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use crate::shared::response_wrapper::OpenAIError;
use derive_builder::Builder;
use serde::{Deserialize, Serialize};
#[derive(Builder, Clone, Debug, Default, Serialize)]
#[builder(name = "CreateFineTuneRequestBuilder")]
#[builder(pattern = "mutable")]
#[builder(setter(into, strip_option), default)]
#[builder(derive(Debug))]
#[builder(build_fn(error = "OpenAIError"))]
pub struct CreateFineTuneRequest {
/// 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, 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](https://platform.openai.com/docs/guides/fine-tuning/creating-training-data) for more details.
pub training_file: 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](https://platform.openai.com/docs/guides/fine-tuning/analyzing-your-fine-tuned-model).
/// 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](https://platform.openai.com/docs/guides/fine-tuning/creating-training-data) for more details.
#[serde(skip_serializing_if = "Option::is_none")]
pub validation_file: 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.
/// To learn more about these models, see the [Models](https://platform.openai.com/docs/models) documentation.
pub model: Option<String>,
/// The number of epochs to train the model for.
/// An epoch refers to one full cycle through the training dataset.
#[serde(skip_serializing_if = "Option::is_none")]
pub n_epochs: Option<u32>,
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub batch_size: Option<u32>,
/// 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 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub learning_rate_multiplier: Option<f32>,
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub prompt_loss_weight: Option<f32>,
/// 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](https://platform.openai.com/docs/guides/fine-tuning/analyzing-your-fine-tuned-model).
///
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub compute_classification_metrics: Option<bool>,
/// The number of classes in a classification task.
///
/// This parameter is required for multiclass classification.
#[serde(skip_serializing_if = "Option::is_none")]
pub classification_n_classes: Option<u32>,
/// The positive class in binary classification.
///
/// This parameter is needed to generate precision, recall, and F1 metrics when doing binary classification.
#[serde(skip_serializing_if = "Option::is_none")]
pub classification_positive_class: Option<String>,
/// If 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.
#[serde(skip_serializing_if = "Option::is_none")]
classification_betas: Option<Vec<f32>>,
/// 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`.
#[serde(skip_serializing_if = "Option::is_none")]
suffix: Option<String>,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct FineTuneResponse {
pub id: String,
pub object: String,
pub model: String,
pub created_at: u32,
pub events: Option<Vec<FineTuneEvent>>,
pub fine_tuned_model: Option<String>,
pub hyperparams: HyperParams,
pub organization_id: String,
pub result_files: Vec<TrainingFile>,
pub status: String,
pub validation_files: Vec<TrainingFile>,
pub training_files: Vec<TrainingFile>,
pub updated_at: u32,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct FineTuneEvent {
pub object: String,
pub created_at: u32,
pub level: String,
pub message: String,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct HyperParams {
pub batch_size: u32,
pub learning_rate_multiplier: f32,
pub n_epochs: u32,
pub prompt_loss_weight: f32,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct TrainingFile {
pub id: String,
pub object: String,
pub bytes: u32,
pub created_at: u32,
pub filename: String,
pub purpose: String,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct FineTuneListResponse {
pub object: String,
pub data: Vec<FineTuneResponse>,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct EventListResponse {
pub object: String,
pub data: Vec<FineTuneEvent>,
}
#[derive(Debug, Deserialize, Clone, Serialize)]
pub struct DeleteFileResponse {
pub id: String,
pub object: String,
pub deleted: bool,
}