diff --git a/gptqmodel/models/base.py b/gptqmodel/models/base.py
index 4bbe1e358..330861666 100644
--- a/gptqmodel/models/base.py
+++ b/gptqmodel/models/base.py
@@ -395,7 +395,7 @@ def collate_batch(batch):
                 raise NotImplementedError(f"This type({type(lm_head_module)}) of lm_head quantization is currently not "
                                           f"supported. SUPPORTS_MODULE_TYPES is {SUPPORTS_MODULE_TYPES}")
 
-            lm_head_quant_config = {"bits": 8, "group_size": 32, "sym": False}
+            lm_head_quant_config = {"bits": 8, "group_size": 32, "sym": True}
             if self.quantize_config.dynamic is None:
                 self.quantize_config.dynamic = {self.lm_head: lm_head_quant_config}
             elif self.quantize_config.dynamic_get(self.lm_head, default_value=None) is None:
@@ -413,6 +413,9 @@ def collate_batch(batch):
         num_batches = len(calibration_dataset)
         layers = get_module_by_name_prefix(self.model, self.layers_node)
 
+        # TODO lm_head need move model to same device
+        self.model.to(self.quantize_config.device)
+
         cur_layer_device = get_device(layers[0])
         data_device = cur_layer_device if calibration_enable_gpu_cache else CPU
 
@@ -445,6 +448,22 @@ def store_input_hook(_, args, kwargs):
                     one_kwargs[k] = nested_move_to(v, data_device)
             layer_input_kwargs.append(one_kwargs)
 
+            if not self.quantize_config.lm_head:
+                raise ValueError
+
+        lm_head_inputs = []
+        def store_lm_head_input_hook(_, args, kwargs):
+            # Positional arguments.
+            lm_head_layer_input = []
+            for inp in args:
+                lm_head_layer_input.append(move_to(inp, data_device))
+            if len(lm_head_layer_input) == 0:
+                # Some models put hidden_states in kwargs instead of args.
+                # For example, gptj ...
+                if kwargs.get("hidden_states") is not None:
+                    lm_head_layer_input.append(move_to(kwargs["hidden_states"], data_device))
+
+            lm_head_inputs.append(lm_head_layer_input)
             raise ValueError
 
         # move layer to target device
@@ -463,6 +482,8 @@ def store_input_hook(_, args, kwargs):
 
         # TODO: make this optional, backporting https://github.com/huggingface/optimum/blob/main/optimum/gptq/quantizer.py
         handle = layers[0].register_forward_pre_hook(store_input_hook, with_kwargs=True)
+        if self.quantize_config.lm_head:
+            lm_head_handle = layers[0].register_forward_pre_hook(store_lm_head_input_hook, with_kwargs=True)
         is_ovis = self.__class__.__name__ == "OvisGPTQ"
         for example in calibration_dataset:
             for k, v in example.items():
@@ -483,6 +504,8 @@ def store_input_hook(_, args, kwargs):
             except ValueError:
                 pass
         handle.remove()
+        if self.quantize_config.lm_head:
+            lm_head_handle.remove()
 
         move_to(layers[0], CPU)
         for module_name in self.base_modules:
@@ -525,9 +548,7 @@ def store_input_hook(_, args, kwargs):
             if is_lm_head:
                 layer_pb.set_description(f"Quantizing lm_head")
                 layer = get_module(self.model, key=self.lm_head)
-                if only_quant_lm_head:
-                    layer_inputs = torch.load(lm_head_layer_inputs_path)
-                    print("loaded lm_head_layer_inputs.pt", layer_inputs)
+                layer_inputs = lm_head_inputs
             else:
                 layer_pb.set_description(f"Quantizing layer {i} of {layer_count - 1}")
                 layer = layers[i]
@@ -744,10 +765,6 @@ def tmp(_, inp: Tuple[torch.Tensor, ...], out: torch.Tensor):
                 [],
             )  # TODO: is it really OK to cache only the first positional argument?
 
-            if i == layer_count - 1:
-                print("saved lm_head_layer_inputs.pt", layer_inputs)
-                torch.save(layer_inputs, lm_head_layer_inputs_path)
-
             torch_empty_cache()
 
         logger.info(f"Quantization summary:\n{self.quant_log}")