pub struct CreateFineTuningJobRequest {
pub hyperparameters: Option<CreateFineTuningJobRequestHyperparameters>,
pub integrations: Option<Vec<CreateFineTuningJobRequestIntegrations>>,
pub metadata: Option<Metadata>,
pub method: Option<FineTuneMethod>,
pub model: Value,
pub seed: Option<i32>,
pub suffix: Option<String>,
pub training_file: String,
pub validation_file: Option<String>,
}
Fields§
§hyperparameters: Option<CreateFineTuningJobRequestHyperparameters>
§integrations: Option<Vec<CreateFineTuningJobRequestIntegrations>>
A list of integrations to enable for your fine-tuning job.
metadata: Option<Metadata>
§method: Option<FineTuneMethod>
§model: Value
The name of the model to fine-tune. You can select one of the supported models.
seed: Option<i32>
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.
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
.
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.
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.