diff --git a/litgpt/pretrain.py b/litgpt/pretrain.py
index eef48bdc0c..4ab31b414e 100644
--- a/litgpt/pretrain.py
+++ b/litgpt/pretrain.py
@@ -199,6 +199,10 @@ def main(
 
     train_time = time.perf_counter()
     fit(fabric, devices, state, train_dataloader, val_dataloader, out_dir, tokenizer_dir, train, eval)
+
+    # Save final checkpoint
+    save_checkpoint(fabric, state, tokenizer_dir, out_dir / "final" / "lit_model.pth")
+
     fabric.print(f"Training time: {(time.perf_counter()-train_time):.2f}s")
     if fabric.device.type == "cuda":
         fabric.print(f"Memory used: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB")
@@ -325,15 +329,7 @@ def fit(
             fabric.barrier()
 
         if train.save_interval is not None and not is_accumulating and state["step_count"] % train.save_interval == 0:
-            checkpoint_file = out_dir / f"step-{state['step_count']:08d}" / "lit_model.pth"
-            checkpoint_file.parent.mkdir(parents=True, exist_ok=True)
-            fabric.print(f"Saving checkpoint to {str(checkpoint_file)!r}")
-            fabric.save(checkpoint_file, state)
-            if fabric.global_rank == 0:
-                save_hyperparameters(setup, checkpoint_file.parent)
-                if tokenizer_dir is not None:
-                    copy_config_files(tokenizer_dir, checkpoint_file.parent)
-                save_config(model.config, checkpoint_file.parent)
+            save_checkpoint(fabric, state, tokenizer_dir, out_dir / f"step-{state['step_count']:08d}" / "lit_model.pth")
 
 
 @torch.no_grad()
@@ -413,6 +409,18 @@ def init_out_dir(out_dir: Path) -> Path:
     return out_dir
 
 
+def save_checkpoint(fabric, state, tokenizer_dir, checkpoint_file):
+    model = state["model"]
+    checkpoint_file.parent.mkdir(parents=True, exist_ok=True)
+    fabric.print(f"Saving checkpoint to {str(checkpoint_file)!r}")
+    fabric.save(checkpoint_file, state)
+    if fabric.global_rank == 0:
+        save_hyperparameters(setup, checkpoint_file.parent)
+        if tokenizer_dir is not None:
+            copy_config_files(tokenizer_dir, checkpoint_file.parent)
+        save_config(model.config, checkpoint_file.parent)
+
+
 def validate_args(train: TrainArgs, eval: EvalArgs, initial_checkpoint_dir, resume) -> None:
     issues = []
     unsupported = [(train, ["max_steps", "epochs"]), (eval, ["max_new_tokens"])]