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# coding: utf-8 # Copyright (c) 2016, 2026, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. # NOTE: This class is auto generated by OracleSDKGenerator. DO NOT EDIT. API Version: 20231130 from .training_config import TrainingConfig from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class LoraTrainingConfig(TrainingConfig): """ The Lora training method hyperparameters. """ def __init__(self, **kwargs): """ Initializes a new LoraTrainingConfig object with values from keyword arguments. The default value of the :py:attr:`~oci.generative_ai.models.LoraTrainingConfig.training_config_type` attribute of this class is ``LORA_TRAINING_CONFIG`` and it should not be changed. The following keyword arguments are supported (corresponding to the getters/setters of this class): :param training_config_type: The value to assign to the training_config_type property of this LoraTrainingConfig. Allowed values for this property are: "TFEW_TRAINING_CONFIG", "VANILLA_TRAINING_CONFIG", "LORA_TRAINING_CONFIG" :type training_config_type: str :param total_training_epochs: The value to assign to the total_training_epochs property of this LoraTrainingConfig. :type total_training_epochs: int :param learning_rate: The value to assign to the learning_rate property of this LoraTrainingConfig. :type learning_rate: float :param training_batch_size: The value to assign to the training_batch_size property of this LoraTrainingConfig. :type training_batch_size: int :param early_stopping_patience: The value to assign to the early_stopping_patience property of this LoraTrainingConfig. :type early_stopping_patience: int :param early_stopping_threshold: The value to assign to the early_stopping_threshold property of this LoraTrainingConfig. :type early_stopping_threshold: float :param log_model_metrics_interval_in_steps: The value to assign to the log_model_metrics_interval_in_steps property of this LoraTrainingConfig. :type log_model_metrics_interval_in_steps: int :param lora_r: The value to assign to the lora_r property of this LoraTrainingConfig. :type lora_r: int :param lora_alpha: The value to assign to the lora_alpha property of this LoraTrainingConfig. :type lora_alpha: int :param lora_dropout: The value to assign to the lora_dropout property of this LoraTrainingConfig. :type lora_dropout: float """ self.swagger_types = { 'training_config_type': 'str', 'total_training_epochs': 'int', 'learning_rate': 'float', 'training_batch_size': 'int', 'early_stopping_patience': 'int', 'early_stopping_threshold': 'float', 'log_model_metrics_interval_in_steps': 'int', 'lora_r': 'int', 'lora_alpha': 'int', 'lora_dropout': 'float' } self.attribute_map = { 'training_config_type': 'trainingConfigType', 'total_training_epochs': 'totalTrainingEpochs', 'learning_rate': 'learningRate', 'training_batch_size': 'trainingBatchSize', 'early_stopping_patience': 'earlyStoppingPatience', 'early_stopping_threshold': 'earlyStoppingThreshold', 'log_model_metrics_interval_in_steps': 'logModelMetricsIntervalInSteps', 'lora_r': 'loraR', 'lora_alpha': 'loraAlpha', 'lora_dropout': 'loraDropout' } self._training_config_type = None self._total_training_epochs = None self._learning_rate = None self._training_batch_size = None self._early_stopping_patience = None self._early_stopping_threshold = None self._log_model_metrics_interval_in_steps = None self._lora_r = None self._lora_alpha = None self._lora_dropout = None self._training_config_type = 'LORA_TRAINING_CONFIG' @property def lora_r(self): """ Gets the lora_r of this LoraTrainingConfig. This parameter represents the LoRA rank of the update matrices. :return: The lora_r of this LoraTrainingConfig. :rtype: int """ return self._lora_r @lora_r.setter def lora_r(self, lora_r): """ Sets the lora_r of this LoraTrainingConfig. This parameter represents the LoRA rank of the update matrices. :param lora_r: The lora_r of this LoraTrainingConfig. :type: int """ self._lora_r = lora_r @property def lora_alpha(self): """ Gets the lora_alpha of this LoraTrainingConfig. This parameter represents the scaling factor for the weight matrices in LoRA. :return: The lora_alpha of this LoraTrainingConfig. :rtype: int """ return self._lora_alpha @lora_alpha.setter def lora_alpha(self, lora_alpha): """ Sets the lora_alpha of this LoraTrainingConfig. This parameter represents the scaling factor for the weight matrices in LoRA. :param lora_alpha: The lora_alpha of this LoraTrainingConfig. :type: int """ self._lora_alpha = lora_alpha @property def lora_dropout(self): """ Gets the lora_dropout of this LoraTrainingConfig. This parameter indicates the dropout probability for LoRA layers. :return: The lora_dropout of this LoraTrainingConfig. :rtype: float """ return self._lora_dropout @lora_dropout.setter def lora_dropout(self, lora_dropout): """ Sets the lora_dropout of this LoraTrainingConfig. This parameter indicates the dropout probability for LoRA layers. :param lora_dropout: The lora_dropout of this LoraTrainingConfig. :type: float """ self._lora_dropout = lora_dropout def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

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