl.store(g  + offsets, de_row, mask = mask) # de
 pass
+
+
+def geglu_approx_backward_kernel(DW, e, g):
+    batch_seq_len, hd = e.shape
+    n_elements = e.numel()
+    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
+    _approx_backward_kernel[grid](DW, e, g, n_elements, BLOCK_SIZE = 1024,)
+    return DW, e, g
+pass
diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index 54e016d1af..87ca5d8487 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -1335,7 +1335,7 @@ class FastLlamaModel:
 
         if   model_type == "llama":   apply_lora_mlp = apply_lora_mlp_swiglu
         elif model_type == "mistral": apply_lora_mlp = apply_lora_mlp_swiglu
-        elif model_type == "gemma":   apply_lora_mlp = apply_lora_mlp_geglu
+        elif model_type == "gemma":   apply_lora_mlp = apply_lora_mlp_geglu_approx
         else:
             raise NotImplementedError(f"Unsloth: {model_type} is not yet implemented!")
         pass