diff --git a/aimodel/src/subcommands/pretrain_predict.py b/aimodel/src/subcommands/pretrain_predict.py
index 57f6335..3e18d1f 100644
--- a/aimodel/src/subcommands/pretrain_predict.py
+++ b/aimodel/src/subcommands/pretrain_predict.py
@@ -33,6 +33,13 @@ def parse_args():
 	
 	return parser
 
+def handle_open_modeset(filepath, write_mode, handle_mode):
+	if handle_mode == MODE_TFRECORD:
+		options = tf.io.TFRecordOptions(compression_type="GZIP", compression_level=9) if filepath.endswith(".gz") else tf.io.TFRecordOptions()
+		return tf.io.TFRecordWriter(filepath, options=options)
+	else:
+		return handle_open(filepath, write_mode)
+
 def run(args):
 	
 	# Note that we do NOT check to see if the checkpoint file exists, because Tensorflow/Keras requires that we pass the stem instead of the actual index file..... :-/
@@ -81,9 +88,10 @@ def run(args):
 	handle = sys.stdout
 	filepath_metadata = None
 	if filepath_output != "-":
-		handle = handle_open(
+		handle = handle_open_modeset(
 			filepath_output if args.records_per_file <= 0 else filepath_output.replace("+d", str(0)),
-			write_mode
+			write_mode=write_mode,
+			handle_mode=output_mode
 		)
 		filepath_metadata = os.path.join(os.path.dirname(filepath_output), "metadata.json")
 	
@@ -99,7 +107,7 @@ def run(args):
 			i_file = 0
 			handle.close()
 			logger.info(f"PROGRESS:file {files_done}")
-			handle = handle_open(filepath_output.replace("+d", str(files_done+1)), write_mode)
+			handle = handle_open_modeset(filepath_output.replace("+d", str(files_done+1)), write_mode, hand_mode=output_mode)
 		
 		if output_mode == MODE_JSONL:
 			handle.write(json.dumps(step_rainfall.numpy().tolist(), separators=(',', ':'))+"\n") # Ref https://stackoverflow.com/a/64710892/1460422