API Reference

Fine Tuning

Jobs

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Retrieve fine-tuning job

client.fineTuning.jobs.retrieve(stringfineTuningJobID, RequestOptionsoptions?): FineTuningJob { id, created_at, error, 16 more }

GET/fine_tuning/jobs/{fine_tuning_job_id}

Get info about a fine-tuning job.

Learn more about fine-tuning

ParametersExpand Collapse

fineTuningJobID: string

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.

Retrieve fine-tuning job

TypeScript

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Python

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Ruby

Java

CLI Tool

import OpenAI from "openai";

const openai = new OpenAI();

async function main() {
  const fineTune = await openai.fineTuning.jobs.retrieve("ftjob-abc123");

  console.log(fineTune);
}

main();

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "davinci-002",
  "created_at": 1692661014,
  "finished_at": 1692661190,
  "fine_tuned_model": "ft:davinci-002:my-org:custom_suffix:7q8mpxmy",
  "organization_id": "org-123",
  "result_files": [
      "file-abc123"
  ],
  "status": "succeeded",
  "validation_file": null,
  "training_file": "file-abc123",
  "hyperparameters": {
      "n_epochs": 4,
      "batch_size": 1,
      "learning_rate_multiplier": 1.0
  },
  "trained_tokens": 5768,
  "integrations": [],
  "seed": 0,
  "estimated_finish": 0,
  "method": {
    "type": "supervised",
    "supervised": {
      "hyperparameters": {
        "n_epochs": 4,
        "batch_size": 1,
        "learning_rate_multiplier": 1.0
      }
    }
  }
}

Returns Examples

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "davinci-002",
  "created_at": 1692661014,
  "finished_at": 1692661190,
  "fine_tuned_model": "ft:davinci-002:my-org:custom_suffix:7q8mpxmy",
  "organization_id": "org-123",
  "result_files": [
      "file-abc123"
  ],
  "status": "succeeded",
  "validation_file": null,
  "training_file": "file-abc123",
  "hyperparameters": {
      "n_epochs": 4,
      "batch_size": 1,
      "learning_rate_multiplier": 1.0
  },
  "trained_tokens": 5768,
  "integrations": [],
  "seed": 0,
  "estimated_finish": 0,
  "method": {
    "type": "supervised",
    "supervised": {
      "hyperparameters": {
        "n_epochs": 4,
        "batch_size": 1,
        "learning_rate_multiplier": 1.0
      }
    }
  }
}