API Reference

Fine Tuning

Jobs

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Create fine-tuning job

FineTuningJob fineTuning().jobs().create(JobCreateParamsparams, RequestOptionsrequestOptions = RequestOptions.none())

POST/fine_tuning/jobs

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

ParametersExpand Collapse

JobCreateParams params

Model model

The name of the model to fine-tune. You can select one of the supported models.

String trainingFile

The ID of an uploaded file that contains training data.

See upload file 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.

The contents of the file should differ depending on if the model uses the chat, completions format, or if the fine-tuning method uses the preference format.

See the fine-tuning guide for more details.

DeprecatedOptional<Hyperparameters> hyperparameters

The hyperparameters used for the fine-tuning job. This value is now deprecated in favor of method, and should be passed in under the method parameter.

Optional<List<Integration>> integrations

A list of integrations to enable for your fine-tuning job.

Optional<Metadata> metadata

Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

Optional<Method> method

The method used for fine-tuning.

Optional<Long> seed

The seed controls the reproducibility of the job. Passing in the same seed and job parameters should produce the same results, but may differ in rare cases. If a seed is not specified, one will be generated for you.

minimum0

maximum2147483647

Optional<String> suffix

A string of up to 64 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-4o-mini:openai:custom-model-name:7p4lURel.

minLength1

maxLength64

Optional<String> validationFile

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 for more details.

ReturnsExpand Collapse

class FineTuningJob:

The fine_tuning.job object represents a fine-tuning job that has been created through the API.

String id

The object identifier, which can be referenced in the API endpoints.

long createdAt

The Unix timestamp (in seconds) for when the fine-tuning job was created.

formatunixtime

Optional<Error> error

For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

Optional<String> fineTunedModel

The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

Optional<Long> finishedAt

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.

formatunixtime

Hyperparameters hyperparameters

The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

String model

The base model that is being fine-tuned.

JsonValue; object_ "fine_tuning.job"constant"fine_tuning.job"constant

The object type, which is always “fine_tuning.job”.

String organizationId

The organization that owns the fine-tuning job.

List<String> resultFiles

The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

long seed

The seed used for the fine-tuning job.

Status status

The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

Optional<Long> trainedTokens

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.

String trainingFile

The file ID used for training. You can retrieve the training data with the Files API.

Optional<String> validationFile

The file ID used for validation. You can retrieve the validation results with the Files API.

Optional<Long> estimatedFinish

The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

formatunixtime

Optional<List<FineTuningJobWandbIntegrationObject>> integrations

A list of integrations to enable for this fine-tuning job.

Optional<Metadata> metadata

Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

Optional<Method> method

The method used for fine-tuning.

Create fine-tuning job

Java

HTTP

Python

TypeScript

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Java

CLI Tool

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.finetuning.jobs.FineTuningJob;
import com.openai.models.finetuning.jobs.JobCreateParams;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        JobCreateParams params = JobCreateParams.builder()
            .model(JobCreateParams.Model.GPT_4O_MINI)
            .trainingFile("file-abc123")
            .build();
        FineTuningJob fineTuningJob = client.fineTuning().jobs().create(params);
    }
}

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "gpt-4o-mini-2024-07-18",
  "created_at": 1721764800,
  "fine_tuned_model": null,
  "organization_id": "org-123",
  "result_files": [],
  "status": "queued",
  "validation_file": null,
  "training_file": "file-abc123",
  "method": {
    "type": "supervised",
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
      }
    }
  },
  "metadata": null
}

Returns Examples

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "gpt-4o-mini-2024-07-18",
  "created_at": 1721764800,
  "fine_tuned_model": null,
  "organization_id": "org-123",
  "result_files": [],
  "status": "queued",
  "validation_file": null,
  "training_file": "file-abc123",
  "method": {
    "type": "supervised",
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
      }
    }
  },
  "metadata": null
}