diff --git a/src/axolotl/core/trainer_builder.py b/src/axolotl/core/trainer_builder.py
index 8c13eb78d..15ad71470 100644
--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -170,24 +170,30 @@ class AxolotlTrainer(Trainer):
             num_training_steps (int): The number of training steps to do.
             optimizer (torch.optim.Optimizer): The training optimizer
         """
+        use_cosine_quadratic = (
+            self.args.lr_scheduler_type == "cosine"
+            and self.args.lr_quadratic_warmup is True
+        )
+
+        use_cosine_min_lr = (
+            self.args.lr_scheduler_type == "cosine"
+            and self.args.cosine_min_lr_ratio is not None
+        )
 
         # fmt: off
         if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
             # fmt: on
-            if (
-                self.args.lr_scheduler_type == "cosine"
-                and self.args.lr_quadratic_warmup is True
-            ):
+            if use_cosine_quadratic:
+                if use_cosine_min_lr:
+                    LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
+
                 self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
                     optimizer,
                     num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                     num_training_steps=num_training_steps,
                 )
-            elif self.args.lr_scheduler_type == "cosine" and self.args.cosine_min_lr_ratio is not None:
+            elif self.args.cosine_min_lr_ratio and use_cosine_min_lr:
                 assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
-                if self.args.deepspeed:
-                    LOG.warning("Using cosine scheduler with deepspeed. This may be ignored if a scheduler is set \
-                                in the deepspeed JSON")
                 self.lr_scheduler = get_cosine_schedule_with_min_lr(  # pylint: disable=attribute-defined-outside-init
                     optimizer,
                     num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
@@ -196,6 +202,13 @@ class AxolotlTrainer(Trainer):
                 )
             else:
                 return super().create_scheduler(num_training_steps, optimizer)
+        else:
+            if use_cosine_quadratic:
+                LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
+
+            if use_cosine_min_lr:
+                LOG.warning("axolotl's cosine scheduler with min lr not used (e.g., because of deepspeed).")
+
         return self.lr_scheduler
 
     def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]: