=encoding utf8

=head1 NAME

OpenAPI::Client::OpenAI::Path::fine_tuning-jobs-fine_tuning_job_id-pause - Documentation for the /fine_tuning/jobs/{fine_tuning_job_id}/pause path.

=head1 OPERATIONS

=head2 POST /fine_tuning/jobs/{fine_tuning_job_id}/pause

=head3 pauseFineTuningJob

  $client->pause_fine_tuning_job({
      body => { ... },
  });

Pause a fine-tune job.

=head4 Path/query parameters

=over

=item * C<fine_tuning_job_id> (in path, required, string) - The ID of the fine-tuning job to pause.

=back

=head4 Responses

B<200 - OK>

Content-Type: application/json

Example:

    {
       "created_at" : 1692661014,
       "estimated_finish" : 0,
       "fine_tuned_model" : "ft:davinci-002:my-org:custom_suffix:7q8mpxmy",
       "finished_at" : 1692661190,
       "hyperparameters" : {
          "batch_size" : 1,
          "learning_rate_multiplier" : 1,
          "n_epochs" : 4
       },
       "id" : "ftjob-abc123",
       "integrations" : [],
       "metadata" : {
          "key" : "value"
       },
       "method" : {
          "supervised" : {
             "hyperparameters" : {
                "batch_size" : 1,
                "learning_rate_multiplier" : 1,
                "n_epochs" : 4
             }
          },
          "type" : "supervised"
       },
       "model" : "davinci-002",
       "object" : "fine_tuning.job",
       "organization_id" : "org-123",
       "result_files" : [
          "file-abc123"
       ],
       "seed" : 0,
       "status" : "succeeded",
       "trained_tokens" : 5768,
       "training_file" : "file-abc123",
       "validation_file" : null
    }

=head1 SCHEMAS

=head2 FineTuneDPOHyperparameters

Properties:

=over

=item * C<batch_size> (oneOf) - Number of examples in each batch. A larger batch size means that model
parameters are updated less frequently, but with lower variance.

Default: auto

=item * C<beta> (oneOf) - The beta value for the DPO method. A higher beta value will increase the
weight of the penalty between the policy and reference model.

Default: auto

=item * C<learning_rate_multiplier> (oneOf) - Scaling factor for the learning rate. A smaller learning rate may be useful to
avoid overfitting.

Default: auto

=item * C<n_epochs> (oneOf) - The number of epochs to train the model for. An epoch refers to one full cycle
through the training dataset.

Default: auto

=back

=head2 FineTuneDPOMethod

Properties:

=over

=item * C<hyperparameters> (FineTuneDPOHyperparameters)

See L</FineTuneDPOHyperparameters> below for shape.

=back

=head2 FineTuneMethod

Properties:

=over

=item * C<dpo> (FineTuneDPOMethod)

See L</FineTuneDPOMethod> below for shape.

=item * C<reinforcement> (FineTuneReinforcementMethod)

See L</FineTuneReinforcementMethod> below for shape.

=item * C<supervised> (FineTuneSupervisedMethod)

See L</FineTuneSupervisedMethod> below for shape.

=item * C<type> (string, required) - The type of method. Is either C<supervised> , C<dpo> , or C<reinforcement> .

Allowed values: supervised, dpo, reinforcement

=back

=head2 FineTuneReinforcementHyperparameters

Properties:

=over

=item * C<batch_size> (oneOf) - Number of examples in each batch. A larger batch size means that model
parameters are updated less frequently, but with lower variance.

Default: auto

=item * C<compute_multiplier> (oneOf) - Multiplier on amount of compute used for exploring search space during
training.

Default: auto

=item * C<eval_interval> (oneOf) - The number of training steps between evaluation runs.

Default: auto

=item * C<eval_samples> (oneOf) - Number of evaluation samples to generate per training step.

Default: auto

=item * C<learning_rate_multiplier> (oneOf) - Scaling factor for the learning rate. A smaller learning rate may be useful to
avoid overfitting.

Default: auto

=item * C<n_epochs> (oneOf) - The number of epochs to train the model for. An epoch refers to one full cycle
through the training dataset.

Default: auto

=item * C<reasoning_effort> (string) - Level of reasoning effort.

Allowed values: default, low, medium, high

Default: default

=back

=head2 FineTuneReinforcementMethod

Properties:

=over

=item * C<grader> (object, required) - The grader used for the fine-tuning job.

=item * C<hyperparameters> (FineTuneReinforcementHyperparameters)

See L</FineTuneReinforcementHyperparameters> below for shape.

=back

=head2 FineTuneSupervisedHyperparameters

Properties:

=over

=item * C<batch_size> (oneOf) - Number of examples in each batch. A larger batch size means that model
parameters are updated less frequently, but with lower variance.

Default: auto

=item * C<learning_rate_multiplier> (oneOf) - Scaling factor for the learning rate. A smaller learning rate may be useful to
avoid overfitting.

Default: auto

=item * C<n_epochs> (oneOf) - The number of epochs to train the model for. An epoch refers to one full cycle
through the training dataset.

Default: auto

=back

=head2 FineTuneSupervisedMethod

Properties:

=over

=item * C<hyperparameters> (FineTuneSupervisedHyperparameters)

See L</FineTuneSupervisedHyperparameters> below for shape.

=back

=head2 FineTuningJob

Properties:

=over

=item * C<created_at> (integer, required) - The Unix timestamp (in seconds) for when the fine-tuning job was created.

=item * C<error> (anyOf, required)

=item * C<estimated_finish> (anyOf)

=item * C<fine_tuned_model> (anyOf, required)

=item * C<finished_at> (anyOf, required)

=item * C<hyperparameters> (object, required) - The hyperparameters used for the fine-tuning job. This value will only be
returned when running C<supervised> jobs.

=item * C<id> (string, required) - The object identifier, which can be referenced in the API endpoints.

=item * C<integrations> (anyOf)

=item * C<metadata> (Metadata)

See L</Metadata> below for shape.

=item * C<method> (FineTuneMethod)

See L</FineTuneMethod> below for shape.

=item * C<model> (string, required) - The base model that is being fine-tuned.

=item * C<object> (string, required) - The object type, which is always "fine_tuning.job".

Allowed values: fine_tuning.job

=item * C<organization_id> (string, required) - The organization that owns the fine-tuning job.

=item * C<result_files> (array of string, required) - The compiled results file ID(s) for the fine-tuning job. You can retrieve the
results with the
L<Files API|https://platform.openai.com/docs/api-reference/files/retrieve-contents>
.

=item * C<seed> (integer, required) - The seed used for the fine-tuning job.

=item * C<status> (string, required) - The current status of the fine-tuning job, which can be either
C<validating_files> , C<queued> , C<running> , C<succeeded> , C<failed> , or
C<cancelled> .

Allowed values: validating_files, queued, running, succeeded, failed, cancelled

=item * C<trained_tokens> (anyOf, required)

=item * C<training_file> (string, required) - The file ID used for training. You can retrieve the training data with the
L<Files API|https://platform.openai.com/docs/api-reference/files/retrieve-contents>
.

=item * C<validation_file> (anyOf, required)

=back

=head2 Metadata

Set of 16 key-value pairs that can be attached to an object. This can be
useful for storing additional information about the object in a structured
format, and querying for objects via API or the dashboard.

Keys are strings with a maximum length of 64 characters. Values are strings
with a maximum length of 512 characters.

=head1 SEE ALSO

L<OpenAPI::Client::OpenAI::Path>

=head1 COPYRIGHT AND LICENSE

Copyright (C) 2023-2026 by Nelson Ferraz

This library is free software; you can redistribute it and/or modify
it under the same terms as Perl itself, either Perl version 5.14.0 or,
at your option, any later version of Perl 5 you may have available.

=cut
