diff --git a/examples/models/llama/source_transformation/quantize.py b/examples/models/llama/source_transformation/quantize.py
index 36743bb3b79..2ef016de097 100644
--- a/examples/models/llama/source_transformation/quantize.py
+++ b/examples/models/llama/source_transformation/quantize.py
@@ -136,14 +136,14 @@ def quantize(  # noqa C901
         # Check for required args
         if group_size is None:
             raise Exception("For 8da4w quantization, group size must be specified.")
-        from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
 
-        # 1. Quantize in checkpoint dtype.
-        model = Int8DynActInt4WeightQuantizer(
-            precision=checkpoint_torch_dtype, groupsize=group_size
-        ).quantize(model)
-        # 2. Set the computation dtype (what weights/acts dequantize to).
-        model = set_8da4w_computation_dtype(model, computation_torch_dtype)
+        from torchao.quantization import int8_dynamic_activation_int4_weight, quantize_
+        from torchao.utils import unwrap_tensor_subclass
+
+        quantize_(model, int8_dynamic_activation_int4_weight(group_size=group_size))
+        model = unwrap_tensor_subclass(model)
+
+        # TODO: deal with checkpoint / computation dtype decoupling.
 
         if verbose:
             print("quantized model:", model)
@@ -698,7 +698,7 @@ def convert_for_runtime(self) -> nn.Module:
     def quantized_model(self) -> nn.Module:
         model_updated_state_dict = self.create_quantized_state_dict(self.packed)
         self.convert_for_runtime()
-        self.mod.load_state_dict(model_updated_state_dict)
+        self.mod.load_state_dict(model_updated_state_dict, assign=True)
         return self.mod