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michal.zyga
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add FineTuningResponse
1 parent 28e5f76 commit d2591f8

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8 files changed

+370
-134
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8 files changed

+370
-134
lines changed

‎core/src/main/scala/sttp/openai/requests/finetuning/FineTuningRequestBody.scala

-133
Original file line numberDiff line numberDiff line change
@@ -41,139 +41,6 @@ object FineTuningRequestBody {
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implicit val fineTuningRequestBodyWriter: SnakePickle.Writer[FineTuningRequestBody] = SnakePickle.macroW[FineTuningRequestBody]
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}
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sealed abstract class Type(val value: String)
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object Type {
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implicit def typeRW(implicit byTypeValue: Map[String, Type]): SnakePickle.ReadWriter[Type] = SnakePickle
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.readwriter[ujson.Value]
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.bimap[Type](
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`type` => SnakePickle.writeJs(`type`.value),
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jsonValue =>
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SnakePickle.read[ujson.Value](jsonValue) match {
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case Str(value) =>
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byTypeValue.get(value) match {
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case Some(t) => t
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case None => throw DeserializationOpenAIException(new Exception(s"Could not deserialize: $value"))
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}
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case e => throw DeserializationOpenAIException(new Exception(s"Could not deserialize: $e"))
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}
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)
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}
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/** @param batchSize
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* Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower
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* variance.
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* @param learningRateMultiplier
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* Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
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* @param nEpochs
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* The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
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* @param beta
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* The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.
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*/
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case class Hyperparameters(
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batchSize: Option[Int] = None,
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learningRateMultiplier: Option[Float] = None,
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nEpochs: Option[Int] = None,
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beta: Option[Float] = None
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)
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object Hyperparameters {
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implicit val hyperparametersW: SnakePickle.Writer[Hyperparameters] = SnakePickle.macroW[Hyperparameters]
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}
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/** @param hyperparameters
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* The hyperparameters used for the fine-tuning job.
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*/
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case class Supervised(
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hyperparameters: Option[Hyperparameters] = None
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)
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object Supervised {
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implicit val supervisedW: SnakePickle.Writer[Supervised] = SnakePickle.macroW[Supervised]
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}
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/** @param hyperparameters
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* The hyperparameters used for the fine-tuning job.
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*/
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case class Dpo(
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hyperparameters: Option[Hyperparameters] = None
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)
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object Dpo {
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implicit val dpoW: SnakePickle.Writer[Dpo] = SnakePickle.macroW[Dpo]
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}
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/** @param `type`
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* The type of method. Is either supervised or dpo.
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* @param supervised
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* Configuration for the supervised fine-tuning method.
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* @param dpo
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* Configuration for the DPO fine-tuning method.
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*/
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case class Method(
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`type`: Option[Type] = None,
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supervised: Option[Supervised] = None,
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dpo: Option[Dpo] = None
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)
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object Method {
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implicit val methodW: SnakePickle.Writer[Method] = SnakePickle.macroW[Method]
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case object Supervised extends Type("supervised")
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case object Dpo extends Type("dpo")
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private val values: Set[Type] = Set(Supervised, Dpo)
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implicit val byTypeValue: Map[String, Type] = values.map(`type` => `type`.value -> `type`).toMap
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}
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/** @param project
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* The name of the project that the new run will be created under.
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* @param name
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* A display name to set for the run. If not set, we will use the Job ID as the name.
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* @param entity
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* The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the
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* run. If not set, the default entity for the registered WandB API key is used.
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* @param tags
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* A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are
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* generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".
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*/
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case class Wandb(
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project: String,
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name: Option[String] = None,
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entity: Option[String] = None,
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tags: Option[Seq[String]]
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)
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object Wandb {
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implicit val wandbW: SnakePickle.Writer[Wandb] = SnakePickle.macroW[Wandb]
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}
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/** @param `type`
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* The type of integration to enable. Currently, only "wandb" (Weights and Biases) is supported.
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* @param wandb
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* The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to.
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* Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to
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* be associated with your run.
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*/
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case class Integration(
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`type`: Type,
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wandb: Wandb
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)
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object Integration {
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implicit val integrationW: SnakePickle.Writer[Integration] = SnakePickle.macroW[Integration]
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case object Wandb extends Type("wandb")
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private val values: Set[Type] = Set(Wandb)
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private val byTypeValue = values.map(`type` => `type`.value -> `type`).toMap
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implicit val typeRW: SnakePickle.ReadWriter[Type] = Type.typeRW(byTypeValue)
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}
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17744
sealed abstract class FineTuningModel(val value: String)
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17946
object FineTuningModel {

‎core/src/main/scala/sttp/openai/requests/finetuning/FineTuningResponse.scala

+82
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,82 @@
1+
package sttp.openai.requests.finetuning
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import sttp.openai.OpenAIExceptions.OpenAIException.DeserializationOpenAIException
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import sttp.openai.json.SnakePickle
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import ujson.Str
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case class FineTuningResponse(
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id: String,
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createdAt: Int,
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error: Option[Error] = None,
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fineTunedModel: Option[String],
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finishedAt: Option[Int],
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hyperparameters: Option[Hyperparameters],
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model: String,
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`object`: String,
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organizationId: String,
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resultFiles: Seq[String],
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status: Status,
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trainedTokens: Option[Int] = None,
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trainingFile: String,
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validationFile: Option[String],
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integrations: Option[Seq[Integration]] = None,
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seed: Int,
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estimatedFinish: Option[Int] = None,
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method: Method
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)
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28+
object FineTuningResponse {
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implicit val fineTuningResponseDataReader: SnakePickle.Reader[FineTuningResponse] = SnakePickle.macroR[FineTuningResponse]
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}
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/** @param code
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* A machine-readable error code.
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* @param message
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* A human-readable error message.
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* @param param
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* The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not
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* parameter-specific.
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*/
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case class Error(
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code: String,
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message: String,
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param: Option[String] = None
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)
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object Error {
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implicit val errorReader: SnakePickle.Reader[Error] = SnakePickle.macroR[Error]
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}
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50+
sealed abstract class Status(val value: String)
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object Status {
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54+
implicit val statusRW: SnakePickle.Reader[Status] = SnakePickle
55+
.reader[ujson.Value]
56+
.map[Status](
57+
jsonValue =>
58+
SnakePickle.read[ujson.Value](jsonValue) match {
59+
case Str(value) => byStatusValue.getOrElse(value, CustomStatus(value))
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case e => throw DeserializationOpenAIException(new Exception(s"Could not deserialize: $e"))
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}
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)
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case object ValidatingFiles extends Status("validating_files")
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case object Queued extends Status("queued")
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case object Running extends Status("running")
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case object Succeeded extends Status("succeeded")
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case object Failed extends Status("failed")
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case object Cancelled extends Status("cancelled")
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case class CustomStatus(customStatus: String) extends Status(customStatus)
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private val values: Set[Status] = Set(ValidatingFiles, Queued, Running, Succeeded, Failed, Cancelled)
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private val byStatusValue = values.map(status => status.value -> status).toMap
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}

‎core/src/main/scala/sttp/openai/requests/finetuning/Hyperparameters.scala

+24
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@@ -0,0 +1,24 @@
1+
package sttp.openai.requests.finetuning
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3+
import sttp.openai.json.SnakePickle
4+
5+
/** @param batchSize
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* Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower
7+
* variance.
8+
* @param learningRateMultiplier
9+
* Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
10+
* @param nEpochs
11+
* The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
12+
* @param beta
13+
* The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.
14+
*/
15+
case class Hyperparameters(
16+
batchSize: Option[Int] = None,
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learningRateMultiplier: Option[Float] = None,
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nEpochs: Option[Int] = None,
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beta: Option[Float] = None
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)
21+
22+
object Hyperparameters {
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implicit val hyperparametersW: SnakePickle.ReadWriter[Hyperparameters] = SnakePickle.macroRW[Hyperparameters]
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}

‎core/src/main/scala/sttp/openai/requests/finetuning/Integration.scala

+49
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,49 @@
1+
package sttp.openai.requests.finetuning
2+
3+
import sttp.openai.json.SnakePickle
4+
5+
/** @param `type`
6+
* The type of integration to enable. Currently, only "wandb" (Weights and Biases) is supported.
7+
* @param wandb
8+
* The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to.
9+
* Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to
10+
* be associated with your run.
11+
*/
12+
case class Integration(
13+
`type`: Type,
14+
wandb: Wandb
15+
)
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object Integration {
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implicit val integrationRW: SnakePickle.ReadWriter[Integration] = SnakePickle.macroRW[Integration]
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case object Wandb extends Type("wandb")
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private val values: Set[Type] = Set(Wandb)
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private val byTypeValue = values.map(`type` => `type`.value -> `type`).toMap
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implicit val typeRW: SnakePickle.ReadWriter[Type] = Type.typeRW(byTypeValue)
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}
28+
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/** @param project
30+
* The name of the project that the new run will be created under.
31+
* @param name
32+
* A display name to set for the run. If not set, we will use the Job ID as the name.
33+
* @param entity
34+
* The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the
35+
* run. If not set, the default entity for the registered WandB API key is used.
36+
* @param tags
37+
* A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are
38+
* generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".
39+
*/
40+
case class Wandb(
41+
project: String,
42+
name: Option[String] = None,
43+
entity: Option[String] = None,
44+
tags: Option[Seq[String]]
45+
)
46+
47+
object Wandb {
48+
implicit val wandbRW: SnakePickle.ReadWriter[Wandb] = SnakePickle.macroRW[Wandb]
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}

‎core/src/main/scala/sttp/openai/requests/finetuning/Method.scala

+50
Original file line numberDiff line numberDiff line change
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package sttp.openai.requests.finetuning
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3+
import sttp.openai.json.SnakePickle
4+
5+
/** @param `type`
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* The type of method. Is either supervised or dpo.
7+
* @param supervised
8+
* Configuration for the supervised fine-tuning method.
9+
* @param dpo
10+
* Configuration for the DPO fine-tuning method.
11+
*/
12+
case class Method(
13+
`type`: Option[Type] = None,
14+
supervised: Option[Supervised] = None,
15+
dpo: Option[Dpo] = None
16+
)
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18+
object Method {
19+
implicit val methodRW: SnakePickle.ReadWriter[Method] = SnakePickle.macroRW[Method]
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case object Supervised extends Type("supervised")
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case object Dpo extends Type("dpo")
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private val values: Set[Type] = Set(Supervised, Dpo)
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implicit val byTypeValue: Map[String, Type] = values.map(`type` => `type`.value -> `type`).toMap
28+
}
29+
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/** @param hyperparameters
31+
* The hyperparameters used for the fine-tuning job.
32+
*/
33+
case class Supervised(
34+
hyperparameters: Option[Hyperparameters] = None
35+
)
36+
37+
object Supervised {
38+
implicit val supervisedRW: SnakePickle.ReadWriter[Supervised] = SnakePickle.macroRW[Supervised]
39+
}
40+
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/** @param hyperparameters
42+
* The hyperparameters used for the fine-tuning job.
43+
*/
44+
case class Dpo(
45+
hyperparameters: Option[Hyperparameters] = None
46+
)
47+
48+
object Dpo {
49+
implicit val dpoRW: SnakePickle.ReadWriter[Dpo] = SnakePickle.macroRW[Dpo]
50+
}

‎core/src/main/scala/sttp/openai/requests/finetuning/Type.scala

+24
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package sttp.openai.requests.finetuning
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3+
import sttp.openai.OpenAIExceptions.OpenAIException.DeserializationOpenAIException
4+
import sttp.openai.json.SnakePickle
5+
import ujson.Str
6+
7+
abstract class Type(val value: String)
8+
9+
object Type {
10+
implicit def typeRW(implicit byTypeValue: Map[String, Type]): SnakePickle.ReadWriter[Type] = SnakePickle
11+
.readwriter[ujson.Value]
12+
.bimap[Type](
13+
`type` => SnakePickle.writeJs(`type`.value),
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jsonValue =>
15+
SnakePickle.read[ujson.Value](jsonValue) match {
16+
case Str(value) =>
17+
byTypeValue.get(value) match {
18+
case Some(t) => t
19+
case None => throw DeserializationOpenAIException(new Exception(s"Could not deserialize: $value"))
20+
}
21+
case e => throw DeserializationOpenAIException(new Exception(s"Could not deserialize: $e"))
22+
}
23+
)
24+
}

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