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use std::pin::Pin;
use derive_builder::Builder;
use futures::Stream;
use serde::{Deserialize, Serialize};
use crate::error::OpenAIError;
use super::OpenAIFile;
#[derive(Debug, Serialize, Clone, Default, Builder, PartialEq)]
#[builder(name = "CreateFineTuneRequestArgs")]
#[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.
#[serde(skip_serializing_if = "Option::is_none")]
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>, // default: 4
/// 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>, // default: null
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub learning_rate_multiplier: Option<f32>, // default: null
/// 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>, // default: 0.01
/// 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>, // default: false
/// 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>, // default: null
/// 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>, // default: null
/// 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.
#[serde(skip_serializing_if = "Option::is_none")]
pub classification_betas: Option<Vec<f32>>, // default: null
/// 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")]
pub suffix: Option<String>, // default: null, minLength:1, maxLength:40
}
#[derive(Debug, Deserialize, Clone, PartialEq, Serialize)]
pub struct ListFineTuneResponse {
pub object: String,
pub data: Vec<FineTune>,
}
#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
pub struct FineTune {
pub id: String,
pub object: String,
pub created_at: u32,
pub updated_at: u32,
pub model: String,
pub fine_tuned_model: Option<String>, // nullable: true
pub organization_id: String,
pub status: String,
pub hyperparams: serde_json::Value,
pub training_files: Vec<OpenAIFile>,
pub validation_files: Vec<OpenAIFile>,
pub result_files: Vec<OpenAIFile>,
pub events: Option<Vec<FineTuneEvent>>,
}
#[derive(Debug, Deserialize, Serialize, Clone, PartialEq)]
pub struct FineTuneEvent {
pub object: String,
pub created_at: u32,
pub level: String,
pub message: String,
}
#[derive(Debug, Deserialize, Clone, PartialEq, Serialize)]
pub struct ListFineTuneEventsResponse {
pub object: String,
pub data: Vec<FineTuneEvent>,
}
#[derive(Debug, Deserialize, Clone, PartialEq, Serialize)]
pub struct ListFineTuneEventsStreamResponse {
pub object: String,
pub data: Option<Vec<FineTuneEvent>>,
}
/// Parsed server side events stream until an \[DONE\] is received from server.
pub type FineTuneEventsResponseStream =
Pin<Box<dyn Stream<Item = Result<ListFineTuneEventsStreamResponse, OpenAIError>> + Send>>;