diff --git a/optimum/habana/trainer.py b/optimum/habana/trainer.py
index 4e3c99fa07..813216dbee 100644
--- a/optimum/habana/trainer.py
+++ b/optimum/habana/trainer.py
@@ -1464,3 +1464,42 @@ def _push_from_checkpoint(self, checkpoint_folder):
             if self.args.hub_strategy == HubStrategy.CHECKPOINT:
                 # Move back the checkpoint to its place
                 shutil.move(tmp_checkpoint, checkpoint_folder)
+
+    def _load_best_model(self):
+        logger.info(f"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric}).")
+        best_model_path = os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME)
+        model = self.model
+        if os.path.exists(best_model_path):
+            # TODO: uncomment the code below when Habana DeepSpeed >= 0.6.5
+            # if self.deepspeed:
+
+            #     if self.model_wrapped is not None:
+            #         # this removes the pre-hooks from the previous engine
+            #         self.model_wrapped.destroy()
+            #         self.model_wrapped = None
+
+            #     # temp hack until Deepspeed fixes the problem with resume from an existing engine that did some stepping
+            #     deepspeed_engine, optimizer, lr_scheduler = deepspeed_init(
+            #         self,
+            #         num_training_steps=self.args.max_steps,
+            #         resume_from_checkpoint=self.state.best_model_checkpoint,
+            #     )
+            #     self.model = deepspeed_engine.module
+            #     self.model_wrapped = deepspeed_engine
+            #     self.deepspeed = deepspeed_engine
+            #     self.optimizer = optimizer
+            #     self.lr_scheduler = lr_scheduler
+            # else:
+            # We load the model state dict on the CPU to avoid an OOM error.
+            state_dict = torch.load(best_model_path, map_location="cpu")
+            # If the model is on the GPU, it still works!
+            load_result = model.load_state_dict(state_dict, strict=False)
+            self._issue_warnings_after_load(load_result)
+        elif os.path.exists(os.path.join(self.state.best_model_checkpoint, WEIGHTS_INDEX_NAME)):
+            load_result = load_sharded_checkpoint(model, self.state.best_model_checkpoint, strict=False)
+            self._issue_warnings_after_load(load_result)
+        else:
+            logger.warning(
+                f"Could not locate the best model at {best_model_path}, if you are running a distributed training "
+                "on multiple nodes, you should activate `--save_on_each_node`."
+            )