Struct CreateFineTuningJobRequest

pub struct CreateFineTuningJobRequest {
    pub model: String,
    pub training_file: String,
    pub hyperparameters: Option<CreateFineTuningJobRequestHyperparameters>,
    pub suffix: Option<String>,
    pub validation_file: Option<String>,
    pub integrations: Option<Vec<CreateFineTuningJobRequestIntegration>>,
    pub seed: Option<i64>,
    pub method: Option<FineTuneMethod>,
    pub metadata: Option<Metadata>,
}

Fields

model: String

The name of the model to fine-tune. You can select one of the supported models.

training_file: String

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. Additionally, you must upload your file with the purpose fine-tune.

The contents of the file should differ depending on if the model uses the chat, completions format, or if the fine-tuning method uses the preference format.

See the fine-tuning guide for more details.

hyperparameters: Option<CreateFineTuningJobRequestHyperparameters>

The hyperparameters used for the fine-tuning job. This value is now deprecated in favor of method, and should be passed in under the method parameter.

suffix: Option<String>

A string of up to 64 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 ft:gpt-4o-mini:openai:custom-model-name:7p4lURel.

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. The same data should not be present in both train and validation files.

Your dataset must be formatted as a JSONL file. You must upload your file with the purpose fine-tune.

See the fine-tuning guide for more details.

integrations: Option<Vec<CreateFineTuningJobRequestIntegration>>

A list of integrations to enable for your fine-tuning job.

seed: Option<i64>

The seed controls the reproducibility of the job. Passing in the same seed and job parameters should produce the same results, but may differ in rare cases. If a seed is not specified, one will be generated for you.

method: Option<FineTuneMethod>

metadata: Option<Metadata>

Implementations

impl CreateFineTuningJobRequest

pub fn builder() -> CreateFineTuningJobRequestBuilder<((), (), (), (), (), (), (), (), ())>

Create a builder for building CreateFineTuningJobRequest. On the builder, call .model(...), .training_file(...), .hyperparameters(...)(optional), .suffix(...)(optional), .validation_file(...)(optional), .integrations(...)(optional), .seed(...)(optional), .method(...)(optional), .metadata(...)(optional) to set the values of the fields. Finally, call .build() to create the instance of CreateFineTuningJobRequest.

Trait Implementations

impl Clone for CreateFineTuningJobRequest

fn clone(&self) -> CreateFineTuningJobRequest

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateFineTuningJobRequest

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for CreateFineTuningJobRequest

fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>

where D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for CreateFineTuningJobRequest

fn eq(&self, other: &CreateFineTuningJobRequest) -> bool

Tests for self and other values to be equal, and is used by ==.

const fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for CreateFineTuningJobRequest

fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>

where S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for CreateFineTuningJobRequest