diff --git a/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py b/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py
index 30a13196d9..29927c832a 100644
--- a/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py
+++ b/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py
@@ -137,6 +137,14 @@ def fasterprune(
             if sparsity >= SPARSITY_THRESHOLD
             else None
         )
+        
+        g_idx = []
+        if actorder:
+            g_idx = [perm[i] // quant_scheme.weights.group_size for i in range(self.columns)]
+            g_idx = g_idx[invperm]
+        else:
+            g_idx = [i // quant_scheme.weights.group_size for i in range(self.columns)]         
+        g_idx = torch.tensor(g_idx, dtype=torch.int32, device=W.device)
 
         # See section 3.4 of https://arxiv.org/abs/2203.07259
         for i1 in range(0, self.columns, blocksize):
@@ -148,6 +156,15 @@ def fasterprune(
             Err1 = torch.zeros_like(W1)
             Losses1 = torch.zeros_like(W1)
             Hinv1 = Hinv[i1:i2, i1:i2]
+            
+            # """
+            # if not channel wise
+            
+            # strategy = quant_scheme.weights.strategy
+            # if strategy is not QuantizationStrategy.CHANNEL:
+            #     idx = i
+            
+            # """
 
             if sparsity >= SPARSITY_THRESHOLD:
                 tmp = (
@@ -176,6 +193,7 @@ def fasterprune(
                     else:
                         q = torch.quantize_per_channel(q, scale, zero_point, 0, dtype)
                     q = torch.dequantize(q)
+
                 elif hasattr(self.layer, "quantization_scheme"):
                     quant_scheme = self.layer.quantization_scheme
                     if quant_scheme.weights is not None:
@@ -235,9 +253,11 @@ def fasterprune(
 
         _LOGGER.info("time %.2f" % (time.time() - tick))
         _LOGGER.info("error %.2f" % torch.sum(Losses).item())
-
+        
+       
         if actorder:
             W = W[:, invperm]
+            # g_idx = g_idx[invperm]
 
         if isinstance(self.layer, transformers.Conv1D):
             W = W.t()
@@ -247,6 +267,7 @@ def fasterprune(
         # place, clone() or direct assignment won't work
         self.layer.weight -= self.layer.weight
         self.layer.weight += W
+        self.g_idx = g_idx
 
     def free(self):
         """