diff --git a/torchchat/export.py b/torchchat/export.py
index 21e7fcaa8..6b06f1df1 100644
--- a/torchchat/export.py
+++ b/torchchat/export.py
@@ -28,7 +28,7 @@
 
 
 """
-Export for Server 
+Export for Server
 """
 
 
@@ -78,7 +78,7 @@ def export_for_server(
 """
 Export for ExecuTorch
 
-TODO (https://github.com/pytorch/torchchat/issues/1058): Replace 
+TODO (https://github.com/pytorch/torchchat/issues/1058): Replace
 replace_attention_with_custom_sdpa_attention with ET's implementation
 """
 
@@ -94,6 +94,9 @@ def export_for_server(
     from executorch.backends.xnnpack.partition.xnnpack_partitioner import (
         XnnpackDynamicallyQuantizedPartitioner,
     )
+    from executorch.backends.xnnpack.passes.convert_to_linear import (
+        ConvertToLinearPass,
+    )
     from executorch.exir import EdgeProgramManager, to_edge
 
     from executorch.exir.capture._config import (
@@ -274,22 +277,20 @@ def export_for_et(model, device, output_path) -> str:
             _skip_type_promotion=bool(target_precision == torch.float16),
         )
 
-        if target_precision == torch.float16 or target_precision == torch.bfloat16:
-            if state_dict_dtype != torch.float16:
-                print("model.to torch.float16")
-                model = model.to(dtype=torch.float16)
-                state_dict_dtype = torch.float16
-        elif target_precision == torch.float32:
-            if state_dict_dtype != torch.float32:
-                print("model.to torch.float32")
-                model = model.to(dtype=torch.float32)
-        elif target_precision == torch.bfloat16:
-            print("model.to torch.bfloat16")
-            model = model.to(dtype=torch.bfloat16)
-        else:
+        if target_precision not in (torch.float16, torch.float32, torch.bfloat16):
             raise ValueError(f"Unsupported dtype for ET export: {target_precision}")
 
-        replace_attention_with_custom_sdpa_attention(model)
+        if state_dict_dtype != target_precision:
+            print(f"model.to {target_precision}")
+            model = model.to(dtype=target_precision)
+            state_dict_dtype = target_precision
+
+        # Custom SDPA does not work with bfloat16 on CPU currently. (The op doesn't
+        # support anything but bfloat32, and our attempt to use it anyway by converting
+        # to and from float causes other errors.)
+        if target_precision != torch.bfloat16:
+            replace_attention_with_custom_sdpa_attention(model)
+
         with torch.nn.attention.sdpa_kernel(
             [torch.nn.attention.SDPBackend.MATH]
         ), torch.no_grad():
@@ -306,6 +307,7 @@ def export_for_et(model, device, output_path) -> str:
             ExecutorchBackendConfig(
                 extract_delegate_segments=True,
                 passes=[
+                    ConvertToLinearPass(),
                     QuantFusionPass(),
                 ],
                 sym_shape_eval_pass=ConstraintBasedSymShapeEvalPass(),