FineTuneRequest constructor

const FineTuneRequest({ @JsonKey(name: 'training_file') required String trainingFile, @JsonKey(name: 'validation_file') String? validationFile, String? model, @JsonKey(name: 'n_epochs') int? nEpochs, @JsonKey(name: 'batch_size') int? batchSize, @JsonKey(name: 'learning_rate_multiplier') double? learningRateMultiplier, @JsonKey(name: 'prompt_loss_weight') double? promptLossWeight, @JsonKey(name: 'compute_classification_metrics') bool? computeClassificationMetrics, @JsonKey(name: 'classification_n_classes') int? classificationNClasses, @JsonKey(name: 'classification_positive_class') String? classificationPositiveClass, @JsonKey(name: 'classification_betas') List<double>? classificationBetas, String? suffix, })

Implementation

const factory FineTuneRequest({
  /// The ID of an uploaded file that contains training data.
  /// See [upload file](https://beta.openai.com/docs/api-reference/files/upload)
  /// for how to upload a file.
  ///
  /// Your dataset must be formatted as a JSONL file, where each training example
  /// is a JSON object with the keys "prompt" and "completion". Additionally,
  /// you must upload your file with the purpose fine-tune.
  @JsonKey(name: 'training_file') required final String trainingFile,

  /// The ID of an uploaded file that contains validation data.
  ///
  /// Your dataset must be formatted as a JSONL file, where each training example
  /// is a JSON object with the keys "prompt" and "completion". Additionally,
  /// you must upload your file with the purpose fine-tune.
  @JsonKey(name: 'validation_file') final String? validationFile,

  /// The name of the base model to fine-tune. You can select one of "ada",
  /// "babbage", "curie", "davinci", or a fine-tuned model created after
  /// 2022-04-21.
  final String? model,

  /// The number of epochs to train the model for. An epoch refers to one
  /// full cycle through the training dataset.
  @JsonKey(name: 'n_epochs') final int? nEpochs,

  /// The batch size to use for training. The batch size is the number of
  /// training examples used to train a single forward and backward pass.
  ///
  /// By default, the batch size will be dynamically configured to be ~0.2%
  /// of the number of examples in the training set, capped at 256 - in general,
  /// we've found that larger batch sizes tend to work better for larger
  /// datasets.
  @JsonKey(name: 'batch_size') final int? batchSize,

  /// The learning rate multiplier to use for training. The fine-tuning
  /// learning rate is the original learning rate used for pretraining
  /// multiplied by this value.
  ///
  /// By default, the learning rate multiplier is the 0.05, 0.1, or 0.2
  /// depending on final batch_size (larger learning rates tend to perform
  /// better with larger batch sizes). We recommend experimenting with values
  /// in the range 0.02 to 0.2 to see what produces the best results.
  @JsonKey(name: 'learning_rate_multiplier')
      final double? learningRateMultiplier,

  /// The weight to use for loss on the prompt tokens. This controls how much
  /// the model tries to learn to generate the prompt (as compared to the
  /// completion which always has a weight of 1.0), and can add a stabilizing
  /// effect to training when completions are short.
  ///
  /// If prompts are extremely long (relative to completions), it may make
  /// sense to reduce this weight so as to avoid over-prioritizing learning
  /// the prompt.
  @JsonKey(name: 'prompt_loss_weight') final double? promptLossWeight,

  /// If set, we calculate classification-specific metrics such as accuracy
  /// and F-1 score using the validation set at the end of every epoch.
  ///
  /// In order to compute classification metrics, you must provide a
  /// `validation_file`. Additionally, you must specify `classification_n_classes`
  /// for multiclass classification or `classification_positive_class` for binary
  /// classification.
  @JsonKey(name: 'compute_classification_metrics')
      final bool? computeClassificationMetrics,

  /// The number of classes in a classification task.
  @JsonKey(name: 'classification_n_classes')
      final int? classificationNClasses,

  /// The positive class in binary classification.
  @JsonKey(name: 'classification_positive_class')
      final String? classificationPositiveClass,

  /// If this is provided, we calculate F-beta scores at the specified beta
  /// values. The F-beta score is a generalization of F-1 score. This is
  /// only used for binary classification.
  @JsonKey(name: 'classification_betas')
      final List<double>? classificationBetas,

  /// A string of up to 40 characters that will be added to your fine-tuned
  /// model name.
  final String? suffix,
}) = _FineTuneRequest;