Package com.google.genai.types

Class CreateTuningJobConfig

java.lang.Object
com.google.genai.JsonSerializable
com.google.genai.types.CreateTuningJobConfig
public abstract class CreateTuningJobConfig extends JsonSerializable
Fine-tuning job creation request - optional fields.

  • Constructor Details

    • CreateTuningJobConfig

      public CreateTuningJobConfig()

  • Method Details

    • httpOptions

      public abstract Optional<HttpOptions> httpOptions()
      Used to override HTTP request options.

    • method

      public abstract Optional<TuningMethod> method()
      The method to use for tuning (SUPERVISED_FINE_TUNING or PREFERENCE_TUNING). If not set, the default method (SFT) will be used.

    • validationDataset

      public abstract Optional<TuningValidationDataset> validationDataset()
      Validation dataset for tuning. The dataset must be formatted as a JSONL file.

    • tunedModelDisplayName

      public abstract Optional<String> tunedModelDisplayName()
      The display name of the tuned Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    • description

      public abstract Optional<String> description()
      The description of the TuningJob

    • epochCount

      public abstract Optional<Integer> epochCount()
      Number of complete passes the model makes over the entire training dataset during training.

    • learningRateMultiplier

      public abstract Optional<Float> learningRateMultiplier()
      Multiplier for adjusting the default learning rate.

    • exportLastCheckpointOnly

      public abstract Optional<Boolean> exportLastCheckpointOnly()
      If set to true, disable intermediate checkpoints and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints.

    • preTunedModelCheckpointId

      public abstract Optional<String> preTunedModelCheckpointId()
      The optional checkpoint id of the pre-tuned model to use for tuning, if applicable.

    • adapterSize

      public abstract Optional<AdapterSize> adapterSize()
      Adapter size for tuning.

    • batchSize

      public abstract Optional<Integer> batchSize()
      The batch size hyperparameter for tuning. If not set, a default of 4 or 16 will be used based on the number of training examples.

    • learningRate

      public abstract Optional<Float> learningRate()
      The learning rate hyperparameter for tuning. If not set, a default of 0.001 or 0.0002 will be calculated based on the number of training examples.

    • evaluationConfig

      public abstract Optional<EvaluationConfig> evaluationConfig()
      Evaluation config for the tuning job.

    • labels

      public abstract Optional<Map<String,String>> labels()
      Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    • beta

      public abstract Optional<Float> beta()
      Weight for KL Divergence regularization, Preference Optimization tuning only.

    • builder

      public static CreateTuningJobConfig.Builder builder()
      Instantiates a builder for CreateTuningJobConfig.

    • toBuilder

      public abstract CreateTuningJobConfig.Builder toBuilder()
      Creates a builder with the same values as this instance.

    • fromJson

      public static CreateTuningJobConfig fromJson(String jsonString)
      Deserializes a JSON string to a CreateTuningJobConfig object.