diff --git a/unsloth_zoo/gradient_checkpointing.py b/unsloth_zoo/gradient_checkpointing.py
index 2d77458a5..e62de5848 100644
--- a/unsloth_zoo/gradient_checkpointing.py
+++ b/unsloth_zoo/gradient_checkpointing.py
@@ -754,17 +754,11 @@ def unsloth_checkpoint(
     Returns:
         Output of running :attr:`function` on :attr:`*args`
     """
-    if use_reentrant is None:
-        warnings.warn(
-            "torch.utils.checkpoint: the use_reentrant parameter should be "
-            "passed explicitly. In version 2.5 we will raise an exception "
-            "if use_reentrant is not passed. use_reentrant=False is "
-            "recommended, but if you need to preserve the current default "
-            "behavior, you can pass use_reentrant=True. Refer to docs for more "
-            "details on the differences between the two variants.",
-            stacklevel=2
-        )
-        use_reentrant = True
+    # Force use_reentrant=True so UnslothCheckpointFunction (smart CPU offloading)
+    # is always used. This is safe because unsloth_checkpoint is only active when
+    # smart GC is patched; when unpatched, the original torch checkpoint is restored.
+    # Fixes transformers 5.2 which defaults use_reentrant=False, bypassing Unsloth.
+    use_reentrant = True
 
     # Hack to mix *args with **kwargs in a python 2.7-compliant way
     preserve = kwargs.pop("preserve_rng_state", True)
@@ -805,6 +799,15 @@ def patch_unsloth_smart_gradient_checkpointing(dtype = None):
     if torch.utils.checkpoint.checkpoint.__name__ != "unsloth_checkpoint":
         torch.utils.checkpoint._old_checkpoint = torch.utils.checkpoint.checkpoint
         torch.utils.checkpoint.checkpoint = unsloth_checkpoint
+
+    # Always patch transformers.modeling_utils.checkpoint so that
+    # gradient_checkpointing_enable() wraps unsloth_checkpoint, not the original.
+    # Without this, transformers 5.2's use_reentrant=False default bypasses
+    # UnslothCheckpointFunction entirely.
+    # Must be outside the conditional above since torch.utils.checkpoint.checkpoint
+    # may already be patched while transformers.modeling_utils.checkpoint is not.
+    import transformers.modeling_utils
+    transformers.modeling_utils.checkpoint = unsloth_checkpoint
 pass
 
 
@@ -831,6 +834,19 @@ def unpatch_unsloth_smart_gradient_checkpointing():
         hasattr(torch.utils.checkpoint, "_old_checkpoint"):
 
         torch.utils.checkpoint.checkpoint = torch.utils.checkpoint._old_checkpoint
+
+    # Restore transformers.modeling_utils.checkpoint independently.
+    # Must be outside the conditional above because unpatch_unsloth_gradient_checkpointing()
+    # may run first (e.g. training_utils.py:201), deleting _old_checkpoint and restoring
+    # torch.utils.checkpoint.checkpoint, which makes the condition above False.
+    # Use _old_checkpoint if still available, otherwise torch.utils.checkpoint.checkpoint
+    # (which has already been restored to the original at that point).
+    import transformers.modeling_utils
+    if getattr(transformers.modeling_utils, "checkpoint", None) is unsloth_checkpoint:
+        transformers.modeling_utils.checkpoint = getattr(
+            torch.utils.checkpoint, "_old_checkpoint",
+            torch.utils.checkpoint.checkpoint
+        )
 pass