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1.  final def !=(arg0: Any): Boolean

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2.  final def ##: Int

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3.  final def ==(arg0: Any): Boolean

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4.  final def asInstanceOf[T0]: T0

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5.  def cancelFineTuningJob(fine_tuning_job_id: String): ZIO[FineTuning, OpenAIFailure, FineTuningJob]

   Immediately cancel a fine-tune job.

6.  def clone(): AnyRef

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7.  def createFineTuningJob(model: Model, trainingFile: String, hyperparameters: Optional[Hyperparameters] = Optional.Absent, suffix: Optional[Suffix] = Optional.Absent, validationFile: Optional[String] = Optional.Absent): ZIO[FineTuning, OpenAIFailure, FineTuningJob]

   Creates a job that fine-tunes a specified model from a given dataset.

   Creates a job that fine-tunes a specified 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](/docs/guides/fine-tuning)

   model

   The name of the model to fine-tune. You can select one of the [supported models](/docs/guides/fine-tuning/what-models-can-be-fine-tuned).

   trainingFile

   The ID of an uploaded file that contains training data. See [upload file](/docs/api-reference/files/upload) 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. See the [fine-tuning guide](/docs/guides/fine-tuning) for more details.

   hyperparameters

   The hyperparameters used for the fine-tuning job.

   suffix

   A string of up to 18 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-3.5-turbo:openai:custom-model-name:7p4lURel.

   validationFile

   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](/docs/guides/fine-tuning) for more details.

8.  def createFineTuningJob(body: CreateFineTuningJobRequest): ZIO[FineTuning, OpenAIFailure, FineTuningJob]

   Creates a job that fine-tunes a specified model from a given dataset.

   Creates a job that fine-tunes a specified 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](/docs/guides/fine-tuning)

9.  def default: ZLayer[Any, Throwable, FineTuning]
10.  final def eq(arg0: AnyRef): Boolean

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11.  def equals(arg0: AnyRef): Boolean

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12.  def finalize(): Unit

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13.  final def getClass(): Class[_ <: AnyRef]

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    @native()

14.  def hashCode(): Int

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15.  final def isInstanceOf[T0]: Boolean

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16.  def listFineTuningEvents(fine_tuning_job_id: String, after: Optional[String], limit: Optional[Int]): ZIO[FineTuning, OpenAIFailure, ListFineTuningJobEventsResponse]

    Get status updates for a fine-tuning job.

17.  def listPaginatedFineTuningJobs(after: Optional[String], limit: Optional[Int]): ZIO[FineTuning, OpenAIFailure, ListPaginatedFineTuningJobsResponse]

    List your organization's fine-tuning jobs

18.  def live: ZLayer[Client, Nothing, FineTuning]
19.  final def ne(arg0: AnyRef): Boolean

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20.  final def notify(): Unit

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21.  final def notifyAll(): Unit

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    @native()

22.  def retrieveFineTuningJob(fine_tuning_job_id: String): ZIO[FineTuning, OpenAIFailure, FineTuningJob]

    Get info about a fine-tuning job.

    Get info about a fine-tuning job.

    [Learn more about fine-tuning](/docs/guides/fine-tuning)

23.  final def synchronized[T0](arg0: => T0): T0

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24.  def toString(): String

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25.  final def wait(): Unit

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    @throws(classOf[java.lang.InterruptedException])

26.  final def wait(arg0: Long, arg1: Int): Unit

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    @throws(classOf[java.lang.InterruptedException])

27.  final def wait(arg0: Long): Unit

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    @throws(classOf[java.lang.InterruptedException]) @native()