diff --git a/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py b/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py
index 8b6ae1e5e0..5e8052ffe7 100644
--- a/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py
+++ b/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py
@@ -17,6 +17,7 @@
 from sparseml.modifiers.utils import SPARSITY_THRESHOLD
 from sparseml.modifiers.utils.compression_wrapper import ModuleCompressionWrapper
 
+from torch.nn import Parameter
 
 try:
     import transformers
@@ -119,6 +120,7 @@ def fasterprune(
         self.H[dead, dead] = 1
         W[:, dead] = 0
 
+        # Or read from self.layer.quantization_scheme
         if actorder:
             perm = torch.argsort(torch.diag(self.H), descending=True)
             W = W[:, perm]
@@ -135,15 +137,6 @@ def fasterprune(
         self.H = torch.linalg.cholesky(self.H, upper=True)
         Hinv = self.H
 
-
-        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):
             i2 = min(i1 + blocksize, self.columns)
@@ -154,15 +147,6 @@ 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 preserve_zeros:
                 W1_nz_mask = W_nz_mask[:, i1:i2]
@@ -189,6 +173,18 @@ def fasterprune(
                     if quant_scheme.weights is not None:
                         scale = self.layer.weight_scale
                         zero_point = self.layer.weight_zero_point
+
+                        group_size = quant_scheme.weights.group_size
+                        if group_size is None or group_size == -1:
+                            group_size = self.layer.weight.shape[1]
+                            
+                        if actorder:
+                            g_idx = torch.Tensor([perm[j] // group_size for j in range(self.columns)],  dtype=torch.int32, device=invperm.device)
+                            g_idx = g_idx[invperm]
+                            self.layer.weight_g_idx = Parameter(g_idx, requires_grad=False,)
+                        else:
+                            g_idx = torch.Tensor([j // group_size for j in range(self.columns)], dtype=torch.int32, device=W.device)
+
                         from compressed_tensors.quantization import QuantizationStrategy
                         from compressed_tensors.quantization.lifecycle.forward import (
                             fake_quantize,
@@ -255,7 +251,6 @@ def fasterprune(
        
         if actorder:
             W = W[:, invperm]
-            # g_idx = g_idx[invperm]
 
         if isinstance(self.layer, transformers.Conv1D):
             W = W.t()
@@ -265,7 +260,6 @@ 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):
         """