API Reference

Fine Tuning

Jobs

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Retrieve fine-tuning job

fine_tuning.jobs.retrieve(strfine_tuning_job_id) -> FineTuningJob

GET/fine_tuning/jobs/{fine_tuning_job_id}

Get info about a fine-tuning job.

Learn more about fine-tuning

ParametersExpand Collapse

fine_tuning_job_id: str

ReturnsExpand Collapse

class FineTuningJob: …

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

id: str

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

created_at: int

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

formatunixtime

error: Optional[Error]

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

fine_tuned_model: Optional[str]

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: Optional[int]

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.

model: str

The base model that is being fine-tuned.

object: Literal["fine_tuning.job"]

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

organization_id: str

The organization that owns the fine-tuning job.

result_files: List[str]

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

seed: int

The seed used for the fine-tuning job.

status: Literal["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: Optional[int]

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: str

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

validation_file: Optional[str]

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

estimated_finish: Optional[int]

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: Optional[List[FineTuningJobWandbIntegrationObject]]

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

metadata: Optional[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.

method: Optional[Method]

The method used for fine-tuning.

Retrieve fine-tuning job

Python

HTTP

Python

TypeScript

Go

Ruby

Java

CLI Tool

from openai import OpenAI
client = OpenAI()

client.fine_tuning.jobs.retrieve("ftjob-abc123")

{
  "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
      }
    }
  }
}