diff --git a/deepmd/tf/model/model.py b/deepmd/tf/model/model.py
index 3377ed2d51..5e26569524 100644
--- a/deepmd/tf/model/model.py
+++ b/deepmd/tf/model/model.py
@@ -826,19 +826,32 @@ def deserialize(cls, data: dict, suffix: str = "") -> "Descriptor":
             # deepcopy is not used for performance reasons
             data["fitting"] = data["fitting"].copy()
             data["fitting"]["@variables"] = data["fitting"]["@variables"].copy()
-            if (
-                int(np.any(data["fitting"]["@variables"]["bias_atom_e"]))
-                + int(np.any(data["@variables"]["out_bias"]))
-                > 1
-            ):
-                raise ValueError(
-                    "fitting/@variables/bias_atom_e and @variables/out_bias should not be both non-zero"
+
+            # For InvarFitting types (ener, dos, property), out_bias can be reshaped and added to bias_atom_e
+            # For GeneralFitting types (dipole, polar), out_bias and bias_atom_e have different purposes and shapes
+            # and should not be added together
+            fitting_type = data["fitting"].get("type", "ener")
+            if fitting_type in [
+                "ener",
+                "dos",
+                "property",
+            ]:
+                # For InvarFitting types, use the original logic to reshape and add out_bias to bias_atom_e
+                if (
+                    int(np.any(data["fitting"]["@variables"]["bias_atom_e"]))
+                    + int(np.any(data["@variables"]["out_bias"]))
+                    > 1
+                ):
+                    raise ValueError(
+                        "fitting/@variables/bias_atom_e and @variables/out_bias should not be both non-zero"
+                    )
+                data["fitting"]["@variables"]["bias_atom_e"] = data["fitting"][
+                    "@variables"
+                ]["bias_atom_e"] + data["@variables"]["out_bias"].reshape(
+                    data["fitting"]["@variables"]["bias_atom_e"].shape
                 )
-            data["fitting"]["@variables"]["bias_atom_e"] = data["fitting"][
-                "@variables"
-            ]["bias_atom_e"] + data["@variables"]["out_bias"].reshape(
-                data["fitting"]["@variables"]["bias_atom_e"].shape
-            )
+            # For GeneralFitting types (dipole, polar), keep out_bias separate - don't add to bias_atom_e
+            # These fitting types have different bias structures that are incompatible
         fitting = Fitting.deserialize(data.pop("fitting"), suffix=suffix)
         # pass descriptor type embedding to model
         if descriptor.explicit_ntypes:
@@ -887,7 +900,10 @@ def serialize(self, suffix: str = "") -> dict:
 
         ntypes = len(self.get_type_map())
         dict_fit = self.fitting.serialize(suffix=suffix)
-        if dict_fit.get("@variables", {}).get("bias_atom_e") is not None:
+        if (
+            dict_fit.get("@variables", {}).get("bias_atom_e") is not None
+            and dict_fit["dim_out"] == dict_fit["embedding_width"]
+        ):
             out_bias = dict_fit["@variables"]["bias_atom_e"].reshape(
                 [1, ntypes, dict_fit["dim_out"]]
             )