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Create fine-tuning job

client.fineTuning.jobs.create(JobCreateParams { model, training_file, hyperparameters, 6 more } body, RequestOptionsoptions?): FineTuningJob { id, created_at, error, 16 more }
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
body: JobCreateParams { model, training_file, hyperparameters, 6 more }
model: (string & {}) | "babbage-002" | "davinci-002" | "gpt-3.5-turbo" | "gpt-4o-mini"

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

training_file: string

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.

hyperparameters?: Hyperparameters

Deprecated.

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.

integrations?: Array<Integration> | null

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

metadata?: Metadata | null

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.

method?: Method

The method used for fine-tuning.

seed?: number | null

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
suffix?: string | null

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
validation_file?: 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 for more details.

ReturnsExpand Collapse
FineTuningJob { id, created_at, error, 16 more }

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

id: string

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

created_at: number

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

formatunixtime
error: Error | null

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

fine_tuned_model: string | 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.

finished_at: number | 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.

formatunixtime
hyperparameters: Hyperparameters { batch_size, learning_rate_multiplier, n_epochs }

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

model: string

The base model that is being fine-tuned.

object: "fine_tuning.job"

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

organization_id: string

The organization that owns the fine-tuning job.

result_files: Array<string>

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

seed: number

The seed used for the fine-tuning job.

status: "validating_files" | "queued" | "running" | 3 more

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

trained_tokens: number | null

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.

training_file: string

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

validation_file: string | null

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

estimated_finish?: number | null

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
integrations?: Array<FineTuningJobWandbIntegrationObject { type, wandb } > | null

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

metadata?: Metadata | null

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.

method?: Method { type, dpo, reinforcement, supervised }

The method used for fine-tuning.

Create fine-tuning job

import OpenAI from "openai";

const openai = new OpenAI();

async function main() {
  const fineTune = await openai.fineTuning.jobs.create({
    training_file: "file-abc123"
  });

  console.log(fineTune);
}

main();

Create fine-tuning job

import OpenAI from "openai";
import { SupervisedMethod, SupervisedHyperparameters } from "openai/resources/fine-tuning/methods";

const openai = new OpenAI();

async function main() {
  const fineTune = await openai.fineTuning.jobs.create({
    training_file: "file-abc123",
    model: "gpt-4o-mini",
    method: {
      type: "supervised",
      supervised: {
        hyperparameters: {
          n_epochs: 2
        }
      }
    }
  });

  console.log(fineTune);
}

main();

Create fine-tuning job

import OpenAI from "openai";

const openai = new OpenAI();

async function main() {
  const fineTune = await openai.fineTuning.jobs.create({
    training_file: "file-abc123",
    validation_file: "file-abc123"
  });

  console.log(fineTune);
}

main();
Returns Examples
{
  "id": "id",
  "created_at": 0,
  "error": {
    "code": "code",
    "message": "message",
    "param": "param"
  },
  "fine_tuned_model": "fine_tuned_model",
  "finished_at": 0,
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": "auto"
  },
  "model": "model",
  "object": "fine_tuning.job",
  "organization_id": "organization_id",
  "result_files": [
    "file-abc123"
  ],
  "seed": 0,
  "status": "validating_files",
  "trained_tokens": 0,
  "training_file": "training_file",
  "validation_file": "validation_file",
  "estimated_finish": 0,
  "integrations": [
    {
      "type": "wandb",
      "wandb": {
        "project": "my-wandb-project",
        "entity": "entity",
        "name": "name",
        "tags": [
          "custom-tag"
        ]
      }
    }
  ],
  "metadata": {
    "foo": "string"
  },
  "method": {
    "type": "supervised",
    "dpo": {
      "hyperparameters": {
        "batch_size": "auto",
        "beta": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    },
    "reinforcement": {
      "grader": {
        "input": "input",
        "name": "name",
        "operation": "eq",
        "reference": "reference",
        "type": "string_check"
      },
      "hyperparameters": {
        "batch_size": "auto",
        "compute_multiplier": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "reasoning_effort": "default"
      }
    },
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  }
}