diff --git a/src/axolotl/core/trainer_builder.py b/src/axolotl/core/trainer_builder.py
index e511b6df86..4e55691857 100755
--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -437,6 +437,7 @@ def __init__(
         self.bench_data_collator = bench_data_collator
         self.eval_data_collator = eval_data_collator
         self.dataset_tags = dataset_tags
+        self.optimizer = None
         super().__init__(*_args, **kwargs)
         self.train_data_collator = self.data_collator
         self._stored_metrics = defaultdict(lambda: defaultdict(list))
@@ -457,7 +458,8 @@ def _wrap_model(self, model, training=True, dataloader=None):
 
     def create_optimizer(self):
         # For all other cases, use parent implementation
-        if (self.args.loraplus_lr_ratio is None
+        if (
+            self.args.loraplus_lr_ratio is None
             and self.args.embedding_lr_scale is None
             and self.args.embedding_lr is None
         ):
@@ -473,14 +475,12 @@ def create_optimizer(self):
         )
 
         if self.args.loraplus_lr_ratio is not None:
-            self.optimizer = (
-                create_loraplus_optimizer(
-                    opt_model,
-                    optimizer_cls,
-                    loraplus_lr_ratio=self.args.loraplus_lr_ratio,
-                    loraplus_lr_embedding=self.args.loraplus_lr_embedding,
-                    **optimizer_kwargs,
-                )
+            self.optimizer = create_loraplus_optimizer(
+                opt_model,
+                optimizer_cls,
+                loraplus_lr_ratio=self.args.loraplus_lr_ratio,
+                loraplus_lr_embedding=self.args.loraplus_lr_embedding,
+                **optimizer_kwargs,
             )
         elif (
             self.args.embedding_lr_scale is not None
@@ -493,18 +493,16 @@ def create_optimizer(self):
                 embedding_lr_scale=self.args.embedding_lr_scale,
                 embedding_lr=self.args.embedding_lr,
                 decay_parameters=decay_parameters,
+                weight_decay=self.args.weight_decay,
                 optimizer_cls=optimizer_cls,
                 optimizer_kwargs=optimizer_kwargs,
             )
 
         if is_sagemaker_mp_enabled():
-            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
-                self.optimizer
-            )
+            self.optimizer = smp.DistributedOptimizer(self.optimizer)
 
         return self.optimizer
 
-
     def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
         if self.args.sample_packing and not self.args.pretraining:
             if self.args.multipack_real_batches:
@@ -984,9 +982,9 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
     tag_names = ["axolotl", "dpo"]
 
     def __init__(self, *args, dataset_tags=None, **kwargs):
-        super().__init__(*args, **kwargs)
         self.dataset_tags = dataset_tags
         self.optimizer = None
+        super().__init__(*args, **kwargs)
 
     def create_optimizer(self):
         # For all other cases, use parent implementation
@@ -999,20 +997,16 @@ def create_optimizer(self):
             self.args,
             opt_model,
         )
-        self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-            create_loraplus_optimizer(
-                opt_model,
-                optimizer_cls,
-                loraplus_lr_ratio=self.args.loraplus_lr_ratio,
-                loraplus_lr_embedding=self.args.loraplus_lr_embedding,
-                **optimizer_kwargs,
-            )
+        self.optimizer = create_loraplus_optimizer(
+            opt_model,
+            optimizer_cls,
+            loraplus_lr_ratio=self.args.loraplus_lr_ratio,
+            loraplus_lr_embedding=self.args.loraplus_lr_embedding,
+            **optimizer_kwargs,
         )
 
         if is_sagemaker_mp_enabled():
-            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
-                self.optimizer
-            )
+            self.optimizer = smp.DistributedOptimizer(self.optimizer)
 
         return self.optimizer
 
@@ -1780,7 +1774,7 @@ def build(self, total_num_steps):
 
                 optimizer_cls = AdamWFp8
                 optimizer_kwargs.update(adam_kwargs)
-            elif self.cfg.optimizer = "adopt_adamw":
+            elif self.cfg.optimizer == "adopt_adamw":
                 from axolotl.utils.optimizers.adopt import ADOPT
 
                 optimizer_cls = ADOPT