diff --git a/docs/source/3x/PT_WeightOnlyQuant.md b/docs/source/3x/PT_WeightOnlyQuant.md
index 2406d8d4150..8f7d1d70ba3 100644
--- a/docs/source/3x/PT_WeightOnlyQuant.md
+++ b/docs/source/3x/PT_WeightOnlyQuant.md
@@ -178,6 +178,8 @@ model = convert(model, config)  # after this step, the model is ready for W4A8 i
 |             not_use_best_mse (bool)         |  Whether to use mean squared   error                                                       | False     |
 |             dynamic_max_gap (int)           |  The dynamic maximum gap                                                                   | -1        |
 |             scale_dtype (str)               | The data type of quantization scale to be used, different kernels have   different choices | "float16" |
+|             scheme (str)                    | A preset scheme that defines the quantization configurations.                              | "W4A16"   |
+|             layer_config (dict)             | Layer-wise quantization config                                                             | None      |
 
 ``` python
 # Quantization code
@@ -283,6 +285,23 @@ quant_config = RTNConfig()
 lm_head_config = RTNConfig(dtype="fp32")
 quant_config.set_local("lm_head", lm_head_config)
 ```
+3. Example of using `layer_config` for AutoRound
+```python
+# layer_config = {
+#      "layer1": {
+#          "data_type": "int",
+#          "bits": 3,
+#          "group_size": 128,
+#          "sym": True,
+#      },
+#      "layer2": {
+#          "W8A16"
+#       }
+# }
+# Use the AutoRound specific 'layer_config' instead of the 'set_local' API.
+layer_config = {"lm_head": {"data_type": "int"}}
+quant_config = AutoRoundConfig(layer_config=layer_config)
+```
 
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