diff --git a/gptqmodel/integration/optimum/utils.py b/gptqmodel/integration/optimum/utils.py
index 26c9324e..782672db 100644
--- a/gptqmodel/integration/optimum/utils.py
+++ b/gptqmodel/integration/optimum/utils.py
@@ -117,6 +117,91 @@ def get_seqlen(model: nn.Module):
 
 
 def monkey_patch_gptqmodel_into_transformers():
+    # monkey_patch transformers.utils.quantization_config.GPTQConfig.post_init()
+    # Because it checks the auto_gptq version
+    def post_init(self):
+        r"""
+        Safety checker that arguments are correct
+        """
+        from packaging import version
+        import importlib
+        from transformers.utils.quantization_config import ExllamaVersion
+        print("monkey patch postin")
+        if self.bits not in [2, 3, 4, 8]:
+            raise ValueError(f"Only support quantization to [2,3,4,8] bits but found {self.bits}")
+        if self.group_size != -1 and self.group_size <= 0:
+            raise ValueError("group_size must be greater than 0 or equal to -1")
+        if not (0 < self.damp_percent < 1):
+            raise ValueError("damp_percent must between 0 and 1.")
+        if self.dataset is not None:
+            if isinstance(self.dataset, str):
+                if self.dataset in ["ptb", "ptb-new"]:
+                    raise ValueError(
+                        f"""{self.dataset} dataset was deprecated. You can only choose between
+                        ['wikitext2','c4','c4-new']"""
+                    )
+                if self.dataset not in ["wikitext2", "c4", "c4-new"]:
+                    raise ValueError(
+                        f"""You have entered a string value for dataset. You can only choose between
+                        ['wikitext2','c4','c4-new'], but we found {self.dataset}"""
+                    )
+            elif not isinstance(self.dataset, list):
+                raise ValueError(
+                    f"""dataset needs to be either a list of string or a value in
+                    ['wikitext2','c4','c4-new'], but we found {self.dataset}"""
+                )
+
+        if self.disable_exllama is None and self.use_exllama is None:
+            # New default behaviour
+            self.use_exllama = True
+        elif self.disable_exllama is not None and self.use_exllama is None:
+            # Follow pattern of old config
+            logger.warning(
+                "Using `disable_exllama` is deprecated and will be removed in version 4.37. Use `use_exllama` instead and specify the version with `exllama_config`."
+                "The value of `use_exllama` will be overwritten by `disable_exllama` passed in `GPTQConfig` or stored in your config file."
+            )
+            self.use_exllama = not self.disable_exllama
+            self.disable_exllama = None
+        elif self.disable_exllama is not None and self.use_exllama is not None:
+            # Only happens if user explicitly passes in both arguments
+            raise ValueError("Cannot specify both `disable_exllama` and `use_exllama`. Please use just `use_exllama`")
+
+        if self.exllama_config is None:
+            self.exllama_config = {"version": ExllamaVersion.ONE}
+        else:
+            if "version" not in self.exllama_config:
+                raise ValueError("`exllama_config` needs to have a `version` key.")
+            elif self.exllama_config["version"] not in [ExllamaVersion.ONE, ExllamaVersion.TWO]:
+                exllama_version = self.exllama_config["version"]
+                raise ValueError(
+                    f"Only supported versions are in [ExllamaVersion.ONE, ExllamaVersion.TWO] - not recognized version {exllama_version}"
+                )
+
+        if self.bits == 4 and self.use_exllama:
+            if self.exllama_config["version"] == ExllamaVersion.ONE:
+                logger.info(
+                    "You have activated exllama backend. Note that you can get better inference "
+                    "speed using exllamav2 kernel by setting `exllama_config`."
+                )
+            elif self.exllama_config["version"] == ExllamaVersion.TWO:
+                optimum_version = version.parse(importlib.metadata.version("optimum"))
+                # autogptq_version = version.parse(importlib.metadata.version("auto_gptq"))
+                # if optimum_version <= version.parse("1.13.2") or autogptq_version <= version.parse("0.4.2"):
+                if optimum_version <= version.parse("1.13.2"):
+                    raise ValueError(
+                        # f"You need optimum > 1.13.2 and auto-gptq > 0.4.2 . Make sure to have that version installed - detected version : optimum {optimum_version} and autogptq {autogptq_version}"
+                        f"You need optimum > 1.13.2 . Make sure to have that version installed - detected version : optimum {optimum_version}"
+                    )
+        if self.modules_in_block_to_quantize is not None:
+            optimum_version = version.parse(importlib.metadata.version("optimum"))
+            if optimum_version < version.parse("1.15.0"):
+                raise ValueError(
+                    "You current version of `optimum` does not support `modules_in_block_to_quantize` quantization argument, please upgrade `optimum` package to a version superior than 1.15.0 ."
+                )
+
+    from transformers.utils.quantization_config import GPTQConfig
+    GPTQConfig.post_init = post_init
+
     from transformers.quantizers import auto
 
     from .hf_quantizer_gptq import GptqHfQuantizer
diff --git a/tests/test_transformers_integration.py b/tests/test_transformers_integration.py
index 8109f842..0e2d8159 100644
--- a/tests/test_transformers_integration.py
+++ b/tests/test_transformers_integration.py
@@ -97,7 +97,6 @@ def test_quant_and_load(self, exllama_version):
             self.assertResult(model, tokenizer, True, exllama_version, reference_output)
 
     def assertResult(self, model, tokenizer, load_quant_model, exllama_version, reference_output):
-        print("qlinear type", type(model.model.decoder.layers[0].self_attn.k_proj))
         if exllama_version == 1:
             self.assertIsInstance(model.model.decoder.layers[0].self_attn.k_proj, ExllamaQuantLinear)
         elif exllama_version == 2: