diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 370ab1bf358..f0c8966e27e 100755
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -19,6 +19,7 @@ Changelog
 
 *Misc*
 
+- Add ``nodes_to_exclude`` regex support to the Q/DQ-aware ONNX ``convert_to_f16`` API, matching ``convert_to_mixed_precision`` node-name exclusion semantics while composing with the existing operation and tensor block lists.
 - Add ``modelopt.torch.utils.mlflow.MlflowRunLogger`` for recording a script run on an MLflow tracking server: the invocation, the ModelOpt version, the run log (captured by teeing ``stdout``/``stderr``) and any caller-supplied artifacts, with configuration as searchable params. ``mlflow`` is an optional dependency, imported only when tracking is enabled.
 - Add ``--mlflow <tracking-uri>`` to ``examples/hf_ptq/hf_ptq.py`` (MLflow's own ``MLFLOW_TRACKING_URI`` is honoured too). A tracked run records the invocation, the resolved recipe (``$import``\ s expanded), the run log and the quantization summaries, with every command-line argument as a searchable param; failed runs are recorded with their traceback. The experiment defaults to ``$USER/hf_ptq/<checkpoint basename>-<recipe name or --qformat>`` and can be overridden with ``--mlflow_experiment`` / ``--mlflow_run_name``.
 - Add ``--mlflow <tracking-uri>`` to ``examples/vllm_serve/vllm_serve_fakequant.py`` (MLflow's own ``MLFLOW_TRACKING_URI`` is honoured too), so a fake-quant serve records what it quantized and an evaluation of that endpoint can be traced back to a recipe. A tracked run uploads the launcher command, the resolved ``RECIPE_PATH`` (or the merged ``QUANT_CFG``/``KV_QUANT_CFG`` when presets are used), the worker log and the quantizer summary; the experiment defaults to ``$USER/vllm_serve_fakequant/<model basename>-<recipe name or quantization config>`` and can be overridden with ``--mlflow-experiment`` / ``--mlflow-run-name``.
diff --git a/modelopt/onnx/autocast/convert.py b/modelopt/onnx/autocast/convert.py
index d9bc8d68c99..65cabe86974 100644
--- a/modelopt/onnx/autocast/convert.py
+++ b/modelopt/onnx/autocast/convert.py
@@ -32,7 +32,11 @@
 import modelopt.onnx.utils as onnx_utils
 from modelopt.onnx.autocast.graphsanitizer import GraphSanitizer
 from modelopt.onnx.autocast.logging_config import logger
-from modelopt.onnx.autocast.nodeclassifier import NodeClassifier, NodeRuleBase
+from modelopt.onnx.autocast.nodeclassifier import (
+    DisabledNodeNameRegexRule,
+    NodeClassifier,
+    NodeRuleBase,
+)
 from modelopt.onnx.autocast.precisionconverter import PrecisionConverter
 from modelopt.onnx.autocast.referencerunner import ReferenceRunner
 from modelopt.onnx.utils import get_min_opset_for_precisions, get_qdq_precisions
@@ -221,6 +225,7 @@ def convert_to_f16(
     trt_plugins: list[str] | None = [],
     use_standalone_type_inference: bool = False,
     opset: int | None = None,
+    nodes_to_exclude: list[str] | None = None,
 ) -> onnx.ModelProto:
     """Convert model to mixed precision, using PrecisionConverter.
 
@@ -240,6 +245,7 @@ def convert_to_f16(
                (22 for bf16, 19 for fp16) and Q/DQ node requirements. The opset may be automatically
                increased if Q/DQ nodes in the model require a higher version (e.g., FP8 requires 19,
                INT4 requires 21, NVFP4 requires 23).
+        nodes_to_exclude: List of regex patterns to match node names that should remain in FP32.
     """
     assert low_precision_type in ["fp16", "bf16"], "low_precision_type must be either fp16 or bf16"
     original_network_io_metadata = _capture_network_io_metadata(model, keep_io_types)
@@ -303,9 +309,15 @@ def convert_to_f16(
         use_standalone_type_inference=use_standalone_type_inference,
         original_network_io_metadata=original_network_io_metadata,
     )
-    high_precision_nodes = [node.name for node in model.graph.node if node.op_type in op_block_list]
+    node_name_rule = DisabledNodeNameRegexRule(nodes_to_exclude or [])
+    high_precision_nodes = [
+        node.name
+        for node in model.graph.node
+        if node.op_type in op_block_list or node_name_rule.check(node)
+    ]
+    high_precision_node_set = set(high_precision_nodes)
     low_precision_nodes = [
-        node.name for node in model.graph.node if node.op_type not in op_block_list
+        node.name for node in model.graph.node if node.name not in high_precision_node_set
     ]
     model_mod = precision_converter.convert(high_precision_nodes, low_precision_nodes)
     return model_mod
diff --git a/tests/unit/onnx/autocast/test_precisionconverter.py b/tests/unit/onnx/autocast/test_precisionconverter.py
index b480bb7c24e..d5ed09215fc 100644
--- a/tests/unit/onnx/autocast/test_precisionconverter.py
+++ b/tests/unit/onnx/autocast/test_precisionconverter.py
@@ -2220,6 +2220,25 @@ def test_convert_to_f16_restores_public_io_metadata_from_entry_boundary():
     onnx.checker.check_model(converted, full_check=True)
 
 
+def test_convert_to_f16_combines_op_and_node_exclusions(simple_model):
+    model, *_ = simple_model
+    converted = convert_to_f16(
+        model,
+        keep_io_types=False,
+        op_block_list=["MatMul"],
+        nodes_to_exclude=[r"^add$"],
+    )
+
+    value_types = {
+        value.name: value.type.tensor_type.elem_type
+        for value in (*converted.graph.output, *converted.graph.value_info)
+    }
+    assert value_types["gemm_output"] == TensorProto.FLOAT
+    assert value_types["add_output"] == TensorProto.FLOAT
+    assert value_types["Y"] == TensorProto.FLOAT16
+    onnx.checker.check_model(converted, full_check=True)
+
+
 def test_convert_to_f16_refreshes_gathernd_pre_cast_declaration(monkeypatch):
     def discover_test_plugins_without_trt(self):
         self.custom_ops = {