/opt/canhelp/node_modules/openai/resources/fine-tuning
Edit: /opt/canhelp/node_modules/openai/resources/fine-tuning/methods.d.mts (4147B)
import { APIResource } from "../../core/resource.mjs";
import * as GraderModelsAPI from "../graders/grader-models.mjs";
export declare class Methods extends APIResource {
}
/**
* The hyperparameters used for the DPO fine-tuning job.
*/
export interface DpoHyperparameters {
/**
* Number of examples in each batch. A larger batch size means that model
* parameters are updated less frequently, but with lower variance.
*/
batch_size?: 'auto' | number;
/**
* The beta value for the DPO method. A higher beta value will increase the weight
* of the penalty between the policy and reference model.
*/
beta?: 'auto' | number;
/**
* Scaling factor for the learning rate. A smaller learning rate may be useful to
* avoid overfitting.
*/
learning_rate_multiplier?: 'auto' | number;
/**
* The number of epochs to train the model for. An epoch refers to one full cycle
* through the training dataset.
*/
n_epochs?: 'auto' | number;
}
/**
* Configuration for the DPO fine-tuning method.
*/
export interface DpoMethod {
/**
* The hyperparameters used for the DPO fine-tuning job.
*/
hyperparameters?: DpoHyperparameters;
}
/**
* The hyperparameters used for the reinforcement fine-tuning job.
*/
export interface ReinforcementHyperparameters {
/**
* Number of examples in each batch. A larger batch size means that model
* parameters are updated less frequently, but with lower variance.
*/
batch_size?: 'auto' | number;
/**
* Multiplier on amount of compute used for exploring search space during training.
*/
compute_multiplier?: 'auto' | number;
/**
* The number of training steps between evaluation runs.
*/
eval_interval?: 'auto' | number;
/**
* Number of evaluation samples to generate per training step.
*/
eval_samples?: 'auto' | number;
/**
* Scaling factor for the learning rate. A smaller learning rate may be useful to
* avoid overfitting.
*/
learning_rate_multiplier?: 'auto' | number;
/**
* The number of epochs to train the model for. An epoch refers to one full cycle
* through the training dataset.
*/
n_epochs?: 'auto' | number;
/**
* Level of reasoning effort.
*/
reasoning_effort?: 'default' | 'low' | 'medium' | 'high';
}
/**
* Configuration for the reinforcement fine-tuning method.
*/
export interface ReinforcementMethod {
/**
* The grader used for the fine-tuning job.
*/
grader: GraderModelsAPI.StringCheckGrader | GraderModelsAPI.TextSimilarityGrader | GraderModelsAPI.PythonGrader | GraderModelsAPI.ScoreModelGrader | GraderModelsAPI.MultiGrader;
/**
* The hyperparameters used for the reinforcement fine-tuning job.
*/
hyperparameters?: ReinforcementHyperparameters;
}
/**
* The hyperparameters used for the fine-tuning job.
*/
export interface SupervisedHyperparameters {
/**
* Number of examples in each batch. A larger batch size means that model
* parameters are updated less frequently, but with lower variance.
*/
batch_size?: 'auto' | number;
/**
* Scaling factor for the learning rate. A smaller learning rate may be useful to
* avoid overfitting.
*/
learning_rate_multiplier?: 'auto' | number;
/**
* The number of epochs to train the model for. An epoch refers to one full cycle
* through the training dataset.
*/
n_epochs?: 'auto' | number;
}
/**
* Configuration for the supervised fine-tuning method.
*/
export interface SupervisedMethod {
/**
* The hyperparameters used for the fine-tuning job.
*/
hyperparameters?: SupervisedHyperparameters;
}
export declare namespace Methods {
export { type DpoHyperparameters as DpoHyperparameters, type DpoMethod as DpoMethod, type ReinforcementHyperparameters as ReinforcementHyperparameters, type ReinforcementMethod as ReinforcementMethod, type SupervisedHyperparameters as SupervisedHyperparameters, type SupervisedMethod as SupervisedMethod, };
}
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