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use derive_builder::Builder;
use serde::{Deserialize, Serialize};
use crate::error::OpenAIError;
#[derive(Debug, Serialize, Deserialize, Clone, Default, PartialEq)]
#[serde(untagged)]
pub enum NEpochs {
NEpochs(u8),
#[default]
#[serde(rename = "auto")]
Auto,
}
#[derive(Debug, Serialize, Deserialize, Clone, Default, PartialEq)]
pub struct Hyperparameters {
/// The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
pub n_epochs: NEpochs,
}
#[derive(Debug, Serialize, Clone, Default, Builder, PartialEq)]
#[builder(name = "CreateFineTuningJobRequestArgs")]
#[builder(pattern = "mutable")]
#[builder(setter(into, strip_option), default)]
#[builder(derive(Debug))]
#[builder(build_fn(error = "OpenAIError"))]
pub struct CreateFineTuningJobRequest {
/// The name of the model to fine-tune. You can select one of the
/// [supported models](https://platform.openai.com/docs/guides/fine-tuning/what-models-can-be-fine-tuned).
pub model: String,
/// The ID of an uploaded file that contains training data.
///
/// See [upload file](https://platform.openai.com/docs/api-reference/files/upload) 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`.
///
/// See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning) for more details.
pub training_file: String,
/// The hyperparameters used for the fine-tuning job.
pub hyperparameters: Option<Hyperparameters>,
/// A string of up to 18 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-3.5-turbo:openai:custom-model-name:7p4lURel`.
#[serde(skip_serializing_if = "Option::is_none")]
pub suffix: Option<String>, // default: null, minLength:1, maxLength:40
/// 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](https://platform.openai.com/docs/guides/fine-tuning) for more details.
#[serde(skip_serializing_if = "Option::is_none")]
pub validation_file: Option<String>,
}
/// For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure.
#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
pub struct FineTuneJobError {
/// A machine-readable error code.
pub code: String,
/// A human-readable error message.
pub message: String,
/// The parameter that was invalid, usually `training_file` or `validation_file`.
/// This field will be null if the failure was not parameter-specific.
pub param: Option<String>, // nullable true
}
#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
#[serde(rename_all = "snake_case")]
pub enum FineTuningJobStatus {
ValidatingFiles,
Queued,
Running,
Succeeded,
Failed,
Cancelled,
}
/// The `fine_tuning.job` object represents a fine-tuning job that has been created through the API.
#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
pub struct FineTuningJob {
/// The object identifier, which can be referenced in the API endpoints.
pub id: String,
/// The Unix timestamp (in seconds) for when the fine-tuning job was created.
pub created_at: u32,
/// For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure.
pub error: Option<FineTuneJobError>,
/// The name of the fine-tuned model that is being created.
/// The value will be null if the fine-tuning job is still running.
pub fine_tuned_model: Option<String>, // nullable: true
/// The Unix timestamp (in seconds) for when the fine-tuning job was finished.
/// The value will be null if the fine-tuning job is still running.
pub finished_at: Option<u32>, // nullable true
/// The hyperparameters used for the fine-tuning job.
/// See the [fine-tuning guide](/docs/guides/fine-tuning) for more details.
pub hyperparameters: Hyperparameters,
/// The base model that is being fine-tuned.
pub model: String,
/// The object type, which is always "fine_tuning.job".
pub object: String,
/// The organization that owns the fine-tuning job.
pub organization_id: String,
/// The compiled results file ID(s) for the fine-tuning job.
/// You can retrieve the results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
pub result_files: Vec<String>,
/// The current status of the fine-tuning job, which can be either
/// `validating_files`, `queued`, `running`, `succeeded`, `failed`, or `cancelled`.
pub status: FineTuningJobStatus,
/// The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.
pub trained_tokens: Option<u32>,
/// The file ID used for training. You can retrieve the training data with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
pub training_file: String,
/// The file ID used for validation. You can retrieve the validation results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
pub validation_file: Option<String>,
}
#[derive(Debug, Serialize, Deserialize, Clone, PartialEq)]
pub struct ListPaginatedFineTuningJobsResponse {
pub data: Vec<FineTuningJob>,
pub has_more: bool,
pub object: String,
}
#[derive(Debug, Serialize, Deserialize, Clone, PartialEq)]
pub struct ListFineTuningJobEventsResponse {
pub data: Vec<FineTuningJobEvent>,
pub object: String,
}
#[derive(Debug, Serialize, Deserialize, Clone, PartialEq)]
#[serde(rename_all = "lowercase")]
pub enum Level {
Info,
Warn,
Error,
}
///Fine-tuning job event object
#[derive(Debug, Serialize, Deserialize, Clone, PartialEq)]
pub struct FineTuningJobEvent {
pub id: String,
pub created_at: u32,
pub level: Level,
pub message: String,
pub object: String,
}