Struct async_openai::types::CreateFineTuneRequestArgs

pub struct CreateFineTuneRequestArgs { /* private fields */ }

Builder for CreateFineTuneRequest.

Implementations

impl CreateFineTuneRequestArgs

pub fn training_file<VALUE: Into<String>>(&mut self, value: VALUE) -> &mut Self

The ID of an uploaded file that contains training data.

See upload file 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 for more details.

pub fn validation_file<VALUE: Into<String>>( &mut self, value: VALUE ) -> &mut Self

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.

See the fine-tuning guide for more details.

pub fn model<VALUE: Into<String>>(&mut self, value: VALUE) -> &mut Self

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. To learn more about these models, see the Models documentation.

pub fn n_epochs<VALUE: Into<u32>>(&mut self, value: VALUE) -> &mut Self

The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

pub fn batch_size<VALUE: Into<u32>>(&mut self, value: VALUE) -> &mut Self

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.

pub fn learning_rate_multiplier<VALUE: Into<f32>>( &mut self, value: VALUE ) -> &mut Self

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.

pub fn prompt_loss_weight<VALUE: Into<f32>>( &mut self, value: VALUE ) -> &mut Self

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.

pub fn compute_classification_metrics<VALUE: Into<bool>>( &mut self, value: VALUE ) -> &mut Self

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.

pub fn classification_n_classes<VALUE: Into<u32>>( &mut self, value: VALUE ) -> &mut Self

The number of classes in a classification task.

This parameter is required for multiclass classification.

pub fn classification_positive_class<VALUE: Into<String>>( &mut self, value: VALUE ) -> &mut Self

The positive class in binary classification.

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

pub fn classification_betas<VALUE: Into<Vec<f32>>>( &mut self, value: VALUE ) -> &mut Self

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.

pub fn suffix<VALUE: Into<String>>(&mut self, value: VALUE) -> &mut Self

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.

pub fn build(&self) -> Result<CreateFineTuneRequest, OpenAIError>

Builds a new CreateFineTuneRequest.

Errors

If a required field has not been initialized.

Trait Implementations

impl Clone for CreateFineTuneRequestArgs

fn clone(&self) -> CreateFineTuneRequestArgs

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateFineTuneRequestArgs

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

Formats the value using the given formatter.

impl Default for CreateFineTuneRequestArgs

fn default() -> Self

Returns the “default value” for a type.