diff --git a/optimum/habana/transformers/trainer.py b/optimum/habana/transformers/trainer.py
index b53f5c12b6..595c566f5d 100644
--- a/optimum/habana/transformers/trainer.py
+++ b/optimum/habana/transformers/trainer.py
@@ -1358,6 +1358,65 @@ def _maybe_log_save_evaluate(
             timer.step()
             self.log_evaluate_save_time += timer.last_duration
 
+    def _save_checkpoint(self, model, trial):
+        # Copied from https://github.com/huggingface/transformers/blob/v4.51-release/src/transformers/trainer.py#L3187
+        # In all cases, including ddp/dp/deepspeed, self.model is always a reference to the model we
+        # want to save except FullyShardedDDP.
+        # assert unwrap_model(model) is self.model, "internal model should be a reference to self.model
+
+        # Save model checkpoint
+        checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
+
+        if self.hp_search_backend is None and trial is None:
+            self.store_flos()
+
+        run_dir = self._get_output_dir(trial=trial)
+        output_dir = os.path.join(run_dir, checkpoint_folder)
+        self.save_model(output_dir, _internal_call=True)
+
+        # NOTE(pbielak): In a multi-card scenario, the model saving is done by the main process (rank zero),
+        # whereas all other ranks continue processing. When checking for the `best_checkpoint_dir` below,
+        # a race condition occurs. This barrier forces other processes to wait till the model is saved.
+        if self.args.parallel_mode == ParallelMode.DISTRIBUTED:
+            torch.distributed.barrier()
+
+        if self.args.save_strategy in [SaveStrategy.STEPS, SaveStrategy.EPOCH] and self.state.best_global_step:
+            best_checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.best_global_step}"
+            best_checkpoint_dir = os.path.join(run_dir, best_checkpoint_folder)
+
+            if os.path.exists(best_checkpoint_dir):
+                self.state.best_model_checkpoint = best_checkpoint_dir
+
+        if not self.args.save_only_model:
+            # Save optimizer and scheduler
+            self._save_optimizer_and_scheduler(output_dir)
+            self._save_scaler(output_dir)
+            # Save RNG state
+            self._save_rng_state(output_dir)
+
+        # Save the Trainer state
+        if self.args.should_save:
+            # Update `ExportableState` callbacks and `TrainerControl` state to where we are currently
+            for cb in [
+                cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState)
+            ]:
+                cb_name = cb.__class__.__name__
+                cb_state = cb.state()
+                if isinstance(self.state.stateful_callbacks[cb_name], list):
+                    self.state.stateful_callbacks[cb_name].append(cb_state)
+                else:
+                    self.state.stateful_callbacks[cb_name] = cb_state
+            self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))
+
+        if self.args.push_to_hub:
+            self._push_from_checkpoint(output_dir)
+
+        # Maybe delete some older checkpoints.
+        if self.args.should_save:
+            # Solely rely on numerical checkpoint id for rotation.
+            # mtime is not reliable especially on some fuse fs in cloud environments.
+            self._rotate_checkpoints(use_mtime=False, output_dir=run_dir)
+
     def _load_rng_state(self, checkpoint):
         # Load RNG states from `checkpoint`
         if checkpoint is None: