Models.md

Models

• List of models
• Retrieve a model
• Delete a model
• Model distilation

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Refert to official documentation.

List of models

The list of available models can be retrieved from the Models API response. The models are ordered by release date, with the most recently published appearing first.

//uses GenAI, GenAI.Types, GenAI.Tutorial.VCL;

  TutorialHub.JSONRequestClear;

  //Asynchronous example
  Client.Models.AsynList(
    function : TAsynModels
    begin
      Result.Sender := TutorialHub;
      Result.OnStart := Start;
      Result.OnSuccess := Display;
      Result.OnError := Display;
    end);

  //Synchronous example
//  var Value := Client.Models.List;
//  try
//    Display(TutorialHub, Value);
//  finally
//    Value.Free;
//  end;

  //Asynchronous promise example
//  var Promise := Client.Models.AsyncAwaitList;
//
//  Promise
//    .&Then<TModels>(
//      function (Value: TModels): TModels
//      begin
//        Display(TutorialHub, Value);
//        Result := Value;
//      end)
//    .&Catch(
//      procedure (E: Exception)
//      begin
//        Display(TutorialHub, E.Message);
//      end);

Retrieve a model

Retrieve a model using its ID.

//uses GenAI, GenAI.Types, GenAI.Tutorial.VCL;

  TutorialHub.JSONRequestClear;
  TutorialHub.ModelId := '...the id tio retrieve...';

  //Asynchronous example
  Client.Models.AsynRetrieve(TutorialHub.ModelId,
    function : TAsynModel
    begin
      Result.Sender := TutorialHub;
      Result.OnStart := Start;
      Result.OnSuccess := Display;
      Result.OnError := Display;
    end);

  //Synchronous example
//  var Value := Client.Models.Retrieve(TutorialHub.ModelId);
//  try
//    Display(TutorialHub, Value);
//  finally
//    Value.Free;
//  end;

  //Asynchronous promise example
//  var Promise := Client.Models.AsyncAwaitRetrieve(TutorialHub.ModelId);
//
//  Promise
//    .&Then<string>(
//      function (Value: TModel): string
//      begin
//        Result := Value.Id;
//        Display(TutorialHub, Value);
//      end)
//    .&Catch(
//      procedure (E: Exception)
//      begin
//        Display(TutorialHub, E.Message);
//      end);

Delete a model

Deleting a model is only possible if the model is one of your fine-tuned models.

//uses GenAI, GenAI.Types, GenAI.Tutorial.VCL;

  TutorialHub.JSONRequestClear;
  TutorialHub.ModelId := '...Id of the model to delete...';

  //Asynchronous example
  Client.Models.AsynDelete(TutorialHub.ModelId,
    function : TAsynDeletion
    begin
      Result.Sender := TutorialHub;
      Result.OnStart := Start;
      Result.OnSuccess := Display;
      Result.OnError := Display;
    end);

  //Synchronous example
//  var Value := Client.Models.Delete(TutorialHub.ModelId);
//  try
//    Display(TutorialHub, Value);
//  finally
//    Value.Free;
//  end;

  //Asynchronous promise example
//  var Promise := Client.Models.AsyncAwaitDelete(TutorialHub.ModelId);
//
//  Promise
//    .&Then<string>(
//      function (Value: TDeletion): string
//      begin
//        Result := Value.Id;
//        Display(TutorialHub, Value);
//      end)
//    .&Catch(
//      procedure (E: Exception)
//      begin
//        Display(TutorialHub, E.Message);
//      end);

Model distilation

Refer to official documentation.

Model distillation involves using outputs (completions) from a large model to fine-tune a smaller model, enabling it to achieve similar performance for a specific task while reducing both cost and latency.

1. Store outputs from the large model:

   • Generate high-quality results using a large model (e.g., o1-preview or gpt-4o) that meet your performance standards.
   • Use the store: true option and the metadata field in the Chat Completions API to save these results.
   • You can later view and filter these stored completions through the dashboard.

2. Evaluate to establish a baseline:

   • Use the stored completions to assess the performance of both the large and small models using the evaluation product (evals).
   • Compare the results to measure the initial performance gap between the two models.

3. Create a training dataset for fine-tuning:

   • Select the stored completions you want to use as training data for fine-tuning the smaller model.
   • Start a fine-tuning session by selecting the base snapshot (e.g., GPT-4o-mini). A few hundred samples may be enough, but a larger, more diverse set of thousands of examples can yield better results.

4. Evaluate the fine-tuned small model:

   • Once fine-tuning is complete, run new evaluations to compare the fine-tuned model with the base small model and the large model.
   • Alternatively, store new completions generated by the fine-tuned model and evaluate them. Adjustments can be made to improve performance by:
     • Increasing the diversity of the training data,
     • Refining prompts and outputs generated by the large model,
     • Improving evaluation criteria (graders).

By iteratively refining these elements, the smaller model can closely match the large model’s performance for specific tasks. Model distillation is a powerful method, but it is just one of many ways to optimize model outputs.