diff --git a/src/axolotl/core/trainer_builder.py b/src/axolotl/core/trainer_builder.py
index 7330a78ef..0fdd126f5 100644
--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -43,7 +43,7 @@ from axolotl.utils.callbacks import (
     LossWatchDogCallback,
     SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
-    SaveModelOnTrainEndCallback,
+    SaveModelCallback,
     bench_eval_callback_factory,
     causal_lm_bench_eval_callback_factory,
     log_prediction_callback_factory,
@@ -945,7 +945,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.loss_watchdog_threshold is not None:
             callbacks.append(LossWatchDogCallback(self.cfg))
 
-        callbacks.append(SaveModelOnTrainEndCallback())
+        callbacks.append(SaveModelCallback())
 
         return callbacks
 
@@ -1431,7 +1431,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
 
     def get_callbacks(self):
         callbacks = super().get_callbacks()
-        callbacks.append(SaveModelOnTrainEndCallback())
+        callbacks.append(SaveModelCallback())
 
         return callbacks
 
diff --git a/src/axolotl/utils/callbacks/__init__.py b/src/axolotl/utils/callbacks/__init__.py
index 2965ac1e2..c21ef0ad7 100644
--- a/src/axolotl/utils/callbacks/__init__.py
+++ b/src/axolotl/utils/callbacks/__init__.py
@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import logging
+import math
 import os
 from shutil import copyfile
 from tempfile import NamedTemporaryFile
@@ -775,7 +776,7 @@ class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
         return control
 
 
-class SaveModelOnTrainEndCallback(TrainerCallback):
+class SaveModelCallback(TrainerCallback):
     """Callback to save model on train end"""
 
     def on_step_end(  # pylint: disable=unused-argument
@@ -788,6 +789,13 @@ class SaveModelOnTrainEndCallback(TrainerCallback):
         # Save
         if state.global_step >= state.max_steps:
             control.should_save = True
+        elif (
+            args.save_strategy == IntervalStrategy.STEPS
+            and state.save_steps < 1.0
+            and state.global_step % math.ceil(state.save_steps * state.max_steps) == 0
+        ):
+            # workaround to save model on fractional save_steps
+            control.should_save = True
 
     def on_train_end(  # pylint: disable=unused-argument
         self, args, state, control, **kwargs