diff --git a/src/lightning/pytorch/cli.py b/src/lightning/pytorch/cli.py
index 2bcb1d8f4b1fd..2e2bc939705ef 100644
--- a/src/lightning/pytorch/cli.py
+++ b/src/lightning/pytorch/cli.py
@@ -560,6 +560,36 @@ def parse_arguments(self, parser: LightningArgumentParser, args: ArgsType) -> No
         else:
             self.config = parser.parse_args(args)
 
+    def adapt_checkpoint_hparams(self, checkpoint_hparams: Dict[str, Any]) -> Dict[str, Any]:
+        """Adapt checkpoint hyperparameters before instantiating the model class.
+
+        This method allows for customization of hyperparameters loaded from a checkpoint when
+        using a different model class than the one used for training. For example, when loading
+        a checkpoint from a TrainingModule to use with an InferenceModule that has different
+        ``__init__`` parameters, you can remove or modify incompatible hyperparameters.
+
+        Args:
+            checkpoint_hparams: Dictionary of hyperparameters loaded from the checkpoint.
+
+        Returns:
+            Dictionary of adapted hyperparameters to be used for model instantiation.
+
+        Example::
+
+            class MyCLI(LightningCLI):
+                def adapt_checkpoint_hparams(self, checkpoint_hparams: Dict[str, Any]) -> Dict[str, Any]:
+                    # Remove training-specific hyperparameters not needed for inference
+                    checkpoint_hparams.pop("lr", None)
+                    checkpoint_hparams.pop("weight_decay", None)
+                    return checkpoint_hparams
+
+        Note:
+            If subclass module mode is enabled and ``_class_path`` is present in the checkpoint
+            hyperparameters, you may need to modify it as well to point to your new module class.
+
+        """
+        return checkpoint_hparams
+
     def _parse_ckpt_path(self) -> None:
         """If a checkpoint path is given, parse the hyperparameters from the checkpoint and update the config."""
         if not self.config.get("subcommand"):
@@ -571,6 +601,12 @@ def _parse_ckpt_path(self) -> None:
             hparams.pop("_instantiator", None)
             if not hparams:
                 return
+
+            # Allow customization of checkpoint hyperparameters via adapt_checkpoint_hparams hook
+            hparams = self.adapt_checkpoint_hparams(hparams)
+            if not hparams:
+                return
+
             if "_class_path" in hparams:
                 hparams = {
                     "class_path": hparams.pop("_class_path"),