diff --git a/server/text_generation_server/utils/awq/quantize/qmodule.py b/server/text_generation_server/utils/awq/quantize/qmodule.py
index e157ce55..fb1adf5c 100644
--- a/server/text_generation_server/utils/awq/quantize/qmodule.py
+++ b/server/text_generation_server/utils/awq/quantize/qmodule.py
@@ -1,4 +1,4 @@
-# Copied from https://github.com/mit-han-lab/llm-awq/blob/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa/awq/quantize/qmodule.py
+# Copied logic from https://github.com/mit-han-lab/llm-awq/blob/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa/awq/quantize/qmodule.py
 
 import math
 import torch
@@ -17,77 +17,29 @@ class ScaledActivation(nn.Module):
 
 
 class WQLinear(nn.Module):
-    def __init__(self, w_bit, group_size, in_features, out_features, bias, dev):
+    def __init__(self, w_bit, group_size, qweight, qzeros, scales, bias):
         super().__init__()
         
         if w_bit not in [4]:
             raise NotImplementedError("Only 4-bit are supported for now.")
         
-        self.in_features = in_features
-        self.out_features = out_features
+        self.in_features = qweight.shape[0]
+        self.out_features = qweight.shape[1] * 32 // w_bit
+
         self.w_bit = w_bit
-        self.group_size = group_size if group_size != -1 else in_features
+        self.group_size = group_size if group_size != -1 else self.in_features
         # quick sanity check (make sure aligment)
         assert self.in_features % self.group_size == 0
-        assert out_features % (32 // self.w_bit) == 0
+        assert self.out_features % (32 // self.w_bit) == 0
 
-        self.register_buffer('qweight', torch.zeros((in_features, out_features // (32 // self.w_bit)), dtype=torch.int32, device=dev))
-        self.register_buffer('qzeros', torch.zeros((in_features // self.group_size, out_features // (32 // self.w_bit)), dtype=torch.int32, device=dev))
-        self.register_buffer('scales', torch.zeros((in_features // self.group_size, out_features), dtype=torch.float16, device=dev))
+        self.register_buffer('qweight', qweight)
+        self.register_buffer('qzeros', qzeros)
+        self.register_buffer('scales', scales)
         if bias:
-            self.register_buffer('bias', torch.zeros((out_features), dtype=torch.float16, device=dev))
+            self.register_buffer('bias', bias)
         else:
             self.bias = None
 
-    @classmethod
-    def from_linear(cls, linear, w_bit, group_size, init_only=False, scales=None, zeros=None):
-        awq_linear = cls(w_bit, group_size, linear.in_features, linear.out_features, linear.bias is not None, linear.weight.device)
-        if init_only:  # just prepare for loading sd
-            return awq_linear
-        
-        # need scales and zeros info for real quantization
-        assert scales is not None and zeros is not None  
-        scale_zeros = zeros * scales
-        
-        awq_linear.scales = scales.clone().half()
-        if linear.bias is not None:
-            awq_linear.bias = linear.bias.clone().half()
-
-        pack_num = 32 // awq_linear.w_bit
-        
-        intweight = []
-        for idx in range(awq_linear.in_features):
-            intweight.append(torch.round((linear.weight.data[:, idx] + scale_zeros[idx // group_size]) / awq_linear.scales[idx // group_size]).to(torch.int)[:, None])
-        intweight = torch.cat(intweight, dim=1)
-        intweight = intweight.t().contiguous()
-        intweight = intweight.to(dtype=torch.int32)
-        qweight = torch.zeros((intweight.shape[0], intweight.shape[1] // 32 * awq_linear.w_bit), dtype=torch.int32, device=intweight.device)           
-         
-        for col in range(intweight.shape[1] // pack_num):
-            if awq_linear.w_bit == 4:
-                order_map = [0, 2, 4, 6, 1, 3, 5, 7]
-            else:
-                raise NotImplementedError("Only 4-bit are supported for now.")
-            for i in range(pack_num):
-                qweight_col = intweight[:, col * pack_num + order_map[i]]
-                qweight[:, col] |= qweight_col << (i * awq_linear.w_bit)
-        awq_linear.qweight = qweight
-
-        zeros = zeros.to(dtype=torch.int32)
-        qzeros = torch.zeros((zeros.shape[0], zeros.shape[1] // 32 * awq_linear.w_bit), dtype=torch.int32, device=zeros.device)
-        
-        for col in range(zeros.shape[1] // pack_num):     
-            if awq_linear.w_bit == 4:
-                order_map = [0, 2, 4, 6, 1, 3, 5, 7]
-            else:
-                raise NotImplementedError("Only 4-bit are supported for now.")
-            for i in range(pack_num):
-                qzero_col = zeros[:, col * pack_num + order_map[i]]
-                qzeros[:, col] |= qzero_col << (i * awq_linear.w_bit)
-        awq_linear.qzeros = qzeros
-        
-        return awq_linear
-
     @torch.no_grad()
     def forward(self, x):
         out_shape = x.shape[:-1] + (self.out_features, )
diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py
index f0227177..3fb3766a 100644
--- a/server/text_generation_server/utils/layers.py
+++ b/server/text_generation_server/utils/layers.py
@@ -256,12 +256,7 @@ def get_linear(weight, bias, quantize):
             raise NotImplementedError(
                 f"The passed weight is not `awq` compatible, loader needs to be updated."
             )
-        in_features = qweight.shape[0]
-        out_features = qweight.shape[1] * 32 // bits
-        linear = WQLinear(w_bit=bits, group_size=groupsize, in_features=in_features, out_features=out_features, bias=bias is not None, dev=qweight.device)
-        linear.qweight = qweight
-        linear.qzeros = qzeros
-        linear.scales = scales
+        linear = WQLinear(w_bit=bits, group_size=groupsize, qweight=qweight, qzeros=qzeros, scales=scales, bias=bias is not None)
     else:
         raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.")
     return linear