diff --git a/src/axolotl/core/trainer_builder.py b/src/axolotl/core/trainer_builder.py
index e47c09d51..aab9a80b8 100755
--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -48,6 +48,7 @@ from trl import (
 )
 from trl.trainer.utils import RewardDataCollatorWithPadding, pad_to_length
 
+from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils import is_comet_available, is_mlflow_available
@@ -1147,6 +1148,12 @@ class TrainerBuilderBase(abc.ABC):
 
     def get_callbacks(self) -> List[TrainerCallback]:
         callbacks = []
+
+        plugin_manager = PluginManager.get_instance()
+        callbacks.extend(
+            plugin_manager.add_callbacks_pre_trainer(cfg=self.cfg, model=self.model)
+        )
+
         if self.cfg.use_wandb:
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
@@ -1173,11 +1180,17 @@ class TrainerBuilderBase(abc.ABC):
 
         return callbacks
 
-    @abstractmethod
     def get_post_trainer_create_callbacks(self, trainer):
         """
         Callbacks added after the trainer is created, usually b/c these need access to the trainer
         """
+        callbacks = []
+
+        plugin_manager = PluginManager.get_instance()
+        callbacks.extend(
+            plugin_manager.add_callbacks_post_trainer(cfg=self.cfg, trainer=trainer)
+        )
+        return callbacks
 
     def hook_pre_create_training_args(self, training_arguments_kwargs):
         # TODO
@@ -1223,7 +1236,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         if self.cfg.use_wandb and self.cfg.eval_table_size > 0:
             LogPredictionCallback = log_prediction_callback_factory(
                 trainer, self.tokenizer, "wandb"
@@ -1791,7 +1804,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         return callbacks
 
     def build_training_arguments(self, total_num_steps):
@@ -2000,11 +2013,11 @@ class HFPPOTrainerBuilder(TrainerBuilderBase):
     """
 
     def get_callbacks(self):
-        callbacks = []
+        callbacks = super().get_callbacks()
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
-        callbacks = []
+        callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         return callbacks
 
     def build(self, total_num_steps):
diff --git a/src/axolotl/integrations/base.py b/src/axolotl/integrations/base.py
index e2bd79bc4..43afa431a 100644
--- a/src/axolotl/integrations/base.py
+++ b/src/axolotl/integrations/base.py
@@ -18,9 +18,10 @@ Plugins can be used to integrate third-party models, modify the training process
 
 To create a new plugin, you need to inherit from the BasePlugin class and implement the required methods.
 """
+import collections
 import importlib
 import logging
-from typing import List
+from typing import OrderedDict
 
 
 class BasePlugin:
@@ -47,7 +48,7 @@ class BasePlugin:
         Initializes the BasePlugin.
         """
 
-    def register(self, cfg):
+    def register(self, cfg):  # pylint: disable=unused-argument
         """
         Registers the plugin with the given configuration.
 
@@ -63,7 +64,7 @@ class BasePlugin:
         Returns a pydantic model for the plugin's input arguments.
         """
 
-    def pre_model_load(self, cfg):
+    def pre_model_load(self, cfg):  # pylint: disable=unused-argument
         """
         Performs actions before the model is loaded.
 
@@ -74,7 +75,7 @@ class BasePlugin:
         None
         """
 
-    def post_model_load(self, cfg, model):
+    def post_model_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after the model is loaded.
 
@@ -86,7 +87,7 @@ class BasePlugin:
         None
         """
 
-    def pre_lora_load(self, cfg, model):
+    def pre_lora_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions before LoRA weights are loaded.
 
@@ -98,7 +99,7 @@ class BasePlugin:
         None
         """
 
-    def post_lora_load(self, cfg, model):
+    def post_lora_load(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after LoRA weights are loaded.
 
@@ -110,7 +111,7 @@ class BasePlugin:
         None
         """
 
-    def create_optimizer(self, cfg, trainer):
+    def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
         """
         Creates and returns an optimizer for training.
 
@@ -122,7 +123,9 @@ class BasePlugin:
         object: The created optimizer.
         """
 
-    def create_lr_scheduler(self, cfg, trainer, optimizer):
+    def create_lr_scheduler(
+        self, cfg, trainer, optimizer
+    ):  # pylint: disable=unused-argument
         """
         Creates and returns a learning rate scheduler.
 
@@ -135,7 +138,7 @@ class BasePlugin:
         object: The created learning rate scheduler.
         """
 
-    def add_callbacks_pre_trainer(self, cfg, model):
+    def add_callbacks_pre_trainer(self, cfg, model):  # pylint: disable=unused-argument
         """
         Adds callbacks to the trainer before training.
 
@@ -146,8 +149,11 @@ class BasePlugin:
         Returns:
         List[callable]: A list of callback functions to be added to the TrainingArgs
         """
+        return []
 
-    def add_callbacks_post_trainer(self, cfg, trainer):
+    def add_callbacks_post_trainer(
+        self, cfg, trainer
+    ):  # pylint: disable=unused-argument
         """
         Adds callbacks to the trainer after training.
 
@@ -158,8 +164,9 @@ class BasePlugin:
         Returns:
         List[callable]: A list of callback functions to be added to the TrainingArgs
         """
+        return []
 
-    def post_train(self, cfg, model):
+    def post_train(self, cfg, model):  # pylint: disable=unused-argument
         """
         Performs actions after training is complete.
 
@@ -171,7 +178,7 @@ class BasePlugin:
         None
         """
 
-    def post_train_unload(self, cfg):
+    def post_train_unload(self, cfg):  # pylint: disable=unused-argument
         """
         Performs actions after training is complete and the model is unloaded.
 
@@ -227,7 +234,7 @@ class PluginManager:
     pre_model_load(cfg): Calls the pre_model_load method of all registered plugins.
     """
 
-    plugins: List[BasePlugin] = []
+    plugins: OrderedDict[str, BasePlugin] = collections.OrderedDict()
 
     _instance = None
 
@@ -237,7 +244,7 @@ class PluginManager:
         """
         if cls._instance is None:
             cls._instance = super(PluginManager, cls).__new__(cls)
-            cls._instance.plugins: List[BasePlugin] = []
+            cls._instance.plugins = collections.OrderedDict()
         return cls._instance
 
     @staticmethod
@@ -265,7 +272,7 @@ class PluginManager:
         """
         try:
             plugin = load_plugin(plugin_name)
-            self.plugins.append(plugin)
+            self.plugins[plugin_name] = plugin
         except ImportError:
             logging.error(f"Failed to load plugin: {plugin_name}")
 
@@ -277,7 +284,7 @@ class PluginManager:
         list[str]: A list of Pydantic classes for all registered plugins' input arguments.'
         """
         input_args = []
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             input_args_from_plugin = plugin.get_input_args()
             if input_args_from_plugin is not None:
                 input_args.append(input_args_from_plugin)
@@ -293,7 +300,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.pre_model_load(cfg)
 
     def post_model_load(self, cfg, model):
@@ -307,7 +314,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.post_model_load(cfg, model)
 
     def pre_lora_load(self, cfg, model):
@@ -321,7 +328,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.pre_lora_load(cfg, model)
 
     def post_lora_load(self, cfg, model):
@@ -335,7 +342,7 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.post_lora_load(cfg, model)
 
     def create_optimizer(self, cfg, trainer):
@@ -349,7 +356,7 @@ class PluginManager:
         Returns:
         object: The created optimizer, or None if none was found.
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             optimizer = plugin.create_optimizer(cfg, trainer)
             if optimizer is not None:
                 return optimizer
@@ -367,7 +374,7 @@ class PluginManager:
         Returns:
         object: The created learning rate scheduler, or None if none was found.
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             scheduler = plugin.create_lr_scheduler(cfg, trainer, optimizer)
             if scheduler is not None:
                 return scheduler
@@ -385,7 +392,7 @@ class PluginManager:
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             callbacks.extend(plugin.add_callbacks_pre_trainer(cfg, model))
         return callbacks
 
@@ -401,7 +408,7 @@ class PluginManager:
         List[callable]: A list of callback functions to be added to the TrainingArgs.
         """
         callbacks = []
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             callbacks.extend(plugin.add_callbacks_post_trainer(cfg, trainer))
         return callbacks
 
@@ -416,5 +423,5 @@ class PluginManager:
         Returns:
         None
         """
-        for plugin in self.plugins:
+        for plugin in self.plugins.values():
             plugin.post_train_unload(cfg)