diff --git a/gptqmodel/models/base.py b/gptqmodel/models/base.py
index e81ad7883..b0f9a84f2 100644
--- a/gptqmodel/models/base.py
+++ b/gptqmodel/models/base.py
@@ -691,7 +691,8 @@ def save_quantized(
             state_dict = {k: v.clone().contiguous() for k, v in state_dict.items()}
             model_save_name = model_base_name + ".safetensors"
         else:
-            model_save_name = model_base_name + ".bin"
+            model_save_name = model_base_name + ".pt"
+
         if not self.qlinear_kernel.SUPPORTS_SHARDS and max_shard_size is not None:
             logger.warning("Sharding is not supported for this quant. Disabling sharding.")
             max_shard_size = None
@@ -1106,7 +1107,6 @@ def from_quantized(
         use_safetensors: bool = True,
         trust_remote_code: bool = False,
         format: Optional[FORMAT] = None,
-        allow_unsafe_loading: bool = False,
         verify_hash: Optional[Union[str, List[str]]] = None,
         **kwargs,
     ):
@@ -1247,7 +1247,7 @@ def from_quantized(
         if use_safetensors:
             extensions.append(".safetensors")
         else:
-            extensions += [".bin", ".pt"]
+            extensions += [".pt", ".pth"]
 
         model_name_or_path = str(model_name_or_path)
 
@@ -1260,14 +1260,9 @@ def from_quantized(
 
         # bin files have security issues: disable loading by default
         if ".bin" in resolved_archive_file:
-            if allow_unsafe_loading:
-                logger.warning(
-                    "There are security risks when loading tensors from .bin files. Make sure you are loading model only from a trusted source."
-                )
-            else:
-                raise ValueError(
-                    "Loading of unsafe .bin files are not allowed by default. Pass allow_unsafe_loading=True to bypass."
-                )
+            raise ValueError(
+                "Loading of .bin files are not allowed due to safety. Please convert your model to safetensor or pytorch format."
+            )
 
         quantize_config.runtime_format = quantize_config.format
 
diff --git a/gptqmodel/utils/model.py b/gptqmodel/utils/model.py
index 87225f1da..852ec3af4 100644
--- a/gptqmodel/utils/model.py
+++ b/gptqmodel/utils/model.py
@@ -543,7 +543,7 @@ def get_checkpoints(
     model_name_or_path: str, extensions: List[str], **cached_file_kwargs
 ):
     """
-    Retrives (and if necessary downloads from Hugging Face Hub) the model checkpoint. Sharding is supported. All the `possible_model_basenames` (e.g. `["model", "model-4bit-gptq"]`) will be explored over all `extensions` (e.g. `[".bin", ".safetensors"]`).
+    Retrives (and if necessary downloads from Hugging Face Hub) the model checkpoint. Sharding is supported. All the `possible_model_basenames` (e.g. `["model", "model-4bit-gptq"]`) will be explored over all `extensions` (e.g. `[".safetensors"]`).
     """
     searched_files = []
     resolved_archive_file = None
diff --git a/tests/test_pt.py b/tests/test_pt.py
new file mode 100644
index 000000000..52294f42f
--- /dev/null
+++ b/tests/test_pt.py
@@ -0,0 +1,37 @@
+import torch
+import unittest
+
+from transformers import AutoTokenizer
+
+from gptqmodel import GPTQModel, QuantizeConfig
+
+pretrained_model_id = "facebook/opt-125m"
+quantized_model_id = "facebook-opt-125m"
+
+class Test_save_load_pt_weight(unittest.TestCase):
+    def test_pt(self):
+        tokenizer = AutoTokenizer.from_pretrained(pretrained_model_id, use_fast=True)
+        calibration_dataset = [
+            tokenizer(
+                "gptqmodel is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."
+            )
+        ]
+
+        reference_output = "</s>gptqmodel is an easy-to-use model for creating a variety of a variety"
+
+        quantize_config = QuantizeConfig(
+            bits=4,
+            group_size=128,
+        )
+
+        model = GPTQModel.from_pretrained(pretrained_model_id, quantize_config)
+
+        model.quantize(calibration_dataset)
+
+        model.save_quantized(quantized_model_id, use_safetensors=False)
+
+        model = GPTQModel.from_quantized(quantized_model_id, device="cuda:0", use_safetensors=False)
+
+        result = tokenizer.decode(model.generate(**tokenizer("gptqmodel is an easy-to-use model", return_tensors="pt").to(model.device))[0])
+
+        self.assertEqual(result, reference_output)