diff --git a/tests/quantization/autoawq/test_awq.py b/tests/quantization/autoawq/test_awq.py
index f18f477b3944..56d411fde60c 100644
--- a/tests/quantization/autoawq/test_awq.py
+++ b/tests/quantization/autoawq/test_awq.py
@@ -110,7 +110,8 @@ class AwqTest(unittest.TestCase):
     )
 
     EXPECTED_OUTPUT_BF16 = [
-        "Hello my name is Katie and I am a 20 year old student at the University of North Carolina at Chapel Hill. I am a junior and I am majoring in Journalism and minoring in Spanish"
+        "Hello my name is Katie and I am a 20 year old student at the University of North Carolina at Chapel Hill. I am a junior and I am majoring in Journalism and minoring in Spanish",
+        "Hello my name is Katie and I am a 20 year old student at the University of North Carolina at Chapel Hill. I am a junior and I am majoring in Exercise and Sport Science with a",
     ]
 
     EXPECTED_OUTPUT_EXLLAMA = [
@@ -127,11 +128,7 @@ def setUpClass(cls):
         Setup quantized model
         """
         cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
-        # Use GEMM so that test_save_pretrained() writes out the quantized weights.
-        quantization_config = AwqConfig(backend=AwqBackend.GEMM)
-        cls.quantized_model = AutoModelForCausalLM.from_pretrained(
-            cls.model_name, device_map=cls.device_map, quantization_config=quantization_config
-        )
+        cls.quantized_model = AutoModelForCausalLM.from_pretrained(cls.model_name, device_map=cls.device_map)
 
     def tearDown(self):
         gc.collect()
@@ -142,8 +139,7 @@ def test_quantized_model_conversion(self):
         """
         Simple test that checks if the quantized model has been converted properly
         """
-        from gptqmodel.nn_modules.qlinear.gemm_awq import AwqGEMMQuantLinear
-        from gptqmodel.nn_modules.qlinear.gemv_awq import AwqGEMVQuantLinear
+        from gptqmodel.nn_modules.qlinear import BaseQuantLinear
 
         from transformers.integrations.awq import replace_with_awq_linear
 
@@ -159,10 +155,10 @@ def test_quantized_model_conversion(self):
             if isinstance(module, torch.nn.Linear):
                 nb_linears += 1
 
-        model, _ = replace_with_awq_linear(model, quantization_config=quantization_config)
+        model = replace_with_awq_linear(model, quantization_config=quantization_config)
         nb_awq_linear = 0
         for module in model.modules():
-            if isinstance(module, (AwqGEMMQuantLinear, AwqGEMVQuantLinear)):
+            if isinstance(module, BaseQuantLinear):
                 nb_awq_linear += 1
 
         self.assertEqual(nb_linears, nb_awq_linear)
@@ -171,12 +167,12 @@ def test_quantized_model_conversion(self):
         with torch.device("meta"):
             model = OPTForCausalLM(config)
 
-        model, _ = replace_with_awq_linear(
+        model = replace_with_awq_linear(
             model, quantization_config=quantization_config, modules_to_not_convert=["lm_head"]
         )
         nb_awq_linear = 0
         for module in model.modules():
-            if isinstance(module, (AwqGEMMQuantLinear, AwqGEMVQuantLinear)):
+            if isinstance(module, BaseQuantLinear):
                 nb_awq_linear += 1
 
         self.assertEqual(nb_linears - 1, nb_awq_linear)
@@ -238,8 +234,12 @@ def test_save_pretrained(self):
         """
         Simple test that checks if the quantized model is working properly after being saved and loaded
         """
+        # Load a fresh model for saving — the shared self.quantized_model may have
+        # already been in-place transformed by a prior generate() call, and saving
+        # those transformed buffers then re-transforming on reload would corrupt data.
+        fresh_model = AutoModelForCausalLM.from_pretrained(self.model_name)
         with tempfile.TemporaryDirectory() as tmpdirname:
-            self.quantized_model.save_pretrained(tmpdirname)
+            fresh_model.save_pretrained(tmpdirname)
             model = AutoModelForCausalLM.from_pretrained(tmpdirname, device_map=self.device_map)
 
             input_ids = self.tokenizer(self.input_text, return_tensors="pt").to(torch_device)