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Fine Tuning
Jobs

Create fine-tuning job

fine_tuning.jobs.create(JobCreateParams**kwargs) -> FineTuningJob
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
model: Union[str, Literal["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: str

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.

Deprecatedhyperparameters: Optional[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.

integrations: Optional[Iterable[Integration]]

A list of integrations to enable for your 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.

seed: Optional[int]

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: Optional[str]

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: Optional[str]

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.

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.

Create fine-tuning job

from openai import OpenAI
from openai.types.fine_tuning import DpoMethod, DpoHyperparameters

client = OpenAI()

client.fine_tuning.jobs.create(
  training_file="file-abc",
  validation_file="file-123",
  model="gpt-4o-mini",
  method={
    "type": "dpo",
    "dpo": DpoMethod(
      hyperparameters=DpoHyperparameters(beta=0.1)
    )
  }
)

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc",
  "model": "gpt-4o-mini",
  "created_at": 1746130590,
  "fine_tuned_model": null,
  "organization_id": "org-abc",
  "result_files": [],
  "status": "queued",
  "validation_file": "file-123",
  "training_file": "file-abc",
  "method": {
    "type": "dpo",
    "dpo": {
      "hyperparameters": {
        "beta": 0.1,
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  },
  "metadata": null,
  "error": {
    "code": null,
    "message": null,
    "param": null
  },
  "finished_at": null,
  "hyperparameters": null,
  "seed": 1036326793,
  "estimated_finish": null,
  "integrations": [],
  "user_provided_suffix": null,
  "usage_metrics": null,
  "shared_with_openai": false
}

Create fine-tuning job

from openai import OpenAI
client = OpenAI()

client.fine_tuning.jobs.create(
  training_file="file-abc123",
  model="gpt-4o-mini"
)

{
  "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
}

Create fine-tuning job

from openai import OpenAI
from openai.types.fine_tuning import SupervisedMethod, SupervisedHyperparameters

client = OpenAI()

client.fine_tuning.jobs.create(
  training_file="file-abc123",
  model="gpt-4o-mini",
  method={
    "type": "supervised",
    "supervised": SupervisedMethod(
      hyperparameters=SupervisedHyperparameters(
        n_epochs=2
      )
    )
  }
)

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "gpt-4o-mini",
  "created_at": 1721764800,
  "fine_tuned_model": null,
  "organization_id": "org-123",
  "result_files": [],
  "status": "queued",
  "validation_file": null,
  "training_file": "file-abc123",
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": 2
  },
  "method": {
    "type": "supervised",
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": 2
      }
    }
  },
  "metadata": null,
  "error": {
    "code": null,
    "message": null,
    "param": null
  },
  "finished_at": null,
  "seed": 683058546,
  "trained_tokens": null,
  "estimated_finish": null,
  "integrations": [],
  "user_provided_suffix": null,
  "usage_metrics": null,
  "shared_with_openai": false
}

Create fine-tuning job

from openai import OpenAI
from openai.types.fine_tuning import ReinforcementMethod, ReinforcementHyperparameters
from openai.types.graders import StringCheckGrader

client = OpenAI()

client.fine_tuning.jobs.create(
  training_file="file-abc",
  validation_file="file-123",
  model="o4-mini",
  method={
    "type": "reinforcement",
    "reinforcement": ReinforcementMethod(
      grader=StringCheckGrader(
        name="Example string check grader",
        type="string_check",
        input="{{item.label}}",
        operation="eq",
        reference="{{sample.output_text}}"
      ),
      hyperparameters=ReinforcementHyperparameters(
          reasoning_effort="medium",
      )
    )
  }, 
  seed=42,
)

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "o4-mini",
  "created_at": 1721764800,
  "finished_at": null,
  "fine_tuned_model": null,
  "organization_id": "org-123",
  "result_files": [],
  "status": "validating_files",
  "validation_file": "file-123",
  "training_file": "file-abc",
  "trained_tokens": null,
  "error": {},
  "user_provided_suffix": null,
  "seed": 950189191,
  "estimated_finish": null,
  "integrations": [],
  "method": {
    "type": "reinforcement",
    "reinforcement": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "compute_multiplier": "auto",
        "reasoning_effort": "medium"
      },
      "grader": {
        "type": "string_check",
        "name": "Example string check grader",
        "input": "{{sample.output_text}}",
        "reference": "{{item.label}}",
        "operation": "eq"
      },
      "response_format": null
    }
  },
  "metadata": null,
  "usage_metrics": null,
  "shared_with_openai": false
}

Create fine-tuning job

from openai import OpenAI
client = OpenAI()

client.fine_tuning.jobs.create(
  training_file="file-abc123",
  validation_file="file-def456",
  model="gpt-4o-mini"
)

{
  "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": "file-abc123",
  "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
}