367 lines
14 KiB
TypeScript
367 lines
14 KiB
TypeScript
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import { APIResource } from "../../../resource.js";
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import * as Core from "../../../core.js";
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import * as JobsAPI from "./jobs.js";
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import * as CheckpointsAPI from "./checkpoints.js";
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import { CursorPage, type CursorPageParams } from "../../../pagination.js";
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export declare class Jobs extends APIResource {
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checkpoints: CheckpointsAPI.Checkpoints;
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/**
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* Creates a fine-tuning job which begins the process of creating a new model from
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* a given dataset.
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*
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* Response includes details of the enqueued job including job status and the name
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* of the fine-tuned models once complete.
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*
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* [Learn more about fine-tuning](https://platform.openai.com/docs/guides/fine-tuning)
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*/
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create(body: JobCreateParams, options?: Core.RequestOptions): Core.APIPromise<FineTuningJob>;
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/**
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* Get info about a fine-tuning job.
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*
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* [Learn more about fine-tuning](https://platform.openai.com/docs/guides/fine-tuning)
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*/
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retrieve(fineTuningJobId: string, options?: Core.RequestOptions): Core.APIPromise<FineTuningJob>;
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/**
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* List your organization's fine-tuning jobs
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*/
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list(query?: JobListParams, options?: Core.RequestOptions): Core.PagePromise<FineTuningJobsPage, FineTuningJob>;
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list(options?: Core.RequestOptions): Core.PagePromise<FineTuningJobsPage, FineTuningJob>;
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/**
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* Immediately cancel a fine-tune job.
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*/
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cancel(fineTuningJobId: string, options?: Core.RequestOptions): Core.APIPromise<FineTuningJob>;
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/**
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* Get status updates for a fine-tuning job.
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*/
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listEvents(fineTuningJobId: string, query?: JobListEventsParams, options?: Core.RequestOptions): Core.PagePromise<FineTuningJobEventsPage, FineTuningJobEvent>;
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listEvents(fineTuningJobId: string, options?: Core.RequestOptions): Core.PagePromise<FineTuningJobEventsPage, FineTuningJobEvent>;
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}
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export declare class FineTuningJobsPage extends CursorPage<FineTuningJob> {
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}
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export declare class FineTuningJobEventsPage extends CursorPage<FineTuningJobEvent> {
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}
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/**
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* The `fine_tuning.job` object represents a fine-tuning job that has been created
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* through the API.
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*/
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export interface FineTuningJob {
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/**
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* The object identifier, which can be referenced in the API endpoints.
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*/
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id: string;
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/**
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* The Unix timestamp (in seconds) for when the fine-tuning job was created.
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*/
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created_at: number;
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/**
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* For fine-tuning jobs that have `failed`, this will contain more information on
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* the cause of the failure.
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*/
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error: FineTuningJob.Error | null;
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/**
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* The name of the fine-tuned model that is being created. The value will be null
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* if the fine-tuning job is still running.
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*/
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fine_tuned_model: string | null;
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/**
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* The Unix timestamp (in seconds) for when the fine-tuning job was finished. The
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* value will be null if the fine-tuning job is still running.
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*/
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finished_at: number | null;
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/**
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* The hyperparameters used for the fine-tuning job. See the
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* [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning) for
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* more details.
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*/
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hyperparameters: FineTuningJob.Hyperparameters;
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/**
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* The base model that is being fine-tuned.
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*/
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model: string;
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/**
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* The object type, which is always "fine_tuning.job".
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*/
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object: 'fine_tuning.job';
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/**
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* The organization that owns the fine-tuning job.
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*/
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organization_id: string;
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/**
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* The compiled results file ID(s) for the fine-tuning job. You can retrieve the
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* results with the
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* [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
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*/
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result_files: Array<string>;
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/**
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* The seed used for the fine-tuning job.
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*/
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seed: number;
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/**
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* The current status of the fine-tuning job, which can be either
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* `validating_files`, `queued`, `running`, `succeeded`, `failed`, or `cancelled`.
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*/
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status: 'validating_files' | 'queued' | 'running' | 'succeeded' | 'failed' | 'cancelled';
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/**
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* The total number of billable tokens processed by this fine-tuning job. The value
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* will be null if the fine-tuning job is still running.
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*/
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trained_tokens: number | null;
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/**
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* The file ID used for training. You can retrieve the training data with the
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* [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
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*/
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training_file: string;
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/**
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* The file ID used for validation. You can retrieve the validation results with
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* the
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* [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
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*/
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validation_file: string | null;
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/**
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* The Unix timestamp (in seconds) for when the fine-tuning job is estimated to
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* finish. The value will be null if the fine-tuning job is not running.
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*/
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estimated_finish?: number | null;
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/**
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* A list of integrations to enable for this fine-tuning job.
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*/
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integrations?: Array<FineTuningJobWandbIntegrationObject> | null;
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}
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export declare namespace FineTuningJob {
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/**
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* For fine-tuning jobs that have `failed`, this will contain more information on
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* the cause of the failure.
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*/
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interface Error {
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/**
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* A machine-readable error code.
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*/
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code: string;
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/**
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* A human-readable error message.
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*/
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message: string;
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/**
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* The parameter that was invalid, usually `training_file` or `validation_file`.
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* This field will be null if the failure was not parameter-specific.
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*/
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param: string | null;
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}
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/**
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* The hyperparameters used for the fine-tuning job. See the
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* [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning) for
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* more details.
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*/
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interface Hyperparameters {
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/**
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* The number of epochs to train the model for. An epoch refers to one full cycle
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* through the training dataset. "auto" decides the optimal number of epochs based
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* on the size of the dataset. If setting the number manually, we support any
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* number between 1 and 50 epochs.
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*/
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n_epochs: 'auto' | number;
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}
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}
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/**
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* Fine-tuning job event object
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*/
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export interface FineTuningJobEvent {
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id: string;
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created_at: number;
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level: 'info' | 'warn' | 'error';
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message: string;
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object: 'fine_tuning.job.event';
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}
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export type FineTuningJobIntegration = FineTuningJobWandbIntegrationObject;
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/**
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* The settings for your integration with Weights and Biases. This payload
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* specifies the project that metrics will be sent to. Optionally, you can set an
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* explicit display name for your run, add tags to your run, and set a default
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* entity (team, username, etc) to be associated with your run.
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*/
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export interface FineTuningJobWandbIntegration {
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/**
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* The name of the project that the new run will be created under.
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*/
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project: string;
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/**
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* The entity to use for the run. This allows you to set the team or username of
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* the WandB user that you would like associated with the run. If not set, the
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* default entity for the registered WandB API key is used.
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*/
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entity?: string | null;
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/**
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* A display name to set for the run. If not set, we will use the Job ID as the
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* name.
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*/
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name?: string | null;
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/**
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* A list of tags to be attached to the newly created run. These tags are passed
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* through directly to WandB. Some default tags are generated by OpenAI:
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* "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".
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*/
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tags?: Array<string>;
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}
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export interface FineTuningJobWandbIntegrationObject {
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/**
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* The type of the integration being enabled for the fine-tuning job
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*/
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type: 'wandb';
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/**
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* The settings for your integration with Weights and Biases. This payload
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* specifies the project that metrics will be sent to. Optionally, you can set an
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* explicit display name for your run, add tags to your run, and set a default
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* entity (team, username, etc) to be associated with your run.
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*/
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wandb: FineTuningJobWandbIntegration;
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}
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export interface JobCreateParams {
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/**
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* The name of the model to fine-tune. You can select one of the
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* [supported models](https://platform.openai.com/docs/guides/fine-tuning/which-models-can-be-fine-tuned).
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*/
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model: (string & {}) | 'babbage-002' | 'davinci-002' | 'gpt-3.5-turbo' | 'gpt-4o-mini';
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/**
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* The ID of an uploaded file that contains training data.
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*
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* See [upload file](https://platform.openai.com/docs/api-reference/files/create)
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* for how to upload a file.
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*
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* Your dataset must be formatted as a JSONL file. Additionally, you must upload
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* your file with the purpose `fine-tune`.
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*
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* The contents of the file should differ depending on if the model uses the
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* [chat](https://platform.openai.com/docs/api-reference/fine-tuning/chat-input) or
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* [completions](https://platform.openai.com/docs/api-reference/fine-tuning/completions-input)
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* format.
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*
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* See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning)
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* for more details.
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*/
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training_file: string;
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/**
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* The hyperparameters used for the fine-tuning job.
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*/
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hyperparameters?: JobCreateParams.Hyperparameters;
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/**
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* A list of integrations to enable for your fine-tuning job.
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*/
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integrations?: Array<JobCreateParams.Integration> | null;
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/**
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* The seed controls the reproducibility of the job. Passing in the same seed and
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* job parameters should produce the same results, but may differ in rare cases. If
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* a seed is not specified, one will be generated for you.
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*/
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seed?: number | null;
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/**
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* A string of up to 64 characters that will be added to your fine-tuned model
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* name.
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*
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* For example, a `suffix` of "custom-model-name" would produce a model name like
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* `ft:gpt-4o-mini:openai:custom-model-name:7p4lURel`.
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*/
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suffix?: string | null;
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/**
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* The ID of an uploaded file that contains validation data.
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*
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* If you provide this file, the data is used to generate validation metrics
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* periodically during fine-tuning. These metrics can be viewed in the fine-tuning
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* results file. The same data should not be present in both train and validation
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* files.
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*
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* Your dataset must be formatted as a JSONL file. You must upload your file with
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* the purpose `fine-tune`.
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*
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* See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning)
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* for more details.
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*/
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validation_file?: string | null;
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}
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export declare namespace JobCreateParams {
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/**
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* The hyperparameters used for the fine-tuning job.
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*/
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interface Hyperparameters {
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/**
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* Number of examples in each batch. A larger batch size means that model
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* parameters are updated less frequently, but with lower variance.
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*/
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batch_size?: 'auto' | number;
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/**
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* Scaling factor for the learning rate. A smaller learning rate may be useful to
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* avoid overfitting.
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*/
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learning_rate_multiplier?: 'auto' | number;
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/**
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* The number of epochs to train the model for. An epoch refers to one full cycle
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* through the training dataset.
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*/
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n_epochs?: 'auto' | number;
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}
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interface Integration {
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/**
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* The type of integration to enable. Currently, only "wandb" (Weights and Biases)
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* is supported.
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*/
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type: 'wandb';
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/**
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* The settings for your integration with Weights and Biases. This payload
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* specifies the project that metrics will be sent to. Optionally, you can set an
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* explicit display name for your run, add tags to your run, and set a default
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* entity (team, username, etc) to be associated with your run.
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*/
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wandb: Integration.Wandb;
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}
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namespace Integration {
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/**
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* The settings for your integration with Weights and Biases. This payload
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* specifies the project that metrics will be sent to. Optionally, you can set an
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* explicit display name for your run, add tags to your run, and set a default
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* entity (team, username, etc) to be associated with your run.
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*/
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interface Wandb {
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/**
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* The name of the project that the new run will be created under.
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*/
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project: string;
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/**
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* The entity to use for the run. This allows you to set the team or username of
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* the WandB user that you would like associated with the run. If not set, the
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* default entity for the registered WandB API key is used.
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*/
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entity?: string | null;
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/**
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* A display name to set for the run. If not set, we will use the Job ID as the
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* name.
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*/
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name?: string | null;
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/**
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* A list of tags to be attached to the newly created run. These tags are passed
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* through directly to WandB. Some default tags are generated by OpenAI:
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* "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".
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*/
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tags?: Array<string>;
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}
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}
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}
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export interface JobListParams extends CursorPageParams {
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}
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export interface JobListEventsParams extends CursorPageParams {
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}
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export declare namespace Jobs {
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export import FineTuningJob = JobsAPI.FineTuningJob;
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export import FineTuningJobEvent = JobsAPI.FineTuningJobEvent;
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export import FineTuningJobIntegration = JobsAPI.FineTuningJobIntegration;
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export import FineTuningJobWandbIntegration = JobsAPI.FineTuningJobWandbIntegration;
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export import FineTuningJobWandbIntegrationObject = JobsAPI.FineTuningJobWandbIntegrationObject;
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export import FineTuningJobsPage = JobsAPI.FineTuningJobsPage;
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export import FineTuningJobEventsPage = JobsAPI.FineTuningJobEventsPage;
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export import JobCreateParams = JobsAPI.JobCreateParams;
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export import JobListParams = JobsAPI.JobListParams;
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export import JobListEventsParams = JobsAPI.JobListEventsParams;
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export import Checkpoints = CheckpointsAPI.Checkpoints;
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export import FineTuningJobCheckpoint = CheckpointsAPI.FineTuningJobCheckpoint;
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export import FineTuningJobCheckpointsPage = CheckpointsAPI.FineTuningJobCheckpointsPage;
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export import CheckpointListParams = CheckpointsAPI.CheckpointListParams;
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}
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//# sourceMappingURL=jobs.d.ts.map
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