diff --git a/onnxruntime/python/tools/quantization/quantize_weight_only.py b/onnxruntime/python/tools/quantization/quantize_weight_only.py
index 6c7fc4d63a00c..2ad4c819270c3 100644
--- a/onnxruntime/python/tools/quantization/quantize_weight_only.py
+++ b/onnxruntime/python/tools/quantization/quantize_weight_only.py
@@ -2,6 +2,7 @@
 import logging
 import importlib
 from pathlib import Path
+from packaging import version
 from .calibrate import CalibrationDataReader
 from .quant_utils import load_model_with_shape_infer
 
@@ -42,6 +43,7 @@ def __init__(
         self,
         group_size=32,
         scheme="sym",
+        ratios={},
         use_external_data_format=False,
     ):
         """
@@ -63,6 +65,7 @@ def __init__(
             scheme=scheme,
             use_external_data_format=use_external_data_format
         )
+        self.ratios = ratios
 
 class GPTQWeightOnlyQuantConfig(WeightOnlyQuantConfig):
     def __init__(
@@ -131,9 +134,9 @@ def _generate_weight_only_node_config(model, group_size, scheme):
         dict: weight only quant configuration for nodes.
     """
     weight_only_node_config = {}
-    template_config = {'weight': {"bits": 4, "group_size": group_size, "scheme": scheme}}
+    template_config = {"bits": 4, "group_size": group_size, "scheme": scheme}
     for node in model.graph.node:
-        if node.op_type in ["MatMul"]: # TODO: enable Gemm op support
+        if node.op_type in ["MatMul"]:
             weight_only_node_config[node.name] = template_config
     return weight_only_node_config
 
@@ -156,11 +159,15 @@ def quantize_weight_only(
         RuntimeError: Raise RuntimeError if neural-compressor is not correctly installed.
     """
     try:
-        importlib.import_module("neural_compressor.adaptor.ox_utils.weight_only")
+        importlib.import_module("neural_compressor")
     except Exception as e:
         logging.error(f"{e}.")
         raise RuntimeError("neural-compressor is not correctly installed. Please check your environment.") from e
     
+    import neural_compressor
+    assert version.parse(neural_compressor.__version__) >= version.parse("2.3.0"), \
+    "Require neural-compressor >= 2.3.0 to support weight only quantization!"
+    
     def inc_dataloader():
         data_reader = copy.deepcopy(weight_only_config.calibration_data_reader)
         for data in data_reader:
@@ -174,8 +181,11 @@ def inc_dataloader():
     algorithm = weight_only_config.algorithm
     if algorithm == "RTN":
         from neural_compressor.adaptor.ox_utils.weight_only import rtn_quantize
+        ratios = weight_only_config.ratios
+
         model = rtn_quantize(model=model_input, 
-                             tune_cfg=weight_only_node_config)
+                             weight_config=weight_only_node_config,
+                             ratios=ratios)
     elif algorithm == "GPTQ":
         from neural_compressor.adaptor.ox_utils.weight_only import gptq_quantize
         percdamp = weight_only_config.percdamp
@@ -186,7 +196,7 @@ def inc_dataloader():
         dataloader = inc_dataloader()
 
         model = gptq_quantize(model=model_input,
-                              tune_cfg=weight_only_node_config,
+                              weight_config=weight_only_node_config,
                               dataloader=dataloader,
                               n_samples=-1,
                               percdamp=percdamp,
diff --git a/onnxruntime/test/python/quantization/test_quantize_weight_only.py b/onnxruntime/test/python/quantization/test_quantize_weight_only.py
index 5fb1201788623..e2146a98b0f43 100644
--- a/onnxruntime/test/python/quantization/test_quantize_weight_only.py
+++ b/onnxruntime/test/python/quantization/test_quantize_weight_only.py
@@ -33,12 +33,12 @@ def construct_model(output_model_path):
     initializers = []
     
     # make mul node
-    mul_data = np.random.normal(0, 0.1, [1, 10]).astype(np.float32)
+    mul_data = np.random.normal(0, 0.1, [1, 32]).astype(np.float32)
     initializers.append(onnx.numpy_helper.from_array(mul_data, name="mul.data"))
     mul_node = onnx.helper.make_node("Mul", ["input", "mul.data"], ["mul.output"], "Mul_0")
 
     # make matmul node
-    matmul_weight = np.random.normal(0, 0.1, [10, 1]).astype(np.float32)
+    matmul_weight = np.random.normal(0, 0.1, [32, 1]).astype(np.float32)
     initializers.append(onnx.numpy_helper.from_array(matmul_weight, name="matmul.weight"))
     matmul_node = onnx.helper.make_node("MatMul", 
                                         ["mul.output", "matmul.weight"], 
@@ -46,7 +46,7 @@ def construct_model(output_model_path):
                                         "MatMul_1")
 
     # make graph
-    input_tensor = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10])
+    input_tensor = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 32])
     output_tensor = helper.make_tensor_value_info("output", TensorProto.FLOAT, [1, 1])
     graph_name = "weight_only_quant_test"
     graph = helper.make_graph(
@@ -91,7 +91,7 @@ def test_quantize_weight_only_rtn(self):
             self,
             self._model_fp32_path,
             self._model_weight_only_path,
-            {"input": np.random.rand(1, 10).astype(np.float32)},
+            {"input": np.random.rand(1, 32).astype(np.float32)},
         )
 
         model_fp32 = ONNXModel(onnx.load(self._model_fp32_path))
@@ -108,14 +108,14 @@ def test_quantize_weight_only_gptq(self):
         if not find_spec("neural_compressor"):
             self.skipTest("skip test_quantize_weight_only_gptq since neural_compressor is not installed")
         
-        data_reader = input_feeds_neg_one_zero_one(10, {"input": [1, 10]})
+        data_reader = input_feeds_neg_one_zero_one(10, {"input": [1, 32]})
         weight_only_config = GPTQWeightOnlyQuantConfig(data_reader)
         quantize_weight_only(self._model_fp32_path, self._model_weight_only_path, weight_only_config)
         check_model_correctness(
             self,
             self._model_fp32_path,
             self._model_weight_only_path,
-            {"input": np.random.rand(1, 10).astype(np.float32)},
+            {"input": np.random.rand(1, 32).astype(np.float32)},
         )
 
         model_fp32 = ONNXModel(onnx.load(self._model_fp32_path))