export type CreateFineTuneRequest = {
    /**
     * 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, 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`.
     *
     * See the [fine-tuning guide](/docs/guides/legacy-fine-tuning/creating-training-data) for more details.
     *
     */
    training_file: string;
    /**
     * 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.
     *
     */
    batch_size?: number | null;
    /**
     * 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.
     *
     * With a beta of 1 (i.e. the F-1 score), precision and recall are
     * given the same weight. A larger beta score puts more weight on
     * recall and less on precision. A smaller beta score puts more weight
     * on precision and less on recall.
     *
     */
    classification_betas?: Array<number> | null;
    /**
     * The number of classes in a classification task.
     *
     * This parameter is required for multiclass classification.
     *
     */
    classification_n_classes?: number | null;
    /**
     * The positive class in binary classification.
     *
     * This parameter is needed to generate precision, recall, and F1
     * metrics when doing binary classification.
     *
     */
    classification_positive_class?: string | null;
    /**
     * If set, we calculate classification-specific metrics such as accuracy
     * and F-1 score using the validation set at the end of every epoch.
     * These metrics can be viewed in the [results file](/docs/guides/legacy-fine-tuning/analyzing-your-fine-tuned-model).
     *
     * 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.
     *
     */
    compute_classification_metrics?: boolean | null;
    /**
     * The hyperparameters used for the fine-tuning job.
     */
    hyperparameters?: {
        /**
         * The number of epochs to train the model for. An epoch refers to one
         * full cycle through the training dataset.
         *
         */
        n_epochs?: "auto" | number;
    };
    /**
     * 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.
     *
     */
    learning_rate_multiplier?: number | null;
    /**
     * 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 and before 2023-08-22.
     * To learn more about these models, see the
     * [Models](/docs/models) documentation.
     *
     */
    model?: (string | "ada" | "babbage" | "curie" | "davinci") | null;
    /**
     * 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.
     *
     */
    prompt_loss_weight?: number | null;
    /**
     * A string of up to 40 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 `ada:ft-your-org:custom-model-name-2022-02-15-04-21-04`.
     *
     */
    suffix?: string | null;
    /**
     * 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](/docs/guides/legacy-fine-tuning/analyzing-your-fine-tuned-model).
     * Your train and validation data should be mutually exclusive.
     *
     * Your dataset must be formatted as a JSONL file, where each validation
     * example is a JSON object with the keys "prompt" and "completion".
     * Additionally, you must upload your file with the purpose `fine-tune`.
     *
     * See the [fine-tuning guide](/docs/guides/legacy-fine-tuning/creating-training-data) for more details.
     *
     */
    validation_file?: string | null;
};