integrations.base.BasePlugin.create_lr_scheduler(cfg, trainer, optimizer)integrations.base.BasePlugin.create_lr_scheduler(
+ cfg,
+ trainer,
+ optimizer,
+ num_training_steps,
+)Creates and returns a learning rate scheduler.
Parameters: cfg (dict): The configuration for the plugin. trainer (object): The trainer object for training. -optimizer (object): The optimizer for training.
+optimizer (object): The optimizer for training. +num_training_steps (int): Total number of training stepsReturns: -object: The created learning rate scheduler.
+object (LRScheduler): The created learning rate scheduler.integrations.base.PluginManager.create_lr_scheduler(trainer, optimizer)integrations.base.PluginManager.create_lr_scheduler(
+ trainer,
+ optimizer,
+ num_training_steps,
+)Calls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.
Parameters:
trainer (object): The trainer object for training.
diff --git a/search.json b/search.json
index a51f0521a..7602408e7 100644
--- a/search.json
+++ b/search.json
@@ -2528,14 +2528,14 @@
"href": "docs/api/integrations.base.html",
"title": "integrations.base",
"section": "",
- "text": "integrations.base\nBase class for all plugins.\nA plugin is a reusable, modular, and self-contained piece of code that extends the functionality of Axolotl.\nPlugins can be used to integrate third-party models, modify the training process, or add new features.\nTo create a new plugin, you need to inherit from the BasePlugin class and implement the required methods.\n\n\n\n\n\nName\nDescription\n\n\n\n\nBaseOptimizerFactory\nBase class for factories to create custom optimizers\n\n\nBasePlugin\nBase class for all plugins. Defines the interface for plugin methods.\n\n\nPluginManager\nThe PluginManager class is responsible for loading and managing plugins.\n\n\n\n\n\nintegrations.base.BaseOptimizerFactory()\nBase class for factories to create custom optimizers\n\n\n\nintegrations.base.BasePlugin(self)\nBase class for all plugins. Defines the interface for plugin methods.\nAttributes:\nNone\nMethods:\nregister(cfg): Registers the plugin with the given configuration.\npre_model_load(cfg): Performs actions before the model is loaded.\npost_model_build(cfg, model): Performs actions after the model is loaded, but before LoRA adapters are applied.\npre_lora_load(cfg, model): Performs actions before LoRA weights are loaded.\npost_lora_load(cfg, model): Performs actions after LoRA weights are loaded.\npost_model_load(cfg, model): Performs actions after the model is loaded, inclusive of any adapters.\ncreate_optimizer(cfg, trainer): Creates and returns an optimizer for training.\ncreate_lr_scheduler(cfg, trainer, optimizer): Creates and returns a learning rate scheduler.\nadd_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training.\nadd_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training.\n\n\n\n\n\nName\nDescription\n\n\n\n\nadd_callbacks_post_trainer\nAdds callbacks to the trainer after creating the trainer.\n\n\nadd_callbacks_pre_trainer\nsetup callbacks before creating the trainer.\n\n\ncreate_lr_scheduler\nCreates and returns a learning rate scheduler.\n\n\ncreate_optimizer\nCreates and returns an optimizer for training.\n\n\nget_input_args\nReturns a pydantic model for the plugin’s input arguments.\n\n\nget_trainer_cls\nReturns a custom class for the trainer.\n\n\npost_lora_load\nPerforms actions after LoRA weights are loaded.\n\n\npost_model_build\nPerforms actions after the model is built/loaded, but before any adapters are applied.\n\n\npost_model_load\nPerforms actions after the model is loaded.\n\n\npost_train\nPerforms actions after training is complete.\n\n\npost_train_unload\nPerforms actions after training is complete and the model is unloaded.\n\n\npre_lora_load\nPerforms actions before LoRA weights are loaded.\n\n\npre_model_load\nPerforms actions before the model is loaded.\n\n\nregister\nRegisters the plugin with the given configuration.\n\n\n\n\n\nintegrations.base.BasePlugin.add_callbacks_post_trainer(cfg, trainer)\nAdds callbacks to the trainer after creating the trainer.\nThis is useful for callbacks that require access to the model or trainer.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\nReturns:\nList[callable]: A list of callback functions to be added\n\n\n\nintegrations.base.BasePlugin.add_callbacks_pre_trainer(cfg, model)\nsetup callbacks before creating the trainer.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs\n\n\n\nintegrations.base.BasePlugin.create_lr_scheduler(cfg, trainer, optimizer)\nCreates and returns a learning rate scheduler.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\noptimizer (object): The optimizer for training.\nReturns:\nobject: The created learning rate scheduler.\n\n\n\nintegrations.base.BasePlugin.create_optimizer(cfg, trainer)\nCreates and returns an optimizer for training.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\nReturns:\nobject: The created optimizer.\n\n\n\nintegrations.base.BasePlugin.get_input_args()\nReturns a pydantic model for the plugin’s input arguments.\n\n\n\nintegrations.base.BasePlugin.get_trainer_cls(cfg)\nReturns a custom class for the trainer.\nParameters:\ncfg (dict): The global axolotl configuration.\nReturns:\nclass: The class for the trainer.\n\n\n\nintegrations.base.BasePlugin.post_lora_load(cfg, model)\nPerforms actions after LoRA weights are loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_model_build(cfg, model)\nPerforms actions after the model is built/loaded, but before any adapters are applied.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ncfg\ndict\nThe configuration for the plugin.\nrequired\n\n\n\n\n\n\n\nintegrations.base.BasePlugin.post_model_load(cfg, model)\nPerforms actions after the model is loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_train(cfg, model)\nPerforms actions after training is complete.\nParameters:\ncfg (dict): The axolotl configuration\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_train_unload(cfg)\nPerforms actions after training is complete and the model is unloaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.pre_lora_load(cfg, model)\nPerforms actions before LoRA weights are loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.pre_model_load(cfg)\nPerforms actions before the model is loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.register(cfg)\nRegisters the plugin with the given configuration.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\n\n\nintegrations.base.PluginManager()\nThe PluginManager class is responsible for loading and managing plugins.\nIt should be a singleton so it can be accessed from anywhere in the codebase.\nAttributes:\nplugins (ListBasePlugin): A list of loaded plugins.\nMethods:\nget_instance(): Static method to get the singleton instance of PluginManager.\nregister(plugin_name: str): Registers a new plugin by its name.\npre_model_load(cfg): Calls the pre_model_load method of all registered plugins.\n\n\n\n\n\nName\nDescription\n\n\n\n\nadd_callbacks_post_trainer\nCalls the add_callbacks_post_trainer method of all registered plugins.\n\n\nadd_callbacks_pre_trainer\nCalls the add_callbacks_pre_trainer method of all registered plugins.\n\n\ncreate_lr_scheduler\nCalls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.\n\n\ncreate_optimizer\nCalls the create_optimizer method of all registered plugins and returns the first non-None optimizer.\n\n\nget_input_args\nReturns a list of Pydantic classes for all registered plugins’ input arguments.’\n\n\nget_instance\nReturns the singleton instance of PluginManager.\n\n\nget_trainer_cls\nCalls the get_trainer_cls method of all registered plugins and returns the first non-None trainer class.\n\n\npost_lora_load\nCalls the post_lora_load method of all registered plugins.\n\n\npost_model_build\nCalls the post_model_build method of all registered plugins after the model has been built/loaded,\n\n\npost_model_load\nCalls the post_model_load method of all registered plugins after the model has been loaded\n\n\npost_train\nCalls the post_train method of all registered plugins.\n\n\npost_train_unload\nCalls the post_train_unload method of all registered plugins.\n\n\npre_lora_load\nCalls the pre_lora_load method of all registered plugins.\n\n\npre_model_load\nCalls the pre_model_load method of all registered plugins.\n\n\nregister\nRegisters a new plugin by its name.\n\n\n\n\n\nintegrations.base.PluginManager.add_callbacks_post_trainer(cfg, trainer)\nCalls the add_callbacks_post_trainer method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\ntrainer (object): The trainer object for training.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs.\n\n\n\nintegrations.base.PluginManager.add_callbacks_pre_trainer(cfg, model)\nCalls the add_callbacks_pre_trainer method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs.\n\n\n\nintegrations.base.PluginManager.create_lr_scheduler(trainer, optimizer)\nCalls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.\nParameters:\ntrainer (object): The trainer object for training.\noptimizer (object): The optimizer for training.\nReturns:\nobject: The created learning rate scheduler, or None if none was found.\n\n\n\nintegrations.base.PluginManager.create_optimizer(trainer)\nCalls the create_optimizer method of all registered plugins and returns the first non-None optimizer.\nParameters:\ntrainer (object): The trainer object for training.\nReturns:\nobject: The created optimizer, or None if none was found.\n\n\n\nintegrations.base.PluginManager.get_input_args()\nReturns a list of Pydantic classes for all registered plugins’ input arguments.’\nReturns:\nlist[str]: A list of Pydantic classes for all registered plugins’ input arguments.’\n\n\n\nintegrations.base.PluginManager.get_instance()\nReturns the singleton instance of PluginManager.\nIf the instance doesn’t exist, it creates a new one.\n\n\n\nintegrations.base.PluginManager.get_trainer_cls(cfg)\nCalls the get_trainer_cls method of all registered plugins and returns the first non-None trainer class.\nParameters:\ncfg (dict): The configuration for the plugins.\nReturns:\nobject: The trainer class, or None if none was found.\n\n\n\nintegrations.base.PluginManager.post_lora_load(cfg, model)\nCalls the post_lora_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_model_build(cfg, model)\nCalls the post_model_build method of all registered plugins after the model has been built/loaded,\nbut before any adapters have been applied.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ncfg\ndict\nThe configuration for the plugins.\nrequired\n\n\nmodel\nobject\nThe loaded model.\nrequired\n\n\n\n\n\n\n\nintegrations.base.PluginManager.post_model_load(cfg, model)\nCalls the post_model_load method of all registered plugins after the model has been loaded\ninclusive of any adapters\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_train(cfg, model)\nCalls the post_train method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_train_unload(cfg)\nCalls the post_train_unload method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.pre_lora_load(cfg, model)\nCalls the pre_lora_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.pre_model_load(cfg)\nCalls the pre_model_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.register(plugin_name)\nRegisters a new plugin by its name.\nParameters:\nplugin_name (str): The name of the plugin to be registered.\nReturns:\nNone\nRaises:\nImportError: If the plugin module cannot be imported.\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nload_plugin\nLoads a plugin based on the given plugin name.\n\n\n\n\n\nintegrations.base.load_plugin(plugin_name)\nLoads a plugin based on the given plugin name.\nThe plugin name should be in the format “module_name.class_name”.\nThis function splits the plugin name into module and class, imports the module,\nretrieves the class from the module, and creates an instance of the class.\nParameters:\nplugin_name (str): The name of the plugin to be loaded. The name should be in the format “module_name.class_name”.\nReturns:\nBasePlugin: An instance of the loaded plugin.\nRaises:\nImportError: If the plugin module cannot be imported."
+ "text": "integrations.base\nBase class for all plugins.\nA plugin is a reusable, modular, and self-contained piece of code that extends the functionality of Axolotl.\nPlugins can be used to integrate third-party models, modify the training process, or add new features.\nTo create a new plugin, you need to inherit from the BasePlugin class and implement the required methods.\n\n\n\n\n\nName\nDescription\n\n\n\n\nBaseOptimizerFactory\nBase class for factories to create custom optimizers\n\n\nBasePlugin\nBase class for all plugins. Defines the interface for plugin methods.\n\n\nPluginManager\nThe PluginManager class is responsible for loading and managing plugins.\n\n\n\n\n\nintegrations.base.BaseOptimizerFactory()\nBase class for factories to create custom optimizers\n\n\n\nintegrations.base.BasePlugin(self)\nBase class for all plugins. Defines the interface for plugin methods.\nAttributes:\nNone\nMethods:\nregister(cfg): Registers the plugin with the given configuration.\npre_model_load(cfg): Performs actions before the model is loaded.\npost_model_build(cfg, model): Performs actions after the model is loaded, but before LoRA adapters are applied.\npre_lora_load(cfg, model): Performs actions before LoRA weights are loaded.\npost_lora_load(cfg, model): Performs actions after LoRA weights are loaded.\npost_model_load(cfg, model): Performs actions after the model is loaded, inclusive of any adapters.\ncreate_optimizer(cfg, trainer): Creates and returns an optimizer for training.\ncreate_lr_scheduler(cfg, trainer, optimizer, num_training_steps): Creates and returns a learning rate scheduler.\nadd_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training.\nadd_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training.\n\n\n\n\n\nName\nDescription\n\n\n\n\nadd_callbacks_post_trainer\nAdds callbacks to the trainer after creating the trainer.\n\n\nadd_callbacks_pre_trainer\nsetup callbacks before creating the trainer.\n\n\ncreate_lr_scheduler\nCreates and returns a learning rate scheduler.\n\n\ncreate_optimizer\nCreates and returns an optimizer for training.\n\n\nget_input_args\nReturns a pydantic model for the plugin’s input arguments.\n\n\nget_trainer_cls\nReturns a custom class for the trainer.\n\n\npost_lora_load\nPerforms actions after LoRA weights are loaded.\n\n\npost_model_build\nPerforms actions after the model is built/loaded, but before any adapters are applied.\n\n\npost_model_load\nPerforms actions after the model is loaded.\n\n\npost_train\nPerforms actions after training is complete.\n\n\npost_train_unload\nPerforms actions after training is complete and the model is unloaded.\n\n\npre_lora_load\nPerforms actions before LoRA weights are loaded.\n\n\npre_model_load\nPerforms actions before the model is loaded.\n\n\nregister\nRegisters the plugin with the given configuration.\n\n\n\n\n\nintegrations.base.BasePlugin.add_callbacks_post_trainer(cfg, trainer)\nAdds callbacks to the trainer after creating the trainer.\nThis is useful for callbacks that require access to the model or trainer.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\nReturns:\nList[callable]: A list of callback functions to be added\n\n\n\nintegrations.base.BasePlugin.add_callbacks_pre_trainer(cfg, model)\nsetup callbacks before creating the trainer.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs\n\n\n\nintegrations.base.BasePlugin.create_lr_scheduler(\n cfg,\n trainer,\n optimizer,\n num_training_steps,\n)\nCreates and returns a learning rate scheduler.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\noptimizer (object): The optimizer for training.\nnum_training_steps (int): Total number of training steps\nReturns:\nobject (LRScheduler): The created learning rate scheduler.\n\n\n\nintegrations.base.BasePlugin.create_optimizer(cfg, trainer)\nCreates and returns an optimizer for training.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\nReturns:\nobject: The created optimizer.\n\n\n\nintegrations.base.BasePlugin.get_input_args()\nReturns a pydantic model for the plugin’s input arguments.\n\n\n\nintegrations.base.BasePlugin.get_trainer_cls(cfg)\nReturns a custom class for the trainer.\nParameters:\ncfg (dict): The global axolotl configuration.\nReturns:\nclass: The class for the trainer.\n\n\n\nintegrations.base.BasePlugin.post_lora_load(cfg, model)\nPerforms actions after LoRA weights are loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_model_build(cfg, model)\nPerforms actions after the model is built/loaded, but before any adapters are applied.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ncfg\ndict\nThe configuration for the plugin.\nrequired\n\n\n\n\n\n\n\nintegrations.base.BasePlugin.post_model_load(cfg, model)\nPerforms actions after the model is loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_train(cfg, model)\nPerforms actions after training is complete.\nParameters:\ncfg (dict): The axolotl configuration\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_train_unload(cfg)\nPerforms actions after training is complete and the model is unloaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.pre_lora_load(cfg, model)\nPerforms actions before LoRA weights are loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.pre_model_load(cfg)\nPerforms actions before the model is loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.register(cfg)\nRegisters the plugin with the given configuration.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\n\n\nintegrations.base.PluginManager()\nThe PluginManager class is responsible for loading and managing plugins.\nIt should be a singleton so it can be accessed from anywhere in the codebase.\nAttributes:\nplugins (ListBasePlugin): A list of loaded plugins.\nMethods:\nget_instance(): Static method to get the singleton instance of PluginManager.\nregister(plugin_name: str): Registers a new plugin by its name.\npre_model_load(cfg): Calls the pre_model_load method of all registered plugins.\n\n\n\n\n\nName\nDescription\n\n\n\n\nadd_callbacks_post_trainer\nCalls the add_callbacks_post_trainer method of all registered plugins.\n\n\nadd_callbacks_pre_trainer\nCalls the add_callbacks_pre_trainer method of all registered plugins.\n\n\ncreate_lr_scheduler\nCalls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.\n\n\ncreate_optimizer\nCalls the create_optimizer method of all registered plugins and returns the first non-None optimizer.\n\n\nget_input_args\nReturns a list of Pydantic classes for all registered plugins’ input arguments.’\n\n\nget_instance\nReturns the singleton instance of PluginManager.\n\n\nget_trainer_cls\nCalls the get_trainer_cls method of all registered plugins and returns the first non-None trainer class.\n\n\npost_lora_load\nCalls the post_lora_load method of all registered plugins.\n\n\npost_model_build\nCalls the post_model_build method of all registered plugins after the model has been built/loaded,\n\n\npost_model_load\nCalls the post_model_load method of all registered plugins after the model has been loaded\n\n\npost_train\nCalls the post_train method of all registered plugins.\n\n\npost_train_unload\nCalls the post_train_unload method of all registered plugins.\n\n\npre_lora_load\nCalls the pre_lora_load method of all registered plugins.\n\n\npre_model_load\nCalls the pre_model_load method of all registered plugins.\n\n\nregister\nRegisters a new plugin by its name.\n\n\n\n\n\nintegrations.base.PluginManager.add_callbacks_post_trainer(cfg, trainer)\nCalls the add_callbacks_post_trainer method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\ntrainer (object): The trainer object for training.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs.\n\n\n\nintegrations.base.PluginManager.add_callbacks_pre_trainer(cfg, model)\nCalls the add_callbacks_pre_trainer method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs.\n\n\n\nintegrations.base.PluginManager.create_lr_scheduler(\n trainer,\n optimizer,\n num_training_steps,\n)\nCalls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.\nParameters:\ntrainer (object): The trainer object for training.\noptimizer (object): The optimizer for training.\nReturns:\nobject: The created learning rate scheduler, or None if none was found.\n\n\n\nintegrations.base.PluginManager.create_optimizer(trainer)\nCalls the create_optimizer method of all registered plugins and returns the first non-None optimizer.\nParameters:\ntrainer (object): The trainer object for training.\nReturns:\nobject: The created optimizer, or None if none was found.\n\n\n\nintegrations.base.PluginManager.get_input_args()\nReturns a list of Pydantic classes for all registered plugins’ input arguments.’\nReturns:\nlist[str]: A list of Pydantic classes for all registered plugins’ input arguments.’\n\n\n\nintegrations.base.PluginManager.get_instance()\nReturns the singleton instance of PluginManager.\nIf the instance doesn’t exist, it creates a new one.\n\n\n\nintegrations.base.PluginManager.get_trainer_cls(cfg)\nCalls the get_trainer_cls method of all registered plugins and returns the first non-None trainer class.\nParameters:\ncfg (dict): The configuration for the plugins.\nReturns:\nobject: The trainer class, or None if none was found.\n\n\n\nintegrations.base.PluginManager.post_lora_load(cfg, model)\nCalls the post_lora_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_model_build(cfg, model)\nCalls the post_model_build method of all registered plugins after the model has been built/loaded,\nbut before any adapters have been applied.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ncfg\ndict\nThe configuration for the plugins.\nrequired\n\n\nmodel\nobject\nThe loaded model.\nrequired\n\n\n\n\n\n\n\nintegrations.base.PluginManager.post_model_load(cfg, model)\nCalls the post_model_load method of all registered plugins after the model has been loaded\ninclusive of any adapters\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_train(cfg, model)\nCalls the post_train method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_train_unload(cfg)\nCalls the post_train_unload method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.pre_lora_load(cfg, model)\nCalls the pre_lora_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.pre_model_load(cfg)\nCalls the pre_model_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.register(plugin_name)\nRegisters a new plugin by its name.\nParameters:\nplugin_name (str): The name of the plugin to be registered.\nReturns:\nNone\nRaises:\nImportError: If the plugin module cannot be imported.\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nload_plugin\nLoads a plugin based on the given plugin name.\n\n\n\n\n\nintegrations.base.load_plugin(plugin_name)\nLoads a plugin based on the given plugin name.\nThe plugin name should be in the format “module_name.class_name”.\nThis function splits the plugin name into module and class, imports the module,\nretrieves the class from the module, and creates an instance of the class.\nParameters:\nplugin_name (str): The name of the plugin to be loaded. The name should be in the format “module_name.class_name”.\nReturns:\nBasePlugin: An instance of the loaded plugin.\nRaises:\nImportError: If the plugin module cannot be imported."
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"section": "",
- "text": "Name\nDescription\n\n\n\n\nBaseOptimizerFactory\nBase class for factories to create custom optimizers\n\n\nBasePlugin\nBase class for all plugins. Defines the interface for plugin methods.\n\n\nPluginManager\nThe PluginManager class is responsible for loading and managing plugins.\n\n\n\n\n\nintegrations.base.BaseOptimizerFactory()\nBase class for factories to create custom optimizers\n\n\n\nintegrations.base.BasePlugin(self)\nBase class for all plugins. Defines the interface for plugin methods.\nAttributes:\nNone\nMethods:\nregister(cfg): Registers the plugin with the given configuration.\npre_model_load(cfg): Performs actions before the model is loaded.\npost_model_build(cfg, model): Performs actions after the model is loaded, but before LoRA adapters are applied.\npre_lora_load(cfg, model): Performs actions before LoRA weights are loaded.\npost_lora_load(cfg, model): Performs actions after LoRA weights are loaded.\npost_model_load(cfg, model): Performs actions after the model is loaded, inclusive of any adapters.\ncreate_optimizer(cfg, trainer): Creates and returns an optimizer for training.\ncreate_lr_scheduler(cfg, trainer, optimizer): Creates and returns a learning rate scheduler.\nadd_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training.\nadd_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training.\n\n\n\n\n\nName\nDescription\n\n\n\n\nadd_callbacks_post_trainer\nAdds callbacks to the trainer after creating the trainer.\n\n\nadd_callbacks_pre_trainer\nsetup callbacks before creating the trainer.\n\n\ncreate_lr_scheduler\nCreates and returns a learning rate scheduler.\n\n\ncreate_optimizer\nCreates and returns an optimizer for training.\n\n\nget_input_args\nReturns a pydantic model for the plugin’s input arguments.\n\n\nget_trainer_cls\nReturns a custom class for the trainer.\n\n\npost_lora_load\nPerforms actions after LoRA weights are loaded.\n\n\npost_model_build\nPerforms actions after the model is built/loaded, but before any adapters are applied.\n\n\npost_model_load\nPerforms actions after the model is loaded.\n\n\npost_train\nPerforms actions after training is complete.\n\n\npost_train_unload\nPerforms actions after training is complete and the model is unloaded.\n\n\npre_lora_load\nPerforms actions before LoRA weights are loaded.\n\n\npre_model_load\nPerforms actions before the model is loaded.\n\n\nregister\nRegisters the plugin with the given configuration.\n\n\n\n\n\nintegrations.base.BasePlugin.add_callbacks_post_trainer(cfg, trainer)\nAdds callbacks to the trainer after creating the trainer.\nThis is useful for callbacks that require access to the model or trainer.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\nReturns:\nList[callable]: A list of callback functions to be added\n\n\n\nintegrations.base.BasePlugin.add_callbacks_pre_trainer(cfg, model)\nsetup callbacks before creating the trainer.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs\n\n\n\nintegrations.base.BasePlugin.create_lr_scheduler(cfg, trainer, optimizer)\nCreates and returns a learning rate scheduler.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\noptimizer (object): The optimizer for training.\nReturns:\nobject: The created learning rate scheduler.\n\n\n\nintegrations.base.BasePlugin.create_optimizer(cfg, trainer)\nCreates and returns an optimizer for training.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\nReturns:\nobject: The created optimizer.\n\n\n\nintegrations.base.BasePlugin.get_input_args()\nReturns a pydantic model for the plugin’s input arguments.\n\n\n\nintegrations.base.BasePlugin.get_trainer_cls(cfg)\nReturns a custom class for the trainer.\nParameters:\ncfg (dict): The global axolotl configuration.\nReturns:\nclass: The class for the trainer.\n\n\n\nintegrations.base.BasePlugin.post_lora_load(cfg, model)\nPerforms actions after LoRA weights are loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_model_build(cfg, model)\nPerforms actions after the model is built/loaded, but before any adapters are applied.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ncfg\ndict\nThe configuration for the plugin.\nrequired\n\n\n\n\n\n\n\nintegrations.base.BasePlugin.post_model_load(cfg, model)\nPerforms actions after the model is loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_train(cfg, model)\nPerforms actions after training is complete.\nParameters:\ncfg (dict): The axolotl configuration\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_train_unload(cfg)\nPerforms actions after training is complete and the model is unloaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.pre_lora_load(cfg, model)\nPerforms actions before LoRA weights are loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.pre_model_load(cfg)\nPerforms actions before the model is loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.register(cfg)\nRegisters the plugin with the given configuration.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\n\n\nintegrations.base.PluginManager()\nThe PluginManager class is responsible for loading and managing plugins.\nIt should be a singleton so it can be accessed from anywhere in the codebase.\nAttributes:\nplugins (ListBasePlugin): A list of loaded plugins.\nMethods:\nget_instance(): Static method to get the singleton instance of PluginManager.\nregister(plugin_name: str): Registers a new plugin by its name.\npre_model_load(cfg): Calls the pre_model_load method of all registered plugins.\n\n\n\n\n\nName\nDescription\n\n\n\n\nadd_callbacks_post_trainer\nCalls the add_callbacks_post_trainer method of all registered plugins.\n\n\nadd_callbacks_pre_trainer\nCalls the add_callbacks_pre_trainer method of all registered plugins.\n\n\ncreate_lr_scheduler\nCalls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.\n\n\ncreate_optimizer\nCalls the create_optimizer method of all registered plugins and returns the first non-None optimizer.\n\n\nget_input_args\nReturns a list of Pydantic classes for all registered plugins’ input arguments.’\n\n\nget_instance\nReturns the singleton instance of PluginManager.\n\n\nget_trainer_cls\nCalls the get_trainer_cls method of all registered plugins and returns the first non-None trainer class.\n\n\npost_lora_load\nCalls the post_lora_load method of all registered plugins.\n\n\npost_model_build\nCalls the post_model_build method of all registered plugins after the model has been built/loaded,\n\n\npost_model_load\nCalls the post_model_load method of all registered plugins after the model has been loaded\n\n\npost_train\nCalls the post_train method of all registered plugins.\n\n\npost_train_unload\nCalls the post_train_unload method of all registered plugins.\n\n\npre_lora_load\nCalls the pre_lora_load method of all registered plugins.\n\n\npre_model_load\nCalls the pre_model_load method of all registered plugins.\n\n\nregister\nRegisters a new plugin by its name.\n\n\n\n\n\nintegrations.base.PluginManager.add_callbacks_post_trainer(cfg, trainer)\nCalls the add_callbacks_post_trainer method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\ntrainer (object): The trainer object for training.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs.\n\n\n\nintegrations.base.PluginManager.add_callbacks_pre_trainer(cfg, model)\nCalls the add_callbacks_pre_trainer method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs.\n\n\n\nintegrations.base.PluginManager.create_lr_scheduler(trainer, optimizer)\nCalls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.\nParameters:\ntrainer (object): The trainer object for training.\noptimizer (object): The optimizer for training.\nReturns:\nobject: The created learning rate scheduler, or None if none was found.\n\n\n\nintegrations.base.PluginManager.create_optimizer(trainer)\nCalls the create_optimizer method of all registered plugins and returns the first non-None optimizer.\nParameters:\ntrainer (object): The trainer object for training.\nReturns:\nobject: The created optimizer, or None if none was found.\n\n\n\nintegrations.base.PluginManager.get_input_args()\nReturns a list of Pydantic classes for all registered plugins’ input arguments.’\nReturns:\nlist[str]: A list of Pydantic classes for all registered plugins’ input arguments.’\n\n\n\nintegrations.base.PluginManager.get_instance()\nReturns the singleton instance of PluginManager.\nIf the instance doesn’t exist, it creates a new one.\n\n\n\nintegrations.base.PluginManager.get_trainer_cls(cfg)\nCalls the get_trainer_cls method of all registered plugins and returns the first non-None trainer class.\nParameters:\ncfg (dict): The configuration for the plugins.\nReturns:\nobject: The trainer class, or None if none was found.\n\n\n\nintegrations.base.PluginManager.post_lora_load(cfg, model)\nCalls the post_lora_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_model_build(cfg, model)\nCalls the post_model_build method of all registered plugins after the model has been built/loaded,\nbut before any adapters have been applied.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ncfg\ndict\nThe configuration for the plugins.\nrequired\n\n\nmodel\nobject\nThe loaded model.\nrequired\n\n\n\n\n\n\n\nintegrations.base.PluginManager.post_model_load(cfg, model)\nCalls the post_model_load method of all registered plugins after the model has been loaded\ninclusive of any adapters\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_train(cfg, model)\nCalls the post_train method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_train_unload(cfg)\nCalls the post_train_unload method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.pre_lora_load(cfg, model)\nCalls the pre_lora_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.pre_model_load(cfg)\nCalls the pre_model_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.register(plugin_name)\nRegisters a new plugin by its name.\nParameters:\nplugin_name (str): The name of the plugin to be registered.\nReturns:\nNone\nRaises:\nImportError: If the plugin module cannot be imported."
+ "text": "Name\nDescription\n\n\n\n\nBaseOptimizerFactory\nBase class for factories to create custom optimizers\n\n\nBasePlugin\nBase class for all plugins. Defines the interface for plugin methods.\n\n\nPluginManager\nThe PluginManager class is responsible for loading and managing plugins.\n\n\n\n\n\nintegrations.base.BaseOptimizerFactory()\nBase class for factories to create custom optimizers\n\n\n\nintegrations.base.BasePlugin(self)\nBase class for all plugins. Defines the interface for plugin methods.\nAttributes:\nNone\nMethods:\nregister(cfg): Registers the plugin with the given configuration.\npre_model_load(cfg): Performs actions before the model is loaded.\npost_model_build(cfg, model): Performs actions after the model is loaded, but before LoRA adapters are applied.\npre_lora_load(cfg, model): Performs actions before LoRA weights are loaded.\npost_lora_load(cfg, model): Performs actions after LoRA weights are loaded.\npost_model_load(cfg, model): Performs actions after the model is loaded, inclusive of any adapters.\ncreate_optimizer(cfg, trainer): Creates and returns an optimizer for training.\ncreate_lr_scheduler(cfg, trainer, optimizer, num_training_steps): Creates and returns a learning rate scheduler.\nadd_callbacks_pre_trainer(cfg, model): Adds callbacks to the trainer before training.\nadd_callbacks_post_trainer(cfg, trainer): Adds callbacks to the trainer after training.\n\n\n\n\n\nName\nDescription\n\n\n\n\nadd_callbacks_post_trainer\nAdds callbacks to the trainer after creating the trainer.\n\n\nadd_callbacks_pre_trainer\nsetup callbacks before creating the trainer.\n\n\ncreate_lr_scheduler\nCreates and returns a learning rate scheduler.\n\n\ncreate_optimizer\nCreates and returns an optimizer for training.\n\n\nget_input_args\nReturns a pydantic model for the plugin’s input arguments.\n\n\nget_trainer_cls\nReturns a custom class for the trainer.\n\n\npost_lora_load\nPerforms actions after LoRA weights are loaded.\n\n\npost_model_build\nPerforms actions after the model is built/loaded, but before any adapters are applied.\n\n\npost_model_load\nPerforms actions after the model is loaded.\n\n\npost_train\nPerforms actions after training is complete.\n\n\npost_train_unload\nPerforms actions after training is complete and the model is unloaded.\n\n\npre_lora_load\nPerforms actions before LoRA weights are loaded.\n\n\npre_model_load\nPerforms actions before the model is loaded.\n\n\nregister\nRegisters the plugin with the given configuration.\n\n\n\n\n\nintegrations.base.BasePlugin.add_callbacks_post_trainer(cfg, trainer)\nAdds callbacks to the trainer after creating the trainer.\nThis is useful for callbacks that require access to the model or trainer.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\nReturns:\nList[callable]: A list of callback functions to be added\n\n\n\nintegrations.base.BasePlugin.add_callbacks_pre_trainer(cfg, model)\nsetup callbacks before creating the trainer.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs\n\n\n\nintegrations.base.BasePlugin.create_lr_scheduler(\n cfg,\n trainer,\n optimizer,\n num_training_steps,\n)\nCreates and returns a learning rate scheduler.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\noptimizer (object): The optimizer for training.\nnum_training_steps (int): Total number of training steps\nReturns:\nobject (LRScheduler): The created learning rate scheduler.\n\n\n\nintegrations.base.BasePlugin.create_optimizer(cfg, trainer)\nCreates and returns an optimizer for training.\nParameters:\ncfg (dict): The configuration for the plugin.\ntrainer (object): The trainer object for training.\nReturns:\nobject: The created optimizer.\n\n\n\nintegrations.base.BasePlugin.get_input_args()\nReturns a pydantic model for the plugin’s input arguments.\n\n\n\nintegrations.base.BasePlugin.get_trainer_cls(cfg)\nReturns a custom class for the trainer.\nParameters:\ncfg (dict): The global axolotl configuration.\nReturns:\nclass: The class for the trainer.\n\n\n\nintegrations.base.BasePlugin.post_lora_load(cfg, model)\nPerforms actions after LoRA weights are loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_model_build(cfg, model)\nPerforms actions after the model is built/loaded, but before any adapters are applied.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ncfg\ndict\nThe configuration for the plugin.\nrequired\n\n\n\n\n\n\n\nintegrations.base.BasePlugin.post_model_load(cfg, model)\nPerforms actions after the model is loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_train(cfg, model)\nPerforms actions after training is complete.\nParameters:\ncfg (dict): The axolotl configuration\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.post_train_unload(cfg)\nPerforms actions after training is complete and the model is unloaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.pre_lora_load(cfg, model)\nPerforms actions before LoRA weights are loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.pre_model_load(cfg)\nPerforms actions before the model is loaded.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\nintegrations.base.BasePlugin.register(cfg)\nRegisters the plugin with the given configuration.\nParameters:\ncfg (dict): The configuration for the plugin.\nReturns:\nNone\n\n\n\n\n\nintegrations.base.PluginManager()\nThe PluginManager class is responsible for loading and managing plugins.\nIt should be a singleton so it can be accessed from anywhere in the codebase.\nAttributes:\nplugins (ListBasePlugin): A list of loaded plugins.\nMethods:\nget_instance(): Static method to get the singleton instance of PluginManager.\nregister(plugin_name: str): Registers a new plugin by its name.\npre_model_load(cfg): Calls the pre_model_load method of all registered plugins.\n\n\n\n\n\nName\nDescription\n\n\n\n\nadd_callbacks_post_trainer\nCalls the add_callbacks_post_trainer method of all registered plugins.\n\n\nadd_callbacks_pre_trainer\nCalls the add_callbacks_pre_trainer method of all registered plugins.\n\n\ncreate_lr_scheduler\nCalls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.\n\n\ncreate_optimizer\nCalls the create_optimizer method of all registered plugins and returns the first non-None optimizer.\n\n\nget_input_args\nReturns a list of Pydantic classes for all registered plugins’ input arguments.’\n\n\nget_instance\nReturns the singleton instance of PluginManager.\n\n\nget_trainer_cls\nCalls the get_trainer_cls method of all registered plugins and returns the first non-None trainer class.\n\n\npost_lora_load\nCalls the post_lora_load method of all registered plugins.\n\n\npost_model_build\nCalls the post_model_build method of all registered plugins after the model has been built/loaded,\n\n\npost_model_load\nCalls the post_model_load method of all registered plugins after the model has been loaded\n\n\npost_train\nCalls the post_train method of all registered plugins.\n\n\npost_train_unload\nCalls the post_train_unload method of all registered plugins.\n\n\npre_lora_load\nCalls the pre_lora_load method of all registered plugins.\n\n\npre_model_load\nCalls the pre_model_load method of all registered plugins.\n\n\nregister\nRegisters a new plugin by its name.\n\n\n\n\n\nintegrations.base.PluginManager.add_callbacks_post_trainer(cfg, trainer)\nCalls the add_callbacks_post_trainer method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\ntrainer (object): The trainer object for training.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs.\n\n\n\nintegrations.base.PluginManager.add_callbacks_pre_trainer(cfg, model)\nCalls the add_callbacks_pre_trainer method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nList[callable]: A list of callback functions to be added to the TrainingArgs.\n\n\n\nintegrations.base.PluginManager.create_lr_scheduler(\n trainer,\n optimizer,\n num_training_steps,\n)\nCalls the create_lr_scheduler method of all registered plugins and returns the first non-None scheduler.\nParameters:\ntrainer (object): The trainer object for training.\noptimizer (object): The optimizer for training.\nReturns:\nobject: The created learning rate scheduler, or None if none was found.\n\n\n\nintegrations.base.PluginManager.create_optimizer(trainer)\nCalls the create_optimizer method of all registered plugins and returns the first non-None optimizer.\nParameters:\ntrainer (object): The trainer object for training.\nReturns:\nobject: The created optimizer, or None if none was found.\n\n\n\nintegrations.base.PluginManager.get_input_args()\nReturns a list of Pydantic classes for all registered plugins’ input arguments.’\nReturns:\nlist[str]: A list of Pydantic classes for all registered plugins’ input arguments.’\n\n\n\nintegrations.base.PluginManager.get_instance()\nReturns the singleton instance of PluginManager.\nIf the instance doesn’t exist, it creates a new one.\n\n\n\nintegrations.base.PluginManager.get_trainer_cls(cfg)\nCalls the get_trainer_cls method of all registered plugins and returns the first non-None trainer class.\nParameters:\ncfg (dict): The configuration for the plugins.\nReturns:\nobject: The trainer class, or None if none was found.\n\n\n\nintegrations.base.PluginManager.post_lora_load(cfg, model)\nCalls the post_lora_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_model_build(cfg, model)\nCalls the post_model_build method of all registered plugins after the model has been built/loaded,\nbut before any adapters have been applied.\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\ncfg\ndict\nThe configuration for the plugins.\nrequired\n\n\nmodel\nobject\nThe loaded model.\nrequired\n\n\n\n\n\n\n\nintegrations.base.PluginManager.post_model_load(cfg, model)\nCalls the post_model_load method of all registered plugins after the model has been loaded\ninclusive of any adapters\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_train(cfg, model)\nCalls the post_train method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.post_train_unload(cfg)\nCalls the post_train_unload method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.pre_lora_load(cfg, model)\nCalls the pre_lora_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nmodel (object): The loaded model.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.pre_model_load(cfg)\nCalls the pre_model_load method of all registered plugins.\nParameters:\ncfg (dict): The configuration for the plugins.\nReturns:\nNone\n\n\n\nintegrations.base.PluginManager.register(plugin_name)\nRegisters a new plugin by its name.\nParameters:\nplugin_name (str): The name of the plugin to be registered.\nReturns:\nNone\nRaises:\nImportError: If the plugin module cannot be imported."
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