Fine-tuning endpoint changed to "/fine_tuning/jobs". Closes #42

openai-client/src/main/scala/io/cequence/openaiscala/service/EndPoint.scala

@@ -16,7 +16,7 @@ object EndPoint extends Enumeration {
   case object audio_transcriptions extends EndPoint("audio/transcriptions")
   case object audio_translations extends EndPoint("audio/translations")
   case object files extends EndPoint
-  case object fine_tunes extends EndPoint("fine-tunes")
+  case object fine_tunes extends EndPoint("fine_tuning/jobs")
   case object moderations extends EndPoint
 }
 

openai-client/src/main/scala/io/cequence/openaiscala/service/OpenAIServiceImpl.scala

@@ -327,7 +327,7 @@ private trait OpenAIServiceImpl extends OpenAICoreServiceImpl with OpenAIService
       EndPoint.fine_tunes,
       endPointParam = Some(s"$fineTuneId/events"),
       params = Seq(
-        Param.stream -> Some(false)
+        Param.stream -> Some(false) // TODO: is streaming still supported?
       )
     ).map { response =>
       handleNotFoundAndError(response).map(jsResponse =>

openai-core/src/main/scala/io/cequence/openaiscala/domain/response/FineTuneJob.scala

@@ -2,6 +2,7 @@ package io.cequence.openaiscala.domain.response
 
 import java.{util => ju}
 
+// TODO: adapt
 case class FineTuneJob(
   id: String,
   model: String,
@@ -10,10 +11,10 @@ case class FineTuneJob(
   fine_tuned_model: Option[String],
   hyperparams: FineTuneHyperparams,
   organization_id: String,
-  result_files: Seq[FileInfo],
   status: String, // e.g. pending or cancelled
   validation_files: Seq[FileInfo],
   training_files: Seq[FileInfo],
+  result_files: Seq[FileInfo],
   updated_at: ju.Date
 )
 
@@ -23,6 +24,7 @@ case class FineTuneEvent(
   message: String
 )
 
+// TODO: adapt
 case class FineTuneHyperparams(
   batch_size: Option[Int],
   learning_rate_multiplier: Option[Double],

openai-core/src/main/scala/io/cequence/openaiscala/domain/settings/CreateFineTuneSettings.scala

@@ -16,41 +16,48 @@ case class CreateFineTuneSettings(
   // 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.
+  // TODO: is it still supported?
   batch_size: Option[Int] = None,
 
   // 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.
+  // TODO: is it still supported?
   learning_rate_multiplier: Option[Double] = None,
 
   // 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.
   // Defaults to 0.01
+  // TODO: is it still supported?
   prompt_loss_weight: Option[Double] = None,
 
   // 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 <a href="https://beta.openai.com/docs/guides/fine-tuning/analyzing-your-fine-tuned-model">results file</a>.
   // 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.
   // Defaults to false
+  // TODO: is it still supported?
   compute_classification_metrics: Option[Boolean] = None,
 
   // The number of classes in a classification task.
   // This parameter is required for multiclass classification.
+  // TODO: is it still supported?
   classification_n_classes: Option[Int] = None,
 
   // The positive class in binary classification.
   // This parameter is needed to generate precision, recall, and F1 metrics when doing binary classification.
+  // TODO: is it still supported?
   classification_positive_class: Option[String] = None,
 
   // 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.
+  // TODO: is it still supported?
   classification_betas: Option[Seq[Double]] = None,
 
   // A string of up to 40 characters that will be added to your fine-tuned model name.

openai-core/src/main/scala/io/cequence/openaiscala/service/OpenAIService.scala

@@ -320,6 +320,7 @@ trait OpenAIService extends OpenAICoreService with OpenAIServiceConsts {
    * @see
    *   <a href="https://platform.openai.com/docs/api-reference/fine-tunes/list">OpenAI Doc</a>
    */
+  // TODO: add pagination: after: Option[String], limit: Option[Int]
   def listFineTunes: Future[Seq[FineTuneJob]]
 
   /**
@@ -366,6 +367,7 @@ trait OpenAIService extends OpenAICoreService with OpenAIServiceConsts {
    *   <a href="https://platform.openai.com/docs/api-reference/fine-tunes/events">OpenAI
    *   Doc</a>
    */
+  // TODO: add pagination: after: Option[String], limit: Option[Int]
   def listFineTuneEvents(
     fineTuneId: String
   ): Future[Option[Seq[FineTuneEvent]]]