API Reference

Fine Tuning

Jobs

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Retrieve fine-tuning job

client.FineTuning.Jobs.Get(ctx, fineTuningJobID) (*FineTuningJob, error)

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

type FineTuningJob struct{…}

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.

CreatedAt int64

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

formatunixtime

Error FineTuningJobError

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

FineTunedModel string

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

FinishedAt int64

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 FineTuningJobHyperparameters

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 FineTuningJob

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

OrganizationID string

The organization that owns the fine-tuning job.

ResultFiles []string

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

Seed int64

The seed used for the fine-tuning job.

Status FineTuningJobStatus

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

TrainedTokens int64

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.

TrainingFile string

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

ValidationFile string

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

EstimatedFinish int64Optional

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 []FineTuningJobWandbIntegrationObjectOptional

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

Metadata MetadataOptional

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 FineTuningJobMethodOptional

The method used for fine-tuning.

Retrieve fine-tuning job

Go

HTTP

Python

TypeScript

Go

Ruby

Java

CLI Tool

package main

import (
  "context"
  "fmt"

  "github.com/openai/openai-go"
  "github.com/openai/openai-go/option"
)

func main() {
  client := openai.NewClient(
    option.WithAPIKey("My API Key"),
  )
  fineTuningJob, err := client.FineTuning.Jobs.Get(context.TODO(), "ft-AF1WoRqd3aJAHsqc9NY7iL8F")
  if err != nil {
    panic(err.Error())
  }
  fmt.Printf("%+v\n", fineTuningJob.ID)
}

{
  "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
      }
    }
  }
}