methods.ts•4.08 kB
// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
import { APIResource } from '../../resource';
import * as GraderModelsAPI from '../graders/grader-models';
export 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,
};
}