Struct FineTuningRequest

pub struct FineTuningRequest {
    pub training_file: String,
    pub validation_file: Option<String>,
    pub model: Option<String>,
    pub n_epochs: Option<u32>,
    pub batch_size: Option<u32>,
    pub learning_rate_multiplier: Option<f64>,
    pub prompt_loss_weight: Option<f64>,
    pub compute_classification_metrics: Option<bool>,
    pub classification_n_classes: Option<u32>,
    pub classification_positive_class: Option<String>,
    pub classification_betas: Option<Vec<f64>>,
    pub suffix: Option<String>,
}

Fields

training_file: String

The ID of an uploaded file that contains training 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.

validation_file: Option<String>

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. 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.

model: Option<String>

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.

n_epochs: Option<u32>

The number of epochs to train the model for.

batch_size: Option<u32>

The batch size to use for training.

learning_rate_multiplier: Option<f64>

The learning rate multiplier to use for training.

prompt_loss_weight: Option<f64>

The weight to use for loss on the prompt tokens.

compute_classification_metrics: Option<bool>

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. 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.

classification_n_classes: Option<u32>

The number of classes in a classification task. This parameter is required for multiclass classification.

classification_positive_class: Option<String>

The positive class in binary classification. This parameter is needed to generate precision, recall, and F1 metrics when doing binary classification.

classification_betas: Option<Vec<f64>>

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.

suffix: Option<String>

A string of up to 40 characters that will be added to your fine-tuned model name.

Trait Implementations

impl Debug for FineTuningRequest

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for FineTuningRequest

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl OpenAIRequest for FineTuningRequest

type Response = FineTuneResponse

fn method() -> Method

fn url() -> &'static str

impl Serialize for FineTuningRequest

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.