diff --git a/deepmd/model/pairwise_dprc.py b/deepmd/model/pairwise_dprc.py
index 80aea92bb1..6983a31cfd 100644
--- a/deepmd/model/pairwise_dprc.py
+++ b/deepmd/model/pairwise_dprc.py
@@ -32,10 +32,6 @@
     TypeEmbedNet,
 )
 
-from .ener import (
-    EnerModel,
-)
-
 
 class PairwiseDPRc(Model):
     """Pairwise Deep Potential - Range Correction."""
@@ -87,13 +83,13 @@ def __init__(
                 padding=True,
             )
 
-        self.qm_model = EnerModel(
+        self.qm_model = Model(
             **qm_model,
             type_map=type_map,
             type_embedding=self.typeebd,
             compress=compress,
         )
-        self.qmmm_model = EnerModel(
+        self.qmmm_model = Model(
             **qmmm_model,
             type_map=type_map,
             type_embedding=self.typeebd,
@@ -187,6 +183,14 @@ def build(
 
         mesh_mixed_type = make_default_mesh(False, True)
 
+        # allow loading a frozen QM model that has only QM types
+        # Note: here we don't map the type between models, so
+        #       the type of the frozen model must be the same as
+        #       the first Ntypes of the current model
+        if self.get_ntypes() > self.qm_model.get_ntypes():
+            natoms_qm = tf.slice(natoms_qm, [0], [self.qm_model.get_ntypes() + 2])
+        assert self.get_ntypes() == self.qmmm_model.get_ntypes()
+
         qm_dict = self.qm_model.build(
             coord_qm,
             atype_qm,
@@ -301,7 +305,7 @@ def get_rcut(self):
         return max(self.qm_model.get_rcut(), self.qmmm_model.get_rcut())
 
     def get_ntypes(self) -> int:
-        return self.qm_model.get_ntypes()
+        return self.ntypes
 
     def data_stat(self, data):
         self.qm_model.data_stat(data)