diff --git a/scripts/finetune.py b/scripts/finetune.py
index 850606356..bb96d9789 100644
--- a/scripts/finetune.py
+++ b/scripts/finetune.py
@@ -209,7 +209,13 @@ def train(
                 cfg, train_dataset, eval_dataset
             )
         barrier()
-        total_num_steps = calculate_total_num_steps(cfg, train_dataset, tokenizer)
+        if cfg.max_steps:
+            total_num_steps = min(
+                calculate_total_num_steps(cfg, train_dataset, tokenizer), cfg.max_steps
+            )
+            LOG.info(f"Maximum number of steps set at {total_num_steps}")
+        else:
+            total_num_steps = calculate_total_num_steps(cfg, train_dataset, tokenizer)
 
     if cfg.debug or "debug" in kwargs:
         LOG.info("check_dataset_labels...")
diff --git a/src/axolotl/utils/trainer.py b/src/axolotl/utils/trainer.py
index b2551028b..54ad058f5 100644
--- a/src/axolotl/utils/trainer.py
+++ b/src/axolotl/utils/trainer.py
@@ -461,7 +461,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         evaluation_strategy = "steps"
 
     training_args = AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
-        # max_steps=total_num_steps,  # this is helpful in case we don't actually know total # of steps
+        max_steps=total_num_steps if cfg.max_steps else -1,
         max_seq_length=cfg.sequence_len,
         per_device_train_batch_size=cfg.micro_batch_size,
         per_device_eval_batch_size=cfg.eval_batch_size