async def create(
        self,
        *,
        model: Union[str, Literal["babbage-002", "davinci-002", "gpt-3.5-turbo", "gpt-4o-mini"]],
        training_file: str,
        hyperparameters: job_create_params.Hyperparameters | NotGiven = NOT_GIVEN,
        integrations: Optional[Iterable[job_create_params.Integration]] | NotGiven = NOT_GIVEN,
        metadata: Optional[Metadata] | NotGiven = NOT_GIVEN,
        method: job_create_params.Method | NotGiven = NOT_GIVEN,
        seed: Optional[int] | NotGiven = NOT_GIVEN,
        suffix: Optional[str] | NotGiven = NOT_GIVEN,
        validation_file: Optional[str] | NotGiven = NOT_GIVEN,
        # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
        # The extra values given here take precedence over values defined on the client or passed to this method.
        extra_headers: Headers | None = None,
        extra_query: Query | None = None,
        extra_body: Body | None = None,
        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    ) -> FineTuningJob:
        """
        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](https://platform.openai.com/docs/guides/fine-tuning)

        Args:
          model: The name of the model to fine-tune. You can select one of the
              [supported models](https://platform.openai.com/docs/guides/fine-tuning#which-models-can-be-fine-tuned).

          training_file: The ID of an uploaded file that contains training data.

              See [upload file](https://platform.openai.com/docs/api-reference/files/create)
              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](https://platform.openai.com/docs/api-reference/fine-tuning/chat-input),
              [completions](https://platform.openai.com/docs/api-reference/fine-tuning/completions-input)
              format, or if the fine-tuning method uses the
              [preference](https://platform.openai.com/docs/api-reference/fine-tuning/preference-input)
              format.

              See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning)
              for more details.

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

          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: The method used for fine-tuning.

          seed: 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.

          suffix: 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`.

          validation_file: 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](https://platform.openai.com/docs/guides/fine-tuning)
              for more details.

          extra_headers: Send extra headers

          extra_query: Add additional query parameters to the request

          extra_body: Add additional JSON properties to the request

          timeout: Override the client-level default timeout for this request, in seconds
        """
        return await self._post(
            "/fine_tuning/jobs",
            body=await async_maybe_transform(
                {
                    "model": model,
                    "training_file": training_file,
                    "hyperparameters": hyperparameters,
                    "integrations": integrations,
                    "metadata": metadata,
                    "method": method,
                    "seed": seed,
                    "suffix": suffix,
                    "validation_file": validation_file,
                },
                job_create_params.JobCreateParams,
            ),
            options=make_request_options(
                extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
            ),
            cast_to=FineTuningJob,
        )

        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](https://platform.openai.com/docs/guides/fine-tuning)

        Args:
          model: The name of the model to fine-tune. You can select one of the
              [supported models](https://platform.openai.com/docs/guides/fine-tuning#which-models-can-be-fine-tuned).

          training_file: The ID of an uploaded file that contains training data.

              See [upload file](https://platform.openai.com/docs/api-reference/files/create)
              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](https://platform.openai.com/docs/api-reference/fine-tuning/chat-input),
              [completions](https://platform.openai.com/docs/api-reference/fine-tuning/completions-input)
              format, or if the fine-tuning method uses the
              [preference](https://platform.openai.com/docs/api-reference/fine-tuning/preference-input)
              format.

              See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning)
              for more details.

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

          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: The method used for fine-tuning.

          seed: 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.

          suffix: 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`.

          validation_file: 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](https://platform.openai.com/docs/guides/fine-tuning)
              for more details.

          extra_headers: Send extra headers

          extra_query: Add additional query parameters to the request

          extra_body: Add additional JSON properties to the request

          timeout: Override the client-level default timeout for this request, in seconds
        
src/openai/resources/fine_tuning/jobs/jobs.py