diff --git a/scripts/base_train.py b/scripts/base_train.py
index c7c5bba..55b9ca6 100644
--- a/scripts/base_train.py
+++ b/scripts/base_train.py
@@ -12,6 +12,7 @@ python -m scripts.base_train --depth=4 --max_seq_len=512 --device_batch_size=1 -
 """
 
 import os
+import signal
 os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"
 import argparse
 import time
@@ -68,6 +69,7 @@ parser.add_argument("--core_metric_every", type=int, default=2000, help="evaluat
 parser.add_argument("--core_metric_max_per_task", type=int, default=500, help="examples per task for CORE metric")
 parser.add_argument("--sample_every", type=int, default=2000, help="sample from model every N steps (-1 = disable)")
 parser.add_argument("--save_every", type=int, default=-1, help="save checkpoints every N steps (-1 = only at end)")
+parser.add_argument("--save_on_sigint", action="store_true", help="save a checkpoint when exiting prematurely")
 # Output
 parser.add_argument("--model_tag", type=str, default=None, help="override model tag for checkpoint directory name")
 args = parser.parse_args()
@@ -258,6 +260,38 @@ else:
 
 # -----------------------------------------------------------------------------
 # Training loop
+
+
+def save_on_interrupt(sig,frame):
+    save_checkpoint(
+            checkpoint_dir,
+            step,
+            orig_model.state_dict(), # model parameters
+            [opt.state_dict() for opt in optimizers], # optimizer states
+            { # metadata saved as json
+                "step": step,
+                "val_bpb": val_bpb, # loss at last step
+                "model_config": model_config_kwargs,
+                "user_config": user_config, # inputs to the training script
+                "device_batch_size": args.device_batch_size,
+                "max_seq_len": args.max_seq_len,
+                "dataloader_state_dict": dataloader_state_dict,
+                "loop_state": { # all loop state (other than step) so that we can resume training
+                    "min_val_bpb": min_val_bpb,
+                    "smooth_train_loss": smooth_train_loss,
+                    "total_training_time": total_training_time,
+                },
+            },
+            rank=ddp_rank,
+        )
+    compute_cleanup()
+    print("So long and thanks for all the fish!")
+    exit()
+
+if args.save_on_sigint:
+    signal.signal(signal.SIGINT, save_on_interrupt)
+
+
 while True:
     last_step = step == num_iterations # loop runs num_iterations+1 times so that we can eval/save at the end
     flops_so_far = num_flops_per_token * args.total_batch_size * step
@@ -412,6 +446,7 @@ while True:
     # state update
     step += 1
 
+
 # print a few more stats
 print0(f"Peak memory usage: {get_max_memory() / 1024 / 1024:.2f}MiB")
 print0(f"Total training time: {total_training_time/60:.2f}m")