AI_botter/node_modules/openai/resources/fine-tuning/jobs.d.ts

import * as Core from 'openai/core';
import { APIResource } from 'openai/resource';
import * as JobsAPI from 'openai/resources/fine-tuning/jobs';
import { CursorPage, type CursorPageParams } from 'openai/pagination';
export declare class Jobs extends APIResource {
    /**
     * Creates a fine-tuning job which begins the process of creating a new model from
     * a given dataset.
     *
     * Response includes details of the enqueued job including job status and the name
     * of the fine-tuned models once complete.
     *
     * [Learn more about fine-tuning](https://platform.openai.com/docs/guides/fine-tuning)
     */
    create(body: JobCreateParams, options?: Core.RequestOptions): Core.APIPromise<FineTuningJob>;
    /**
     * Get info about a fine-tuning job.
     *
     * [Learn more about fine-tuning](https://platform.openai.com/docs/guides/fine-tuning)
     */
    retrieve(fineTuningJobId: string, options?: Core.RequestOptions): Core.APIPromise<FineTuningJob>;
    /**
     * List your organization's fine-tuning jobs
     */
    list(query?: JobListParams, options?: Core.RequestOptions): Core.PagePromise<FineTuningJobsPage, FineTuningJob>;
    list(options?: Core.RequestOptions): Core.PagePromise<FineTuningJobsPage, FineTuningJob>;
    /**
     * Immediately cancel a fine-tune job.
     */
    cancel(fineTuningJobId: string, options?: Core.RequestOptions): Core.APIPromise<FineTuningJob>;
    /**
     * Get status updates for a fine-tuning job.
     */
    listEvents(fineTuningJobId: string, query?: JobListEventsParams, options?: Core.RequestOptions): Core.PagePromise<FineTuningJobEventsPage, FineTuningJobEvent>;
    listEvents(fineTuningJobId: string, options?: Core.RequestOptions): Core.PagePromise<FineTuningJobEventsPage, FineTuningJobEvent>;
}
export declare class FineTuningJobsPage extends CursorPage<FineTuningJob> {
}
export declare class FineTuningJobEventsPage extends CursorPage<FineTuningJobEvent> {
}
/**
 * The `fine_tuning.job` object represents a fine-tuning job that has been created
 * through the API.
 */
export interface FineTuningJob {
    /**
     * The object identifier, which can be referenced in the API endpoints.
     */
    id: string;
    /**
     * The Unix timestamp (in seconds) for when the fine-tuning job was created.
     */
    created_at: number;
    /**
     * For fine-tuning jobs that have `failed`, this will contain more information on
     * the cause of the failure.
     */
    error: FineTuningJob.Error | null;
    /**
     * The name of the fine-tuned model that is being created. The value will be null
     * if the fine-tuning job is still running.
     */
    fine_tuned_model: string | null;
    /**
     * The Unix timestamp (in seconds) for when the fine-tuning job was finished. The
     * value will be null if the fine-tuning job is still running.
     */
    finished_at: number | null;
    /**
     * The hyperparameters used for the fine-tuning job. See the
     * [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning) for
     * more details.
     */
    hyperparameters: FineTuningJob.Hyperparameters;
    /**
     * The base model that is being fine-tuned.
     */
    model: string;
    /**
     * The object type, which is always "fine_tuning.job".
     */
    object: 'fine_tuning.job';
    /**
     * The organization that owns the fine-tuning job.
     */
    organization_id: string;
    /**
     * The compiled results file ID(s) for the fine-tuning job. You can retrieve the
     * results with the
     * [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
     */
    result_files: Array<string>;
    /**
     * The current status of the fine-tuning job, which can be either
     * `validating_files`, `queued`, `running`, `succeeded`, `failed`, or `cancelled`.
     */
    status: 'validating_files' | 'queued' | 'running' | 'succeeded' | 'failed' | 'cancelled';
    /**
     * The total number of billable tokens processed by this fine-tuning job. The value
     * will be null if the fine-tuning job is still running.
     */
    trained_tokens: number | null;
    /**
     * The file ID used for training. You can retrieve the training data with the
     * [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
     */
    training_file: string;
    /**
     * The file ID used for validation. You can retrieve the validation results with
     * the
     * [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
     */
    validation_file: string | null;
}
export declare namespace FineTuningJob {
    /**
     * For fine-tuning jobs that have `failed`, this will contain more information on
     * the cause of the failure.
     */
    interface Error {
        /**
         * A machine-readable error code.
         */
        code: string;
        /**
         * A human-readable error message.
         */
        message: string;
        /**
         * The parameter that was invalid, usually `training_file` or `validation_file`.
         * This field will be null if the failure was not parameter-specific.
         */
        param: string | null;
    }
    /**
     * The hyperparameters used for the fine-tuning job. See the
     * [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning) for
     * more details.
     */
    interface Hyperparameters {
        /**
         * The number of epochs to train the model for. An epoch refers to one full cycle
         * through the training dataset. "auto" decides the optimal number of epochs based
         * on the size of the dataset. If setting the number manually, we support any
         * number between 1 and 50 epochs.
         */
        n_epochs: 'auto' | number;
    }
}
/**
 * Fine-tuning job event object
 */
export interface FineTuningJobEvent {
    id: string;
    created_at: number;
    level: 'info' | 'warn' | 'error';
    message: string;
    object: 'fine_tuning.job.event';
}
export interface JobCreateParams {
    /**
     * The name of the model to fine-tune. You can select one of the
     * [supported models](https://platform.openai.com/docs/guides/fine-tuning/what-models-can-be-fine-tuned).
     */
    model: (string & {}) | 'babbage-002' | 'davinci-002' | 'gpt-3.5-turbo';
    /**
     * The ID of an uploaded file that contains training data.
     *
     * See [upload file](https://platform.openai.com/docs/api-reference/files/upload)
     * for how to upload a file.
     *
     * Your dataset must be formatted as a JSONL file. Additionally, you must upload
     * your file with the purpose `fine-tune`.
     *
     * See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning)
     * for more details.
     */
    training_file: string;
    /**
     * The hyperparameters used for the fine-tuning job.
     */
    hyperparameters?: JobCreateParams.Hyperparameters;
    /**
     * A string of up to 18 characters that will be added to your fine-tuned model
     * name.
     *
     * For example, a `suffix` of "custom-model-name" would produce a model name like
     * `ft:gpt-3.5-turbo:openai:custom-model-name:7p4lURel`.
     */
    suffix?: string | null;
    /**
     * The ID of an uploaded file that contains validation data.
     *
     * If you provide this file, the data is used to generate validation metrics
     * periodically during fine-tuning. These metrics can be viewed in the fine-tuning
     * results file. The same data should not be present in both train and validation
     * files.
     *
     * Your dataset must be formatted as a JSONL file. You must upload your file with
     * the purpose `fine-tune`.
     *
     * See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning)
     * for more details.
     */
    validation_file?: string | null;
}
export declare namespace JobCreateParams {
    /**
     * The hyperparameters used for the fine-tuning job.
     */
    interface Hyperparameters {
        /**
         * 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;
    }
}
export interface JobListParams extends CursorPageParams {
}
export interface JobListEventsParams extends CursorPageParams {
}
export declare namespace Jobs {
    export import FineTuningJob = JobsAPI.FineTuningJob;
    export import FineTuningJobEvent = JobsAPI.FineTuningJobEvent;
    export import FineTuningJobsPage = JobsAPI.FineTuningJobsPage;
    export import FineTuningJobEventsPage = JobsAPI.FineTuningJobEventsPage;
    export import JobCreateParams = JobsAPI.JobCreateParams;
    export import JobListParams = JobsAPI.JobListParams;
    export import JobListEventsParams = JobsAPI.JobListEventsParams;
}
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