diff --git a/pymc/model/core.py b/pymc/model/core.py
index b85cc802f..d04011407 100644
--- a/pymc/model/core.py
+++ b/pymc/model/core.py
@@ -32,7 +32,7 @@
 import pytensor.tensor as pt
 import scipy.sparse as sps
 
-from pytensor.compile import DeepCopyOp, Function, get_mode
+from pytensor.compile import DeepCopyOp, Function, ProfileStats, get_mode
 from pytensor.compile.sharedvalue import SharedVariable
 from pytensor.graph.basic import Constant, Variable, ancestors, graph_inputs
 from pytensor.tensor.random.op import RandomVariable
@@ -1657,7 +1657,15 @@ def compile_fn(
             return PointFunc(fn)
         return fn
 
-    def profile(self, outs, *, n=1000, point=None, profile=True, **kwargs):
+    def profile(
+        self,
+        outs,
+        *,
+        n=1000,
+        point=None,
+        profile=True,
+        **compile_fn_kwargs,
+    ) -> ProfileStats:
         """Compile and profile a PyTensor function which returns ``outs`` and takes values of model vars as a dict as an argument.
 
         Parameters
@@ -1668,16 +1676,22 @@ def profile(self, outs, *, n=1000, point=None, profile=True, **kwargs):
         point : Point
             Point to pass to the function
         profile : True or ProfileStats
-        args, kwargs
-            Compilation args
+        compile_fn_kwargs
+            Compilation kwargs for :func:`pymc.model.core.Model.compile_fn`
 
         Returns
         -------
-        ProfileStats
+        pytensor.compile.profiling.ProfileStats
             Use .summary() to print stats.
         """
-        kwargs.setdefault("on_unused_input", "ignore")
-        f = self.compile_fn(outs, inputs=self.value_vars, point_fn=False, profile=profile, **kwargs)
+        compile_fn_kwargs.setdefault("on_unused_input", "ignore")
+        f = self.compile_fn(
+            outs,
+            inputs=self.value_vars,
+            point_fn=False,
+            profile=profile,
+            **compile_fn_kwargs,
+        )
         if point is None:
             point = self.initial_point()