Fine Tuning

Methods

Domain Types

Dpo Hyperparameters

• class DpoHyperparameters

  The hyperparameters used for the DPO fine-tuning job.

  • batch_size: :auto | Integer

    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

    • BatchSize = :auto

      • :auto

    • Integer = Integer

  • beta: :auto | Float

    The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

    • Beta = :auto

      • :auto

    • Float = Float

  • learning_rate_multiplier: :auto | Float

    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

    • LearningRateMultiplier = :auto

      • :auto

    • Float = Float

  • n_epochs: :auto | Integer

    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

    • NEpochs = :auto

      • :auto

    • Integer = Integer

Dpo Method

• class DpoMethod

  Configuration for the DPO fine-tuning method.

  • hyperparameters: DpoHyperparameters

    The hyperparameters used for the DPO fine-tuning job.

    • batch_size: :auto | Integer

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • BatchSize = :auto

        • :auto

      • Integer = Integer

    • beta: :auto | Float

      The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

      • Beta = :auto

        • :auto

      • Float = Float

    • learning_rate_multiplier: :auto | Float

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • LearningRateMultiplier = :auto

        • :auto

      • Float = Float

    • n_epochs: :auto | Integer

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • NEpochs = :auto

        • :auto

      • Integer = Integer

Reinforcement Hyperparameters

• class ReinforcementHyperparameters

  The hyperparameters used for the reinforcement fine-tuning job.

  • batch_size: :auto | Integer

    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

    • BatchSize = :auto

      • :auto

    • Integer = Integer

  • compute_multiplier: :auto | Float

    Multiplier on amount of compute used for exploring search space during training.

    • ComputeMultiplier = :auto

      • :auto

    • Float = Float

  • eval_interval: :auto | Integer

    The number of training steps between evaluation runs.

    • EvalInterval = :auto

      • :auto

    • Integer = Integer

  • eval_samples: :auto | Integer

    Number of evaluation samples to generate per training step.

    • EvalSamples = :auto

      • :auto

    • Integer = Integer

  • learning_rate_multiplier: :auto | Float

    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

    • LearningRateMultiplier = :auto

      • :auto

    • Float = Float

  • n_epochs: :auto | Integer

    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

    • NEpochs = :auto

      • :auto

    • Integer = Integer

  • reasoning_effort: :default | :low | :medium | :high

    Level of reasoning effort.

    • :default

    • :low

    • :medium

    • :high

Reinforcement Method

• class ReinforcementMethod

  Configuration for the reinforcement fine-tuning method.

  • grader: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

    The grader used for the fine-tuning job.

    • class StringCheckGrader

      A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

      • input: String

        The input text. This may include template strings.

      • name: String

        The name of the grader.

      • operation: :eq | :ne | :like | :ilike

        The string check operation to perform. One of eq, ne, like, or ilike.

        • :eq

        • :ne

        • :like

        • :ilike

      • reference: String

        The reference text. This may include template strings.

      • type: :string_check

        The object type, which is always string_check.

        • :string_check

    • class TextSimilarityGrader

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • evaluation_metric: :cosine | :fuzzy_match | :bleu | 8 more

        The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

        • :cosine

        • :fuzzy_match

        • :bleu

        • :gleu

        • :meteor

        • :rouge_1

        • :rouge_2

        • :rouge_3

        • :rouge_4

        • :rouge_5

        • :rouge_l

      • input: String

        The text being graded.

      • name: String

        The name of the grader.

      • reference: String

        The text being graded against.

      • type: :text_similarity

        The type of grader.

        • :text_similarity

    • class PythonGrader

      A PythonGrader object that runs a python script on the input.

      • name: String

        The name of the grader.

      • source: String

        The source code of the python script.

      • type: :python

        The object type, which is always python.

        • :python

      • image_tag: String

        The image tag to use for the python script.

    • class ScoreModelGrader

      A ScoreModelGrader object that uses a model to assign a score to the input.

      • input: Array[Input{ content, role, type}]

        The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

        • content: String | ResponseInputText | OutputText{ text, type} | 3 more

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          • String = String

            A text input to the model.

          • class ResponseInputText

            A text input to the model.

            • text: String

              The text input to the model.

            • type: :input_text

              The type of the input item. Always input_text.

              • :input_text

            • prompt_cache_breakpoint: PromptCacheBreakpoint{ mode}

              Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

              • mode: :explicit

                The breakpoint mode. Always explicit.

                • :explicit

          • class OutputText

            A text output from the model.

            • text: String

              The text output from the model.

            • type: :output_text

              The type of the output text. Always output_text.

              • :output_text

          • class InputImage

            An image input block used within EvalItem content arrays.

            • image_url: String

              The URL of the image input.

            • type: :input_image

              The type of the image input. Always input_image.

              • :input_image

            • detail: String

              The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

          • class ResponseInputAudio

            An audio input to the model.

            • input_audio: InputAudio{ data, format_}

              • data: String

                Base64-encoded audio data.

              • format_: :mp3 | :wav

                The format of the audio data. Currently supported formats are mp3 and wav.

                • :mp3

                • :wav

            • type: :input_audio

              The type of the input item. Always input_audio.

              • :input_audio

          • GraderInputs = Array[GraderInputItem]

            A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • String = String

              A text input to the model.

            • class ResponseInputText

              A text input to the model.

            • class OutputText

              A text output from the model.

              • text: String

                The text output from the model.

              • type: :output_text

                The type of the output text. Always output_text.

                • :output_text

            • class InputImage

              An image input block used within EvalItem content arrays.

              • image_url: String

                The URL of the image input.

              • type: :input_image

                The type of the image input. Always input_image.

                • :input_image

              • detail: String

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • class ResponseInputAudio

              An audio input to the model.

        • role: :user | :assistant | :system | :developer

          The role of the message input. One of user, assistant, system, or developer.

          • :user

          • :assistant

          • :system

          • :developer

        • type: :message

          The type of the message input. Always message.

          • :message

      • model: String

        The model to use for the evaluation.

      • name: String

        The name of the grader.

      • type: :score_model

        The object type, which is always score_model.

        • :score_model

      • range: Array[Float]

        The range of the score. Defaults to [0, 1].

      • sampling_params: SamplingParams{ max_completions_tokens, reasoning_effort, seed, 2 more}

        The sampling parameters for the model.

        • max_completions_tokens: Integer

          The maximum number of tokens the grader model may generate in its response.

        • reasoning_effort: ReasoningEffort

          Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, xhigh, and max. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. Not all reasoning models support every value. See the reasoning guide for model-specific support.

          • :none

          • :minimal

          • :low

          • :medium

          • :high

          • :xhigh

          • :max

        • seed: Integer

          A seed value to initialize the randomness, during sampling.

        • temperature: Float

          A higher temperature increases randomness in the outputs.

        • top_p: Float

          An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

    • class MultiGrader

      A MultiGrader object combines the output of multiple graders to produce a single score.

      • calculate_output: String

        A formula to calculate the output based on grader results.

      • graders: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

        • class StringCheckGrader

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

        • class TextSimilarityGrader

          A TextSimilarityGrader object which grades text based on similarity metrics.

        • class PythonGrader

          A PythonGrader object that runs a python script on the input.

        • class ScoreModelGrader

          A ScoreModelGrader object that uses a model to assign a score to the input.

        • class LabelModelGrader

          A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

          • input: Array[Input{ content, role, type}]

            • content: String | ResponseInputText | OutputText{ text, type} | 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • String = String

                A text input to the model.

              • class ResponseInputText

                A text input to the model.

              • class OutputText

                A text output from the model.

                • text: String

                  The text output from the model.

                • type: :output_text

                  The type of the output text. Always output_text.

                  • :output_text

              • class InputImage

                An image input block used within EvalItem content arrays.

                • image_url: String

                  The URL of the image input.

                • type: :input_image

                  The type of the image input. Always input_image.

                  • :input_image

                • detail: String

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • class ResponseInputAudio

                An audio input to the model.

              • GraderInputs = Array[GraderInputItem]

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • role: :user | :assistant | :system | :developer

              The role of the message input. One of user, assistant, system, or developer.

              • :user

              • :assistant

              • :system

              • :developer

            • type: :message

              The type of the message input. Always message.

              • :message

          • labels: Array[String]

            The labels to assign to each item in the evaluation.

          • model: String

            The model to use for the evaluation. Must support structured outputs.

          • name: String

            The name of the grader.

          • passing_labels: Array[String]

            The labels that indicate a passing result. Must be a subset of labels.

          • type: :label_model

            The object type, which is always label_model.

            • :label_model

      • name: String

        The name of the grader.

      • type: :multi

        The object type, which is always multi.

        • :multi

  • hyperparameters: ReinforcementHyperparameters

    The hyperparameters used for the reinforcement fine-tuning job.

    • batch_size: :auto | Integer

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • BatchSize = :auto

        • :auto

      • Integer = Integer

    • compute_multiplier: :auto | Float

      Multiplier on amount of compute used for exploring search space during training.

      • ComputeMultiplier = :auto

        • :auto

      • Float = Float

    • eval_interval: :auto | Integer

      The number of training steps between evaluation runs.

      • EvalInterval = :auto

        • :auto

      • Integer = Integer

    • eval_samples: :auto | Integer

      Number of evaluation samples to generate per training step.

      • EvalSamples = :auto

        • :auto

      • Integer = Integer

    • learning_rate_multiplier: :auto | Float

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • LearningRateMultiplier = :auto

        • :auto

      • Float = Float

    • n_epochs: :auto | Integer

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • NEpochs = :auto

        • :auto

      • Integer = Integer

    • reasoning_effort: :default | :low | :medium | :high

      Level of reasoning effort.

      • :default

      • :low

      • :medium

      • :high

Supervised Hyperparameters

• class SupervisedHyperparameters

  The hyperparameters used for the fine-tuning job.

  • batch_size: :auto | Integer

    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

    • BatchSize = :auto

      • :auto

    • Integer = Integer

  • learning_rate_multiplier: :auto | Float

    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

    • LearningRateMultiplier = :auto

      • :auto

    • Float = Float

  • n_epochs: :auto | Integer

    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

    • NEpochs = :auto

      • :auto

    • Integer = Integer

Supervised Method

• class SupervisedMethod

  Configuration for the supervised fine-tuning method.

  • hyperparameters: SupervisedHyperparameters

    The hyperparameters used for the fine-tuning job.

    • batch_size: :auto | Integer

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • BatchSize = :auto

        • :auto

      • Integer = Integer

    • learning_rate_multiplier: :auto | Float

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • LearningRateMultiplier = :auto

        • :auto

      • Float = Float

    • n_epochs: :auto | Integer

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • NEpochs = :auto

        • :auto

      • Integer = Integer

Jobs

Create fine-tuning job

fine_tuning.jobs.create(**kwargs) -> FineTuningJob

post /fine_tuning/jobs

Creates a fine-tuning job which begins the process of creating a new model from a given dataset.

Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.

Learn more about fine-tuning

Parameters

• model: String | :"babbage-002" | :"davinci-002" | :"gpt-3.5-turbo" | :"gpt-4o-mini"

  The name of the model to fine-tune. You can select one of the supported models.

  • String = String

  • Model = :"babbage-002" | :"davinci-002" | :"gpt-3.5-turbo" | :"gpt-4o-mini"

    The name of the model to fine-tune. You can select one of the supported models.

    • :"babbage-002"

    • :"davinci-002"

    • :"gpt-3.5-turbo"

    • :"gpt-4o-mini"

• training_file: String

  The ID of an uploaded file that contains training data.

  See upload file for how to upload a file.

  Your dataset must be formatted as a JSONL file. Additionally, you must upload your file with the purpose fine-tune.

  The contents of the file should differ depending on if the model uses the chat, completions format, or if the fine-tuning method uses the preference format.

  See the fine-tuning guide for more details.

• hyperparameters: Hyperparameters{ batch_size, learning_rate_multiplier, n_epochs}

  The hyperparameters used for the fine-tuning job. This value is now deprecated in favor of method, and should be passed in under the method parameter.

  • batch_size: :auto | Integer

    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

    • BatchSize = :auto

      • :auto

    • Integer = Integer

  • learning_rate_multiplier: :auto | Float

    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

    • LearningRateMultiplier = :auto

      • :auto

    • Float = Float

  • n_epochs: :auto | Integer

    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

    • NEpochs = :auto

      • :auto

    • Integer = Integer

• integrations: Array[Integration{ type, wandb}]

  A list of integrations to enable for your fine-tuning job.

  • type: :wandb

    The type of integration to enable. Currently, only "wandb" (Weights and Biases) is supported.

    • :wandb

  • wandb: Wandb{ project, entity, name, tags}

    The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. 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 be associated with your run.

    • project: String

      The name of the project that the new run will be created under.

    • entity: String

      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 run. If not set, the default entity for the registered WandB API key is used.

    • name: String

      A display name to set for the run. If not set, we will use the Job ID as the name.

    • tags: Array[String]

      A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

• metadata: 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.

• method_: Method{ type, dpo, reinforcement, supervised}

  The method used for fine-tuning.

  • type: :supervised | :dpo | :reinforcement

    The type of method. Is either supervised, dpo, or reinforcement.

    • :supervised

    • :dpo

    • :reinforcement

  • dpo: DpoMethod

    Configuration for the DPO fine-tuning method.

    • hyperparameters: DpoHyperparameters

      The hyperparameters used for the DPO fine-tuning job.

      • batch_size: :auto | Integer

        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

        • BatchSize = :auto

          • :auto

        • Integer = Integer

      • beta: :auto | Float

        The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

        • Beta = :auto

          • :auto

        • Float = Float

      • learning_rate_multiplier: :auto | Float

        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

        • LearningRateMultiplier = :auto

          • :auto

        • Float = Float

      • n_epochs: :auto | Integer

        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

        • NEpochs = :auto

          • :auto

        • Integer = Integer

  • reinforcement: ReinforcementMethod

    Configuration for the reinforcement fine-tuning method.

    • grader: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

      The grader used for the fine-tuning job.

      • class StringCheckGrader

        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

        • input: String

          The input text. This may include template strings.

        • name: String

          The name of the grader.

        • operation: :eq | :ne | :like | :ilike

          The string check operation to perform. One of eq, ne, like, or ilike.

          • :eq

          • :ne

          • :like

          • :ilike

        • reference: String

          The reference text. This may include template strings.

        • type: :string_check

          The object type, which is always string_check.

          • :string_check

      • class TextSimilarityGrader

        A TextSimilarityGrader object which grades text based on similarity metrics.

        • evaluation_metric: :cosine | :fuzzy_match | :bleu | 8 more

          The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

          • :cosine

          • :fuzzy_match

          • :bleu

          • :gleu

          • :meteor

          • :rouge_1

          • :rouge_2

          • :rouge_3

          • :rouge_4

          • :rouge_5

          • :rouge_l

        • input: String

          The text being graded.

        • name: String

          The name of the grader.

        • reference: String

          The text being graded against.

        • type: :text_similarity

          The type of grader.

          • :text_similarity

      • class PythonGrader

        A PythonGrader object that runs a python script on the input.

        • name: String

          The name of the grader.

        • source: String

          The source code of the python script.

        • type: :python

          The object type, which is always python.

          • :python

        • image_tag: String

          The image tag to use for the python script.

      • class ScoreModelGrader

        A ScoreModelGrader object that uses a model to assign a score to the input.

        • input: Array[Input{ content, role, type}]

          The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

          • content: String | ResponseInputText | OutputText{ text, type} | 3 more

            Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

            • String = String

              A text input to the model.

            • class ResponseInputText

              A text input to the model.

              • text: String

                The text input to the model.

              • type: :input_text

                The type of the input item. Always input_text.

                • :input_text

              • prompt_cache_breakpoint: PromptCacheBreakpoint{ mode}

                Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

                • mode: :explicit

                  The breakpoint mode. Always explicit.

                  • :explicit

            • class OutputText

              A text output from the model.

              • text: String

                The text output from the model.

              • type: :output_text

                The type of the output text. Always output_text.

                • :output_text

            • class InputImage

              An image input block used within EvalItem content arrays.

              • image_url: String

                The URL of the image input.

              • type: :input_image

                The type of the image input. Always input_image.

                • :input_image

              • detail: String

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • class ResponseInputAudio

              An audio input to the model.

              • input_audio: InputAudio{ data, format_}

                • data: String

                  Base64-encoded audio data.

                • format_: :mp3 | :wav

                  The format of the audio data. Currently supported formats are mp3 and wav.

                  • :mp3

                  • :wav

              • type: :input_audio

                The type of the input item. Always input_audio.

                • :input_audio

            • GraderInputs = Array[GraderInputItem]

              A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

              • String = String

                A text input to the model.

              • class ResponseInputText

                A text input to the model.

              • class OutputText

                A text output from the model.

                • text: String

                  The text output from the model.

                • type: :output_text

                  The type of the output text. Always output_text.

                  • :output_text

              • class InputImage

                An image input block used within EvalItem content arrays.

                • image_url: String

                  The URL of the image input.

                • type: :input_image

                  The type of the image input. Always input_image.

                  • :input_image

                • detail: String

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • class ResponseInputAudio

                An audio input to the model.

          • role: :user | :assistant | :system | :developer

            The role of the message input. One of user, assistant, system, or developer.

            • :user

            • :assistant

            • :system

            • :developer

          • type: :message

            The type of the message input. Always message.

            • :message

        • model: String

          The model to use for the evaluation.

        • name: String

          The name of the grader.

        • type: :score_model

          The object type, which is always score_model.

          • :score_model

        • range: Array[Float]

          The range of the score. Defaults to [0, 1].

        • sampling_params: SamplingParams{ max_completions_tokens, reasoning_effort, seed, 2 more}

          The sampling parameters for the model.

          • max_completions_tokens: Integer

            The maximum number of tokens the grader model may generate in its response.

          • reasoning_effort: ReasoningEffort

            Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, xhigh, and max. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. Not all reasoning models support every value. See the reasoning guide for model-specific support.

            • :none

            • :minimal

            • :low

            • :medium

            • :high

            • :xhigh

            • :max

          • seed: Integer

            A seed value to initialize the randomness, during sampling.

          • temperature: Float

            A higher temperature increases randomness in the outputs.

          • top_p: Float

            An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

      • class MultiGrader

        A MultiGrader object combines the output of multiple graders to produce a single score.

        • calculate_output: String

          A formula to calculate the output based on grader results.

        • graders: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • class StringCheckGrader

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • class TextSimilarityGrader

            A TextSimilarityGrader object which grades text based on similarity metrics.

          • class PythonGrader

            A PythonGrader object that runs a python script on the input.

          • class ScoreModelGrader

            A ScoreModelGrader object that uses a model to assign a score to the input.

          • class LabelModelGrader

            A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

            • input: Array[Input{ content, role, type}]

              • content: String | ResponseInputText | OutputText{ text, type} | 3 more

                Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                • String = String

                  A text input to the model.

                • class ResponseInputText

                  A text input to the model.

                • class OutputText

                  A text output from the model.

                  • text: String

                    The text output from the model.

                  • type: :output_text

                    The type of the output text. Always output_text.

                    • :output_text

                • class InputImage

                  An image input block used within EvalItem content arrays.

                  • image_url: String

                    The URL of the image input.

                  • type: :input_image

                    The type of the image input. Always input_image.

                    • :input_image

                  • detail: String

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • class ResponseInputAudio

                  An audio input to the model.

                • GraderInputs = Array[GraderInputItem]

                  A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

              • role: :user | :assistant | :system | :developer

                The role of the message input. One of user, assistant, system, or developer.

                • :user

                • :assistant

                • :system

                • :developer

              • type: :message

                The type of the message input. Always message.

                • :message

            • labels: Array[String]

              The labels to assign to each item in the evaluation.

            • model: String

              The model to use for the evaluation. Must support structured outputs.

            • name: String

              The name of the grader.

            • passing_labels: Array[String]

              The labels that indicate a passing result. Must be a subset of labels.

            • type: :label_model

              The object type, which is always label_model.

              • :label_model

        • name: String

          The name of the grader.

        • type: :multi

          The object type, which is always multi.

          • :multi

    • hyperparameters: ReinforcementHyperparameters

      The hyperparameters used for the reinforcement fine-tuning job.

      • batch_size: :auto | Integer

        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

        • BatchSize = :auto

          • :auto

        • Integer = Integer

      • compute_multiplier: :auto | Float

        Multiplier on amount of compute used for exploring search space during training.

        • ComputeMultiplier = :auto

          • :auto

        • Float = Float

      • eval_interval: :auto | Integer

        The number of training steps between evaluation runs.

        • EvalInterval = :auto

          • :auto

        • Integer = Integer

      • eval_samples: :auto | Integer

        Number of evaluation samples to generate per training step.

        • EvalSamples = :auto

          • :auto

        • Integer = Integer

      • learning_rate_multiplier: :auto | Float

        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

        • LearningRateMultiplier = :auto

          • :auto

        • Float = Float

      • n_epochs: :auto | Integer

        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

        • NEpochs = :auto

          • :auto

        • Integer = Integer

      • reasoning_effort: :default | :low | :medium | :high

        Level of reasoning effort.

        • :default

        • :low

        • :medium

        • :high

  • supervised: SupervisedMethod

    Configuration for the supervised fine-tuning method.

    • hyperparameters: SupervisedHyperparameters

      The hyperparameters used for the fine-tuning job.

      • batch_size: :auto | Integer

        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

        • BatchSize = :auto

          • :auto

        • Integer = Integer

      • learning_rate_multiplier: :auto | Float

        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

        • LearningRateMultiplier = :auto

          • :auto

        • Float = Float

      • n_epochs: :auto | Integer

        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

        • NEpochs = :auto

          • :auto

        • Integer = Integer

• seed: Integer

  The seed controls the reproducibility of the job. Passing in the same seed and job parameters should produce the same results, but may differ in rare cases. If a seed is not specified, one will be generated for you.

• suffix: String

  A string of up to 64 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 ft:gpt-4o-mini:openai:custom-model-name:7p4lURel.

• validation_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. The same data should not be present in both train and validation files.

  Your dataset must be formatted as a JSONL file. You must upload your file with the purpose fine-tune.

  See the fine-tuning guide for more details.

Returns

• class FineTuningJob

  The fine_tuning.job object represents a fine-tuning job that has been created through the API.

  • id: String

    The object identifier, which can be referenced in the API endpoints.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • error: Error{ code, message, param}

    For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

    • code: String

      A machine-readable error code.

    • message: String

      A human-readable error message.

    • param: String

      The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

  • fine_tuned_model: String

    The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

  • finished_at: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

  • hyperparameters: Hyperparameters{ batch_size, learning_rate_multiplier, n_epochs}

    The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

    • batch_size: :auto | Integer

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • BatchSize = :auto

        • :auto

      • Integer = Integer

    • learning_rate_multiplier: :auto | Float

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • LearningRateMultiplier = :auto

        • :auto

      • Float = Float

    • n_epochs: :auto | Integer

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • NEpochs = :auto

        • :auto

      • Integer = Integer

  • model: String

    The base model that is being fine-tuned.

  • object: :"fine_tuning.job"

    The object type, which is always "fine_tuning.job".

    • :"fine_tuning.job"

  • organization_id: String

    The organization that owns the fine-tuning job.

  • result_files: Array[String]

    The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

  • seed: Integer

    The seed used for the fine-tuning job.

  • status: :validating_files | :queued | :running | 3 more

    The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

    • :validating_files

    • :queued

    • :running

    • :succeeded

    • :failed

    • :cancelled

  • trained_tokens: Integer

    The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

  • training_file: String

    The file ID used for training. You can retrieve the training data with the Files API.

  • validation_file: String

    The file ID used for validation. You can retrieve the validation results with the Files API.

  • estimated_finish: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

  • integrations: Array[FineTuningJobWandbIntegrationObject]

    A list of integrations to enable for this fine-tuning job.

    • type: :wandb

      The type of the integration being enabled for the fine-tuning job

      • :wandb

    • wandb: FineTuningJobWandbIntegration

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. 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 be associated with your run.

      • project: String

        The name of the project that the new run will be created under.

      • entity: String

        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 run. If not set, the default entity for the registered WandB API key is used.

      • name: String

        A display name to set for the run. If not set, we will use the Job ID as the name.

      • tags: Array[String]

        A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

  • metadata: 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.

  • method_: Method{ type, dpo, reinforcement, supervised}

    The method used for fine-tuning.

    • type: :supervised | :dpo | :reinforcement

      The type of method. Is either supervised, dpo, or reinforcement.

      • :supervised

      • :dpo

      • :reinforcement

    • dpo: DpoMethod

      Configuration for the DPO fine-tuning method.

      • hyperparameters: DpoHyperparameters

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • beta: :auto | Float

          The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

          • Beta = :auto

            • :auto

          • Float = Float

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

    • reinforcement: ReinforcementMethod

      Configuration for the reinforcement fine-tuning method.

      • grader: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

        The grader used for the fine-tuning job.

        • class StringCheckGrader

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • input: String

            The input text. This may include template strings.

          • name: String

            The name of the grader.

          • operation: :eq | :ne | :like | :ilike

            The string check operation to perform. One of eq, ne, like, or ilike.

            • :eq

            • :ne

            • :like

            • :ilike

          • reference: String

            The reference text. This may include template strings.

          • type: :string_check

            The object type, which is always string_check.

            • :string_check

        • class TextSimilarityGrader

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: :cosine | :fuzzy_match | :bleu | 8 more

            The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

            • :cosine

            • :fuzzy_match

            • :bleu

            • :gleu

            • :meteor

            • :rouge_1

            • :rouge_2

            • :rouge_3

            • :rouge_4

            • :rouge_5

            • :rouge_l

          • input: String

            The text being graded.

          • name: String

            The name of the grader.

          • reference: String

            The text being graded against.

          • type: :text_similarity

            The type of grader.

            • :text_similarity

        • class PythonGrader

          A PythonGrader object that runs a python script on the input.

          • name: String

            The name of the grader.

          • source: String

            The source code of the python script.

          • type: :python

            The object type, which is always python.

            • :python

          • image_tag: String

            The image tag to use for the python script.

        • class ScoreModelGrader

          A ScoreModelGrader object that uses a model to assign a score to the input.

          • input: Array[Input{ content, role, type}]

            The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

            • content: String | ResponseInputText | OutputText{ text, type} | 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • String = String

                A text input to the model.

              • class ResponseInputText

                A text input to the model.

                • text: String

                  The text input to the model.

                • type: :input_text

                  The type of the input item. Always input_text.

                  • :input_text

                • prompt_cache_breakpoint: PromptCacheBreakpoint{ mode}

                  Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

                  • mode: :explicit

                    The breakpoint mode. Always explicit.

                    • :explicit

              • class OutputText

                A text output from the model.

                • text: String

                  The text output from the model.

                • type: :output_text

                  The type of the output text. Always output_text.

                  • :output_text

              • class InputImage

                An image input block used within EvalItem content arrays.

                • image_url: String

                  The URL of the image input.

                • type: :input_image

                  The type of the image input. Always input_image.

                  • :input_image

                • detail: String

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • class ResponseInputAudio

                An audio input to the model.

                • input_audio: InputAudio{ data, format_}

                  • data: String

                    Base64-encoded audio data.

                  • format_: :mp3 | :wav

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • :mp3

                    • :wav

                • type: :input_audio

                  The type of the input item. Always input_audio.

                  • :input_audio

              • GraderInputs = Array[GraderInputItem]

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • String = String

                  A text input to the model.

                • class ResponseInputText

                  A text input to the model.

                • class OutputText

                  A text output from the model.

                  • text: String

                    The text output from the model.

                  • type: :output_text

                    The type of the output text. Always output_text.

                    • :output_text

                • class InputImage

                  An image input block used within EvalItem content arrays.

                  • image_url: String

                    The URL of the image input.

                  • type: :input_image

                    The type of the image input. Always input_image.

                    • :input_image

                  • detail: String

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • class ResponseInputAudio

                  An audio input to the model.

            • role: :user | :assistant | :system | :developer

              The role of the message input. One of user, assistant, system, or developer.

              • :user

              • :assistant

              • :system

              • :developer

            • type: :message

              The type of the message input. Always message.

              • :message

          • model: String

            The model to use for the evaluation.

          • name: String

            The name of the grader.

          • type: :score_model

            The object type, which is always score_model.

            • :score_model

          • range: Array[Float]

            The range of the score. Defaults to [0, 1].

          • sampling_params: SamplingParams{ max_completions_tokens, reasoning_effort, seed, 2 more}

            The sampling parameters for the model.

            • max_completions_tokens: Integer

              The maximum number of tokens the grader model may generate in its response.

            • reasoning_effort: ReasoningEffort

              Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, xhigh, and max. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. Not all reasoning models support every value. See the reasoning guide for model-specific support.

              • :none

              • :minimal

              • :low

              • :medium

              • :high

              • :xhigh

              • :max

            • seed: Integer

              A seed value to initialize the randomness, during sampling.

            • temperature: Float

              A higher temperature increases randomness in the outputs.

            • top_p: Float

              An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

        • class MultiGrader

          A MultiGrader object combines the output of multiple graders to produce a single score.

          • calculate_output: String

            A formula to calculate the output based on grader results.

          • graders: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • class StringCheckGrader

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • class TextSimilarityGrader

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • class PythonGrader

              A PythonGrader object that runs a python script on the input.

            • class ScoreModelGrader

              A ScoreModelGrader object that uses a model to assign a score to the input.

            • class LabelModelGrader

              A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

              • input: Array[Input{ content, role, type}]

                • content: String | ResponseInputText | OutputText{ text, type} | 3 more

                  Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                  • String = String

                    A text input to the model.

                  • class ResponseInputText

                    A text input to the model.

                  • class OutputText

                    A text output from the model.

                    • text: String

                      The text output from the model.

                    • type: :output_text

                      The type of the output text. Always output_text.

                      • :output_text

                  • class InputImage

                    An image input block used within EvalItem content arrays.

                    • image_url: String

                      The URL of the image input.

                    • type: :input_image

                      The type of the image input. Always input_image.

                      • :input_image

                    • detail: String

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • class ResponseInputAudio

                    An audio input to the model.

                  • GraderInputs = Array[GraderInputItem]

                    A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • role: :user | :assistant | :system | :developer

                  The role of the message input. One of user, assistant, system, or developer.

                  • :user

                  • :assistant

                  • :system

                  • :developer

                • type: :message

                  The type of the message input. Always message.

                  • :message

              • labels: Array[String]

                The labels to assign to each item in the evaluation.

              • model: String

                The model to use for the evaluation. Must support structured outputs.

              • name: String

                The name of the grader.

              • passing_labels: Array[String]

                The labels that indicate a passing result. Must be a subset of labels.

              • type: :label_model

                The object type, which is always label_model.

                • :label_model

          • name: String

            The name of the grader.

          • type: :multi

            The object type, which is always multi.

            • :multi

      • hyperparameters: ReinforcementHyperparameters

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • compute_multiplier: :auto | Float

          Multiplier on amount of compute used for exploring search space during training.

          • ComputeMultiplier = :auto

            • :auto

          • Float = Float

        • eval_interval: :auto | Integer

          The number of training steps between evaluation runs.

          • EvalInterval = :auto

            • :auto

          • Integer = Integer

        • eval_samples: :auto | Integer

          Number of evaluation samples to generate per training step.

          • EvalSamples = :auto

            • :auto

          • Integer = Integer

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

        • reasoning_effort: :default | :low | :medium | :high

          Level of reasoning effort.

          • :default

          • :low

          • :medium

          • :high

    • supervised: SupervisedMethod

      Configuration for the supervised fine-tuning method.

      • hyperparameters: SupervisedHyperparameters

        The hyperparameters used for the fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

Example

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

fine_tuning_job = openai.fine_tuning.jobs.create(model: :"gpt-4o-mini", training_file: "file-abc123")

puts(fine_tuning_job)

Response

{
  "id": "id",
  "created_at": 0,
  "error": {
    "code": "code",
    "message": "message",
    "param": "param"
  },
  "fine_tuned_model": "fine_tuned_model",
  "finished_at": 0,
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": "auto"
  },
  "model": "model",
  "object": "fine_tuning.job",
  "organization_id": "organization_id",
  "result_files": [
    "file-abc123"
  ],
  "seed": 0,
  "status": "validating_files",
  "trained_tokens": 0,
  "training_file": "training_file",
  "validation_file": "validation_file",
  "estimated_finish": 0,
  "integrations": [
    {
      "type": "wandb",
      "wandb": {
        "project": "my-wandb-project",
        "entity": "entity",
        "name": "name",
        "tags": [
          "custom-tag"
        ]
      }
    }
  ],
  "metadata": {
    "foo": "string"
  },
  "method": {
    "type": "supervised",
    "dpo": {
      "hyperparameters": {
        "batch_size": "auto",
        "beta": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    },
    "reinforcement": {
      "grader": {
        "input": "input",
        "name": "name",
        "operation": "eq",
        "reference": "reference",
        "type": "string_check"
      },
      "hyperparameters": {
        "batch_size": "auto",
        "compute_multiplier": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "reasoning_effort": "default"
      }
    },
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  }
}

List fine-tuning jobs

fine_tuning.jobs.list(**kwargs) -> CursorPage<FineTuningJob>

get /fine_tuning/jobs

List your organization's fine-tuning jobs

Parameters

• after: String

  Identifier for the last job from the previous pagination request.

• limit: Integer

  Number of fine-tuning jobs to retrieve.

• metadata: Hash[Symbol, String]

  Optional metadata filter. To filter, use the syntax metadata[k]=v. Alternatively, set metadata=null to indicate no metadata.

Returns

• class FineTuningJob

  The fine_tuning.job object represents a fine-tuning job that has been created through the API.

  • id: String

    The object identifier, which can be referenced in the API endpoints.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • error: Error{ code, message, param}

    For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

    • code: String

      A machine-readable error code.

    • message: String

      A human-readable error message.

    • param: String

      The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

  • fine_tuned_model: String

    The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

  • finished_at: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

  • hyperparameters: Hyperparameters{ batch_size, learning_rate_multiplier, n_epochs}

    The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

    • batch_size: :auto | Integer

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • BatchSize = :auto

        • :auto

      • Integer = Integer

    • learning_rate_multiplier: :auto | Float

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • LearningRateMultiplier = :auto

        • :auto

      • Float = Float

    • n_epochs: :auto | Integer

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • NEpochs = :auto

        • :auto

      • Integer = Integer

  • model: String

    The base model that is being fine-tuned.

  • object: :"fine_tuning.job"

    The object type, which is always "fine_tuning.job".

    • :"fine_tuning.job"

  • organization_id: String

    The organization that owns the fine-tuning job.

  • result_files: Array[String]

    The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

  • seed: Integer

    The seed used for the fine-tuning job.

  • status: :validating_files | :queued | :running | 3 more

    The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

    • :validating_files

    • :queued

    • :running

    • :succeeded

    • :failed

    • :cancelled

  • trained_tokens: Integer

    The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

  • training_file: String

    The file ID used for training. You can retrieve the training data with the Files API.

  • validation_file: String

    The file ID used for validation. You can retrieve the validation results with the Files API.

  • estimated_finish: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

  • integrations: Array[FineTuningJobWandbIntegrationObject]

    A list of integrations to enable for this fine-tuning job.

    • type: :wandb

      The type of the integration being enabled for the fine-tuning job

      • :wandb

    • wandb: FineTuningJobWandbIntegration

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. 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 be associated with your run.

      • project: String

        The name of the project that the new run will be created under.

      • entity: String

        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 run. If not set, the default entity for the registered WandB API key is used.

      • name: String

        A display name to set for the run. If not set, we will use the Job ID as the name.

      • tags: Array[String]

        A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

  • metadata: 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.

  • method_: Method{ type, dpo, reinforcement, supervised}

    The method used for fine-tuning.

    • type: :supervised | :dpo | :reinforcement

      The type of method. Is either supervised, dpo, or reinforcement.

      • :supervised

      • :dpo

      • :reinforcement

    • dpo: DpoMethod

      Configuration for the DPO fine-tuning method.

      • hyperparameters: DpoHyperparameters

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • beta: :auto | Float

          The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

          • Beta = :auto

            • :auto

          • Float = Float

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

    • reinforcement: ReinforcementMethod

      Configuration for the reinforcement fine-tuning method.

      • grader: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

        The grader used for the fine-tuning job.

        • class StringCheckGrader

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • input: String

            The input text. This may include template strings.

          • name: String

            The name of the grader.

          • operation: :eq | :ne | :like | :ilike

            The string check operation to perform. One of eq, ne, like, or ilike.

            • :eq

            • :ne

            • :like

            • :ilike

          • reference: String

            The reference text. This may include template strings.

          • type: :string_check

            The object type, which is always string_check.

            • :string_check

        • class TextSimilarityGrader

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: :cosine | :fuzzy_match | :bleu | 8 more

            The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

            • :cosine

            • :fuzzy_match

            • :bleu

            • :gleu

            • :meteor

            • :rouge_1

            • :rouge_2

            • :rouge_3

            • :rouge_4

            • :rouge_5

            • :rouge_l

          • input: String

            The text being graded.

          • name: String

            The name of the grader.

          • reference: String

            The text being graded against.

          • type: :text_similarity

            The type of grader.

            • :text_similarity

        • class PythonGrader

          A PythonGrader object that runs a python script on the input.

          • name: String

            The name of the grader.

          • source: String

            The source code of the python script.

          • type: :python

            The object type, which is always python.

            • :python

          • image_tag: String

            The image tag to use for the python script.

        • class ScoreModelGrader

          A ScoreModelGrader object that uses a model to assign a score to the input.

          • input: Array[Input{ content, role, type}]

            The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

            • content: String | ResponseInputText | OutputText{ text, type} | 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • String = String

                A text input to the model.

              • class ResponseInputText

                A text input to the model.

                • text: String

                  The text input to the model.

                • type: :input_text

                  The type of the input item. Always input_text.

                  • :input_text

                • prompt_cache_breakpoint: PromptCacheBreakpoint{ mode}

                  Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

                  • mode: :explicit

                    The breakpoint mode. Always explicit.

                    • :explicit

              • class OutputText

                A text output from the model.

                • text: String

                  The text output from the model.

                • type: :output_text

                  The type of the output text. Always output_text.

                  • :output_text

              • class InputImage

                An image input block used within EvalItem content arrays.

                • image_url: String

                  The URL of the image input.

                • type: :input_image

                  The type of the image input. Always input_image.

                  • :input_image

                • detail: String

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • class ResponseInputAudio

                An audio input to the model.

                • input_audio: InputAudio{ data, format_}

                  • data: String

                    Base64-encoded audio data.

                  • format_: :mp3 | :wav

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • :mp3

                    • :wav

                • type: :input_audio

                  The type of the input item. Always input_audio.

                  • :input_audio

              • GraderInputs = Array[GraderInputItem]

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • String = String

                  A text input to the model.

                • class ResponseInputText

                  A text input to the model.

                • class OutputText

                  A text output from the model.

                  • text: String

                    The text output from the model.

                  • type: :output_text

                    The type of the output text. Always output_text.

                    • :output_text

                • class InputImage

                  An image input block used within EvalItem content arrays.

                  • image_url: String

                    The URL of the image input.

                  • type: :input_image

                    The type of the image input. Always input_image.

                    • :input_image

                  • detail: String

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • class ResponseInputAudio

                  An audio input to the model.

            • role: :user | :assistant | :system | :developer

              The role of the message input. One of user, assistant, system, or developer.

              • :user

              • :assistant

              • :system

              • :developer

            • type: :message

              The type of the message input. Always message.

              • :message

          • model: String

            The model to use for the evaluation.

          • name: String

            The name of the grader.

          • type: :score_model

            The object type, which is always score_model.

            • :score_model

          • range: Array[Float]

            The range of the score. Defaults to [0, 1].

          • sampling_params: SamplingParams{ max_completions_tokens, reasoning_effort, seed, 2 more}

            The sampling parameters for the model.

            • max_completions_tokens: Integer

              The maximum number of tokens the grader model may generate in its response.

            • reasoning_effort: ReasoningEffort

              Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, xhigh, and max. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. Not all reasoning models support every value. See the reasoning guide for model-specific support.

              • :none

              • :minimal

              • :low

              • :medium

              • :high

              • :xhigh

              • :max

            • seed: Integer

              A seed value to initialize the randomness, during sampling.

            • temperature: Float

              A higher temperature increases randomness in the outputs.

            • top_p: Float

              An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

        • class MultiGrader

          A MultiGrader object combines the output of multiple graders to produce a single score.

          • calculate_output: String

            A formula to calculate the output based on grader results.

          • graders: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • class StringCheckGrader

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • class TextSimilarityGrader

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • class PythonGrader

              A PythonGrader object that runs a python script on the input.

            • class ScoreModelGrader

              A ScoreModelGrader object that uses a model to assign a score to the input.

            • class LabelModelGrader

              A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

              • input: Array[Input{ content, role, type}]

                • content: String | ResponseInputText | OutputText{ text, type} | 3 more

                  Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                  • String = String

                    A text input to the model.

                  • class ResponseInputText

                    A text input to the model.

                  • class OutputText

                    A text output from the model.

                    • text: String

                      The text output from the model.

                    • type: :output_text

                      The type of the output text. Always output_text.

                      • :output_text

                  • class InputImage

                    An image input block used within EvalItem content arrays.

                    • image_url: String

                      The URL of the image input.

                    • type: :input_image

                      The type of the image input. Always input_image.

                      • :input_image

                    • detail: String

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • class ResponseInputAudio

                    An audio input to the model.

                  • GraderInputs = Array[GraderInputItem]

                    A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • role: :user | :assistant | :system | :developer

                  The role of the message input. One of user, assistant, system, or developer.

                  • :user

                  • :assistant

                  • :system

                  • :developer

                • type: :message

                  The type of the message input. Always message.

                  • :message

              • labels: Array[String]

                The labels to assign to each item in the evaluation.

              • model: String

                The model to use for the evaluation. Must support structured outputs.

              • name: String

                The name of the grader.

              • passing_labels: Array[String]

                The labels that indicate a passing result. Must be a subset of labels.

              • type: :label_model

                The object type, which is always label_model.

                • :label_model

          • name: String

            The name of the grader.

          • type: :multi

            The object type, which is always multi.

            • :multi

      • hyperparameters: ReinforcementHyperparameters

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • compute_multiplier: :auto | Float

          Multiplier on amount of compute used for exploring search space during training.

          • ComputeMultiplier = :auto

            • :auto

          • Float = Float

        • eval_interval: :auto | Integer

          The number of training steps between evaluation runs.

          • EvalInterval = :auto

            • :auto

          • Integer = Integer

        • eval_samples: :auto | Integer

          Number of evaluation samples to generate per training step.

          • EvalSamples = :auto

            • :auto

          • Integer = Integer

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

        • reasoning_effort: :default | :low | :medium | :high

          Level of reasoning effort.

          • :default

          • :low

          • :medium

          • :high

    • supervised: SupervisedMethod

      Configuration for the supervised fine-tuning method.

      • hyperparameters: SupervisedHyperparameters

        The hyperparameters used for the fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

Example

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

page = openai.fine_tuning.jobs.list

puts(page)

Response

{
  "data": [
    {
      "id": "id",
      "created_at": 0,
      "error": {
        "code": "code",
        "message": "message",
        "param": "param"
      },
      "fine_tuned_model": "fine_tuned_model",
      "finished_at": 0,
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      },
      "model": "model",
      "object": "fine_tuning.job",
      "organization_id": "organization_id",
      "result_files": [
        "file-abc123"
      ],
      "seed": 0,
      "status": "validating_files",
      "trained_tokens": 0,
      "training_file": "training_file",
      "validation_file": "validation_file",
      "estimated_finish": 0,
      "integrations": [
        {
          "type": "wandb",
          "wandb": {
            "project": "my-wandb-project",
            "entity": "entity",
            "name": "name",
            "tags": [
              "custom-tag"
            ]
          }
        }
      ],
      "metadata": {
        "foo": "string"
      },
      "method": {
        "type": "supervised",
        "dpo": {
          "hyperparameters": {
            "batch_size": "auto",
            "beta": "auto",
            "learning_rate_multiplier": "auto",
            "n_epochs": "auto"
          }
        },
        "reinforcement": {
          "grader": {
            "input": "input",
            "name": "name",
            "operation": "eq",
            "reference": "reference",
            "type": "string_check"
          },
          "hyperparameters": {
            "batch_size": "auto",
            "compute_multiplier": "auto",
            "eval_interval": "auto",
            "eval_samples": "auto",
            "learning_rate_multiplier": "auto",
            "n_epochs": "auto",
            "reasoning_effort": "default"
          }
        },
        "supervised": {
          "hyperparameters": {
            "batch_size": "auto",
            "learning_rate_multiplier": "auto",
            "n_epochs": "auto"
          }
        }
      }
    }
  ],
  "has_more": true,
  "object": "list"
}

Retrieve fine-tuning job

fine_tuning.jobs.retrieve(fine_tuning_job_id) -> FineTuningJob

get /fine_tuning/jobs/{fine_tuning_job_id}

Get info about a fine-tuning job.

Learn more about fine-tuning

Parameters

• fine_tuning_job_id: String

Returns

• class FineTuningJob

  The fine_tuning.job object represents a fine-tuning job that has been created through the API.

  • id: String

    The object identifier, which can be referenced in the API endpoints.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • error: Error{ code, message, param}

    For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

    • code: String

      A machine-readable error code.

    • message: String

      A human-readable error message.

    • param: String

      The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

  • fine_tuned_model: String

    The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

  • finished_at: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

  • hyperparameters: Hyperparameters{ batch_size, learning_rate_multiplier, n_epochs}

    The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

    • batch_size: :auto | Integer

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • BatchSize = :auto

        • :auto

      • Integer = Integer

    • learning_rate_multiplier: :auto | Float

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • LearningRateMultiplier = :auto

        • :auto

      • Float = Float

    • n_epochs: :auto | Integer

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • NEpochs = :auto

        • :auto

      • Integer = Integer

  • model: String

    The base model that is being fine-tuned.

  • object: :"fine_tuning.job"

    The object type, which is always "fine_tuning.job".

    • :"fine_tuning.job"

  • organization_id: String

    The organization that owns the fine-tuning job.

  • result_files: Array[String]

    The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

  • seed: Integer

    The seed used for the fine-tuning job.

  • status: :validating_files | :queued | :running | 3 more

    The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

    • :validating_files

    • :queued

    • :running

    • :succeeded

    • :failed

    • :cancelled

  • trained_tokens: Integer

    The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

  • training_file: String

    The file ID used for training. You can retrieve the training data with the Files API.

  • validation_file: String

    The file ID used for validation. You can retrieve the validation results with the Files API.

  • estimated_finish: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

  • integrations: Array[FineTuningJobWandbIntegrationObject]

    A list of integrations to enable for this fine-tuning job.

    • type: :wandb

      The type of the integration being enabled for the fine-tuning job

      • :wandb

    • wandb: FineTuningJobWandbIntegration

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. 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 be associated with your run.

      • project: String

        The name of the project that the new run will be created under.

      • entity: String

        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 run. If not set, the default entity for the registered WandB API key is used.

      • name: String

        A display name to set for the run. If not set, we will use the Job ID as the name.

      • tags: Array[String]

        A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

  • metadata: 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.

  • method_: Method{ type, dpo, reinforcement, supervised}

    The method used for fine-tuning.

    • type: :supervised | :dpo | :reinforcement

      The type of method. Is either supervised, dpo, or reinforcement.

      • :supervised

      • :dpo

      • :reinforcement

    • dpo: DpoMethod

      Configuration for the DPO fine-tuning method.

      • hyperparameters: DpoHyperparameters

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • beta: :auto | Float

          The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

          • Beta = :auto

            • :auto

          • Float = Float

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

    • reinforcement: ReinforcementMethod

      Configuration for the reinforcement fine-tuning method.

      • grader: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

        The grader used for the fine-tuning job.

        • class StringCheckGrader

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • input: String

            The input text. This may include template strings.

          • name: String

            The name of the grader.

          • operation: :eq | :ne | :like | :ilike

            The string check operation to perform. One of eq, ne, like, or ilike.

            • :eq

            • :ne

            • :like

            • :ilike

          • reference: String

            The reference text. This may include template strings.

          • type: :string_check

            The object type, which is always string_check.

            • :string_check

        • class TextSimilarityGrader

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: :cosine | :fuzzy_match | :bleu | 8 more

            The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

            • :cosine

            • :fuzzy_match

            • :bleu

            • :gleu

            • :meteor

            • :rouge_1

            • :rouge_2

            • :rouge_3

            • :rouge_4

            • :rouge_5

            • :rouge_l

          • input: String

            The text being graded.

          • name: String

            The name of the grader.

          • reference: String

            The text being graded against.

          • type: :text_similarity

            The type of grader.

            • :text_similarity

        • class PythonGrader

          A PythonGrader object that runs a python script on the input.

          • name: String

            The name of the grader.

          • source: String

            The source code of the python script.

          • type: :python

            The object type, which is always python.

            • :python

          • image_tag: String

            The image tag to use for the python script.

        • class ScoreModelGrader

          A ScoreModelGrader object that uses a model to assign a score to the input.

          • input: Array[Input{ content, role, type}]

            The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

            • content: String | ResponseInputText | OutputText{ text, type} | 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • String = String

                A text input to the model.

              • class ResponseInputText

                A text input to the model.

                • text: String

                  The text input to the model.

                • type: :input_text

                  The type of the input item. Always input_text.

                  • :input_text

                • prompt_cache_breakpoint: PromptCacheBreakpoint{ mode}

                  Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

                  • mode: :explicit

                    The breakpoint mode. Always explicit.

                    • :explicit

              • class OutputText

                A text output from the model.

                • text: String

                  The text output from the model.

                • type: :output_text

                  The type of the output text. Always output_text.

                  • :output_text

              • class InputImage

                An image input block used within EvalItem content arrays.

                • image_url: String

                  The URL of the image input.

                • type: :input_image

                  The type of the image input. Always input_image.

                  • :input_image

                • detail: String

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • class ResponseInputAudio

                An audio input to the model.

                • input_audio: InputAudio{ data, format_}

                  • data: String

                    Base64-encoded audio data.

                  • format_: :mp3 | :wav

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • :mp3

                    • :wav

                • type: :input_audio

                  The type of the input item. Always input_audio.

                  • :input_audio

              • GraderInputs = Array[GraderInputItem]

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • String = String

                  A text input to the model.

                • class ResponseInputText

                  A text input to the model.

                • class OutputText

                  A text output from the model.

                  • text: String

                    The text output from the model.

                  • type: :output_text

                    The type of the output text. Always output_text.

                    • :output_text

                • class InputImage

                  An image input block used within EvalItem content arrays.

                  • image_url: String

                    The URL of the image input.

                  • type: :input_image

                    The type of the image input. Always input_image.

                    • :input_image

                  • detail: String

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • class ResponseInputAudio

                  An audio input to the model.

            • role: :user | :assistant | :system | :developer

              The role of the message input. One of user, assistant, system, or developer.

              • :user

              • :assistant

              • :system

              • :developer

            • type: :message

              The type of the message input. Always message.

              • :message

          • model: String

            The model to use for the evaluation.

          • name: String

            The name of the grader.

          • type: :score_model

            The object type, which is always score_model.

            • :score_model

          • range: Array[Float]

            The range of the score. Defaults to [0, 1].

          • sampling_params: SamplingParams{ max_completions_tokens, reasoning_effort, seed, 2 more}

            The sampling parameters for the model.

            • max_completions_tokens: Integer

              The maximum number of tokens the grader model may generate in its response.

            • reasoning_effort: ReasoningEffort

              Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, xhigh, and max. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. Not all reasoning models support every value. See the reasoning guide for model-specific support.

              • :none

              • :minimal

              • :low

              • :medium

              • :high

              • :xhigh

              • :max

            • seed: Integer

              A seed value to initialize the randomness, during sampling.

            • temperature: Float

              A higher temperature increases randomness in the outputs.

            • top_p: Float

              An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

        • class MultiGrader

          A MultiGrader object combines the output of multiple graders to produce a single score.

          • calculate_output: String

            A formula to calculate the output based on grader results.

          • graders: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • class StringCheckGrader

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • class TextSimilarityGrader

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • class PythonGrader

              A PythonGrader object that runs a python script on the input.

            • class ScoreModelGrader

              A ScoreModelGrader object that uses a model to assign a score to the input.

            • class LabelModelGrader

              A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

              • input: Array[Input{ content, role, type}]

                • content: String | ResponseInputText | OutputText{ text, type} | 3 more

                  Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                  • String = String

                    A text input to the model.

                  • class ResponseInputText

                    A text input to the model.

                  • class OutputText

                    A text output from the model.

                    • text: String

                      The text output from the model.

                    • type: :output_text

                      The type of the output text. Always output_text.

                      • :output_text

                  • class InputImage

                    An image input block used within EvalItem content arrays.

                    • image_url: String

                      The URL of the image input.

                    • type: :input_image

                      The type of the image input. Always input_image.

                      • :input_image

                    • detail: String

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • class ResponseInputAudio

                    An audio input to the model.

                  • GraderInputs = Array[GraderInputItem]

                    A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • role: :user | :assistant | :system | :developer

                  The role of the message input. One of user, assistant, system, or developer.

                  • :user

                  • :assistant

                  • :system

                  • :developer

                • type: :message

                  The type of the message input. Always message.

                  • :message

              • labels: Array[String]

                The labels to assign to each item in the evaluation.

              • model: String

                The model to use for the evaluation. Must support structured outputs.

              • name: String

                The name of the grader.

              • passing_labels: Array[String]

                The labels that indicate a passing result. Must be a subset of labels.

              • type: :label_model

                The object type, which is always label_model.

                • :label_model

          • name: String

            The name of the grader.

          • type: :multi

            The object type, which is always multi.

            • :multi

      • hyperparameters: ReinforcementHyperparameters

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • compute_multiplier: :auto | Float

          Multiplier on amount of compute used for exploring search space during training.

          • ComputeMultiplier = :auto

            • :auto

          • Float = Float

        • eval_interval: :auto | Integer

          The number of training steps between evaluation runs.

          • EvalInterval = :auto

            • :auto

          • Integer = Integer

        • eval_samples: :auto | Integer

          Number of evaluation samples to generate per training step.

          • EvalSamples = :auto

            • :auto

          • Integer = Integer

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

        • reasoning_effort: :default | :low | :medium | :high

          Level of reasoning effort.

          • :default

          • :low

          • :medium

          • :high

    • supervised: SupervisedMethod

      Configuration for the supervised fine-tuning method.

      • hyperparameters: SupervisedHyperparameters

        The hyperparameters used for the fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

Example

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

fine_tuning_job = openai.fine_tuning.jobs.retrieve("ft-AF1WoRqd3aJAHsqc9NY7iL8F")

puts(fine_tuning_job)

Response

{
  "id": "id",
  "created_at": 0,
  "error": {
    "code": "code",
    "message": "message",
    "param": "param"
  },
  "fine_tuned_model": "fine_tuned_model",
  "finished_at": 0,
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": "auto"
  },
  "model": "model",
  "object": "fine_tuning.job",
  "organization_id": "organization_id",
  "result_files": [
    "file-abc123"
  ],
  "seed": 0,
  "status": "validating_files",
  "trained_tokens": 0,
  "training_file": "training_file",
  "validation_file": "validation_file",
  "estimated_finish": 0,
  "integrations": [
    {
      "type": "wandb",
      "wandb": {
        "project": "my-wandb-project",
        "entity": "entity",
        "name": "name",
        "tags": [
          "custom-tag"
        ]
      }
    }
  ],
  "metadata": {
    "foo": "string"
  },
  "method": {
    "type": "supervised",
    "dpo": {
      "hyperparameters": {
        "batch_size": "auto",
        "beta": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    },
    "reinforcement": {
      "grader": {
        "input": "input",
        "name": "name",
        "operation": "eq",
        "reference": "reference",
        "type": "string_check"
      },
      "hyperparameters": {
        "batch_size": "auto",
        "compute_multiplier": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "reasoning_effort": "default"
      }
    },
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  }
}

List fine-tuning events

fine_tuning.jobs.list_events(fine_tuning_job_id, **kwargs) -> CursorPage<FineTuningJobEvent>

get /fine_tuning/jobs/{fine_tuning_job_id}/events

Get status updates for a fine-tuning job.

Parameters

• fine_tuning_job_id: String

• after: String

  Identifier for the last event from the previous pagination request.

• limit: Integer

  Number of events to retrieve.

Returns

• class FineTuningJobEvent

  Fine-tuning job event object

  • id: String

    The object identifier.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • level: :info | :warn | :error

    The log level of the event.

    • :info

    • :warn

    • :error

  • message: String

    The message of the event.

  • object: :"fine_tuning.job.event"

    The object type, which is always "fine_tuning.job.event".

    • :"fine_tuning.job.event"

  • data: untyped

    The data associated with the event.

  • type: :message | :metrics

    The type of event.

    • :message

    • :metrics

Example

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

page = openai.fine_tuning.jobs.list_events("ft-AF1WoRqd3aJAHsqc9NY7iL8F")

puts(page)

Response

{
  "data": [
    {
      "id": "id",
      "created_at": 0,
      "level": "info",
      "message": "message",
      "object": "fine_tuning.job.event",
      "data": {},
      "type": "message"
    }
  ],
  "has_more": true,
  "object": "list"
}

Cancel fine-tuning

fine_tuning.jobs.cancel(fine_tuning_job_id) -> FineTuningJob

post /fine_tuning/jobs/{fine_tuning_job_id}/cancel

Immediately cancel a fine-tune job.

Parameters

• fine_tuning_job_id: String

Returns

• class FineTuningJob

  The fine_tuning.job object represents a fine-tuning job that has been created through the API.

  • id: String

    The object identifier, which can be referenced in the API endpoints.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • error: Error{ code, message, param}

    For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

    • code: String

      A machine-readable error code.

    • message: String

      A human-readable error message.

    • param: String

      The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

  • fine_tuned_model: String

    The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

  • finished_at: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

  • hyperparameters: Hyperparameters{ batch_size, learning_rate_multiplier, n_epochs}

    The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

    • batch_size: :auto | Integer

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • BatchSize = :auto

        • :auto

      • Integer = Integer

    • learning_rate_multiplier: :auto | Float

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • LearningRateMultiplier = :auto

        • :auto

      • Float = Float

    • n_epochs: :auto | Integer

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • NEpochs = :auto

        • :auto

      • Integer = Integer

  • model: String

    The base model that is being fine-tuned.

  • object: :"fine_tuning.job"

    The object type, which is always "fine_tuning.job".

    • :"fine_tuning.job"

  • organization_id: String

    The organization that owns the fine-tuning job.

  • result_files: Array[String]

    The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

  • seed: Integer

    The seed used for the fine-tuning job.

  • status: :validating_files | :queued | :running | 3 more

    The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

    • :validating_files

    • :queued

    • :running

    • :succeeded

    • :failed

    • :cancelled

  • trained_tokens: Integer

    The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

  • training_file: String

    The file ID used for training. You can retrieve the training data with the Files API.

  • validation_file: String

    The file ID used for validation. You can retrieve the validation results with the Files API.

  • estimated_finish: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

  • integrations: Array[FineTuningJobWandbIntegrationObject]

    A list of integrations to enable for this fine-tuning job.

    • type: :wandb

      The type of the integration being enabled for the fine-tuning job

      • :wandb

    • wandb: FineTuningJobWandbIntegration

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. 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 be associated with your run.

      • project: String

        The name of the project that the new run will be created under.

      • entity: String

        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 run. If not set, the default entity for the registered WandB API key is used.

      • name: String

        A display name to set for the run. If not set, we will use the Job ID as the name.

      • tags: Array[String]

        A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

  • metadata: 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.

  • method_: Method{ type, dpo, reinforcement, supervised}

    The method used for fine-tuning.

    • type: :supervised | :dpo | :reinforcement

      The type of method. Is either supervised, dpo, or reinforcement.

      • :supervised

      • :dpo

      • :reinforcement

    • dpo: DpoMethod

      Configuration for the DPO fine-tuning method.

      • hyperparameters: DpoHyperparameters

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • beta: :auto | Float

          The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

          • Beta = :auto

            • :auto

          • Float = Float

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

    • reinforcement: ReinforcementMethod

      Configuration for the reinforcement fine-tuning method.

      • grader: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

        The grader used for the fine-tuning job.

        • class StringCheckGrader

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • input: String

            The input text. This may include template strings.

          • name: String

            The name of the grader.

          • operation: :eq | :ne | :like | :ilike

            The string check operation to perform. One of eq, ne, like, or ilike.

            • :eq

            • :ne

            • :like

            • :ilike

          • reference: String

            The reference text. This may include template strings.

          • type: :string_check

            The object type, which is always string_check.

            • :string_check

        • class TextSimilarityGrader

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: :cosine | :fuzzy_match | :bleu | 8 more

            The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

            • :cosine

            • :fuzzy_match

            • :bleu

            • :gleu

            • :meteor

            • :rouge_1

            • :rouge_2

            • :rouge_3

            • :rouge_4

            • :rouge_5

            • :rouge_l

          • input: String

            The text being graded.

          • name: String

            The name of the grader.

          • reference: String

            The text being graded against.

          • type: :text_similarity

            The type of grader.

            • :text_similarity

        • class PythonGrader

          A PythonGrader object that runs a python script on the input.

          • name: String

            The name of the grader.

          • source: String

            The source code of the python script.

          • type: :python

            The object type, which is always python.

            • :python

          • image_tag: String

            The image tag to use for the python script.

        • class ScoreModelGrader

          A ScoreModelGrader object that uses a model to assign a score to the input.

          • input: Array[Input{ content, role, type}]

            The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

            • content: String | ResponseInputText | OutputText{ text, type} | 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • String = String

                A text input to the model.

              • class ResponseInputText

                A text input to the model.

                • text: String

                  The text input to the model.

                • type: :input_text

                  The type of the input item. Always input_text.

                  • :input_text

                • prompt_cache_breakpoint: PromptCacheBreakpoint{ mode}

                  Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

                  • mode: :explicit

                    The breakpoint mode. Always explicit.

                    • :explicit

              • class OutputText

                A text output from the model.

                • text: String

                  The text output from the model.

                • type: :output_text

                  The type of the output text. Always output_text.

                  • :output_text

              • class InputImage

                An image input block used within EvalItem content arrays.

                • image_url: String

                  The URL of the image input.

                • type: :input_image

                  The type of the image input. Always input_image.

                  • :input_image

                • detail: String

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • class ResponseInputAudio

                An audio input to the model.

                • input_audio: InputAudio{ data, format_}

                  • data: String

                    Base64-encoded audio data.

                  • format_: :mp3 | :wav

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • :mp3

                    • :wav

                • type: :input_audio

                  The type of the input item. Always input_audio.

                  • :input_audio

              • GraderInputs = Array[GraderInputItem]

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • String = String

                  A text input to the model.

                • class ResponseInputText

                  A text input to the model.

                • class OutputText

                  A text output from the model.

                  • text: String

                    The text output from the model.

                  • type: :output_text

                    The type of the output text. Always output_text.

                    • :output_text

                • class InputImage

                  An image input block used within EvalItem content arrays.

                  • image_url: String

                    The URL of the image input.

                  • type: :input_image

                    The type of the image input. Always input_image.

                    • :input_image

                  • detail: String

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • class ResponseInputAudio

                  An audio input to the model.

            • role: :user | :assistant | :system | :developer

              The role of the message input. One of user, assistant, system, or developer.

              • :user

              • :assistant

              • :system

              • :developer

            • type: :message

              The type of the message input. Always message.

              • :message

          • model: String

            The model to use for the evaluation.

          • name: String

            The name of the grader.

          • type: :score_model

            The object type, which is always score_model.

            • :score_model

          • range: Array[Float]

            The range of the score. Defaults to [0, 1].

          • sampling_params: SamplingParams{ max_completions_tokens, reasoning_effort, seed, 2 more}

            The sampling parameters for the model.

            • max_completions_tokens: Integer

              The maximum number of tokens the grader model may generate in its response.

            • reasoning_effort: ReasoningEffort

              Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, xhigh, and max. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. Not all reasoning models support every value. See the reasoning guide for model-specific support.

              • :none

              • :minimal

              • :low

              • :medium

              • :high

              • :xhigh

              • :max

            • seed: Integer

              A seed value to initialize the randomness, during sampling.

            • temperature: Float

              A higher temperature increases randomness in the outputs.

            • top_p: Float

              An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

        • class MultiGrader

          A MultiGrader object combines the output of multiple graders to produce a single score.

          • calculate_output: String

            A formula to calculate the output based on grader results.

          • graders: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • class StringCheckGrader

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • class TextSimilarityGrader

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • class PythonGrader

              A PythonGrader object that runs a python script on the input.

            • class ScoreModelGrader

              A ScoreModelGrader object that uses a model to assign a score to the input.

            • class LabelModelGrader

              A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

              • input: Array[Input{ content, role, type}]

                • content: String | ResponseInputText | OutputText{ text, type} | 3 more

                  Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                  • String = String

                    A text input to the model.

                  • class ResponseInputText

                    A text input to the model.

                  • class OutputText

                    A text output from the model.

                    • text: String

                      The text output from the model.

                    • type: :output_text

                      The type of the output text. Always output_text.

                      • :output_text

                  • class InputImage

                    An image input block used within EvalItem content arrays.

                    • image_url: String

                      The URL of the image input.

                    • type: :input_image

                      The type of the image input. Always input_image.

                      • :input_image

                    • detail: String

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • class ResponseInputAudio

                    An audio input to the model.

                  • GraderInputs = Array[GraderInputItem]

                    A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • role: :user | :assistant | :system | :developer

                  The role of the message input. One of user, assistant, system, or developer.

                  • :user

                  • :assistant

                  • :system

                  • :developer

                • type: :message

                  The type of the message input. Always message.

                  • :message

              • labels: Array[String]

                The labels to assign to each item in the evaluation.

              • model: String

                The model to use for the evaluation. Must support structured outputs.

              • name: String

                The name of the grader.

              • passing_labels: Array[String]

                The labels that indicate a passing result. Must be a subset of labels.

              • type: :label_model

                The object type, which is always label_model.

                • :label_model

          • name: String

            The name of the grader.

          • type: :multi

            The object type, which is always multi.

            • :multi

      • hyperparameters: ReinforcementHyperparameters

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • compute_multiplier: :auto | Float

          Multiplier on amount of compute used for exploring search space during training.

          • ComputeMultiplier = :auto

            • :auto

          • Float = Float

        • eval_interval: :auto | Integer

          The number of training steps between evaluation runs.

          • EvalInterval = :auto

            • :auto

          • Integer = Integer

        • eval_samples: :auto | Integer

          Number of evaluation samples to generate per training step.

          • EvalSamples = :auto

            • :auto

          • Integer = Integer

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

        • reasoning_effort: :default | :low | :medium | :high

          Level of reasoning effort.

          • :default

          • :low

          • :medium

          • :high

    • supervised: SupervisedMethod

      Configuration for the supervised fine-tuning method.

      • hyperparameters: SupervisedHyperparameters

        The hyperparameters used for the fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

Example

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

fine_tuning_job = openai.fine_tuning.jobs.cancel("ft-AF1WoRqd3aJAHsqc9NY7iL8F")

puts(fine_tuning_job)

Response

{
  "id": "id",
  "created_at": 0,
  "error": {
    "code": "code",
    "message": "message",
    "param": "param"
  },
  "fine_tuned_model": "fine_tuned_model",
  "finished_at": 0,
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": "auto"
  },
  "model": "model",
  "object": "fine_tuning.job",
  "organization_id": "organization_id",
  "result_files": [
    "file-abc123"
  ],
  "seed": 0,
  "status": "validating_files",
  "trained_tokens": 0,
  "training_file": "training_file",
  "validation_file": "validation_file",
  "estimated_finish": 0,
  "integrations": [
    {
      "type": "wandb",
      "wandb": {
        "project": "my-wandb-project",
        "entity": "entity",
        "name": "name",
        "tags": [
          "custom-tag"
        ]
      }
    }
  ],
  "metadata": {
    "foo": "string"
  },
  "method": {
    "type": "supervised",
    "dpo": {
      "hyperparameters": {
        "batch_size": "auto",
        "beta": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    },
    "reinforcement": {
      "grader": {
        "input": "input",
        "name": "name",
        "operation": "eq",
        "reference": "reference",
        "type": "string_check"
      },
      "hyperparameters": {
        "batch_size": "auto",
        "compute_multiplier": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "reasoning_effort": "default"
      }
    },
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  }
}

Pause fine-tuning

fine_tuning.jobs.pause(fine_tuning_job_id) -> FineTuningJob

post /fine_tuning/jobs/{fine_tuning_job_id}/pause

Pause a fine-tune job.

Parameters

• fine_tuning_job_id: String

Returns

• class FineTuningJob

  The fine_tuning.job object represents a fine-tuning job that has been created through the API.

  • id: String

    The object identifier, which can be referenced in the API endpoints.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job was created.

  • error: Error{ code, message, param}

    For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

    • code: String

      A machine-readable error code.

    • message: String

      A human-readable error message.

    • param: String

      The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.

  • fine_tuned_model: String

    The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.

  • finished_at: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.

  • hyperparameters: Hyperparameters{ batch_size, learning_rate_multiplier, n_epochs}

    The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.

    • batch_size: :auto | Integer

      Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

      • BatchSize = :auto

        • :auto

      • Integer = Integer

    • learning_rate_multiplier: :auto | Float

      Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

      • LearningRateMultiplier = :auto

        • :auto

      • Float = Float

    • n_epochs: :auto | Integer

      The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

      • NEpochs = :auto

        • :auto

      • Integer = Integer

  • model: String

    The base model that is being fine-tuned.

  • object: :"fine_tuning.job"

    The object type, which is always "fine_tuning.job".

    • :"fine_tuning.job"

  • organization_id: String

    The organization that owns the fine-tuning job.

  • result_files: Array[String]

    The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.

  • seed: Integer

    The seed used for the fine-tuning job.

  • status: :validating_files | :queued | :running | 3 more

    The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

    • :validating_files

    • :queued

    • :running

    • :succeeded

    • :failed

    • :cancelled

  • trained_tokens: Integer

    The total number of billable tokens processed by this fine-tuning job. The value will be null if the fine-tuning job is still running.

  • training_file: String

    The file ID used for training. You can retrieve the training data with the Files API.

  • validation_file: String

    The file ID used for validation. You can retrieve the validation results with the Files API.

  • estimated_finish: Integer

    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to finish. The value will be null if the fine-tuning job is not running.

  • integrations: Array[FineTuningJobWandbIntegrationObject]

    A list of integrations to enable for this fine-tuning job.

    • type: :wandb

      The type of the integration being enabled for the fine-tuning job

      • :wandb

    • wandb: FineTuningJobWandbIntegration

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. 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 be associated with your run.

      • project: String

        The name of the project that the new run will be created under.

      • entity: String

        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 run. If not set, the default entity for the registered WandB API key is used.

      • name: String

        A display name to set for the run. If not set, we will use the Job ID as the name.

      • tags: Array[String]

        A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".

  • metadata: 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.

  • method_: Method{ type, dpo, reinforcement, supervised}

    The method used for fine-tuning.

    • type: :supervised | :dpo | :reinforcement

      The type of method. Is either supervised, dpo, or reinforcement.

      • :supervised

      • :dpo

      • :reinforcement

    • dpo: DpoMethod

      Configuration for the DPO fine-tuning method.

      • hyperparameters: DpoHyperparameters

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • beta: :auto | Float

          The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.

          • Beta = :auto

            • :auto

          • Float = Float

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

    • reinforcement: ReinforcementMethod

      Configuration for the reinforcement fine-tuning method.

      • grader: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

        The grader used for the fine-tuning job.

        • class StringCheckGrader

          A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

          • input: String

            The input text. This may include template strings.

          • name: String

            The name of the grader.

          • operation: :eq | :ne | :like | :ilike

            The string check operation to perform. One of eq, ne, like, or ilike.

            • :eq

            • :ne

            • :like

            • :ilike

          • reference: String

            The reference text. This may include template strings.

          • type: :string_check

            The object type, which is always string_check.

            • :string_check

        • class TextSimilarityGrader

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: :cosine | :fuzzy_match | :bleu | 8 more

            The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.

            • :cosine

            • :fuzzy_match

            • :bleu

            • :gleu

            • :meteor

            • :rouge_1

            • :rouge_2

            • :rouge_3

            • :rouge_4

            • :rouge_5

            • :rouge_l

          • input: String

            The text being graded.

          • name: String

            The name of the grader.

          • reference: String

            The text being graded against.

          • type: :text_similarity

            The type of grader.

            • :text_similarity

        • class PythonGrader

          A PythonGrader object that runs a python script on the input.

          • name: String

            The name of the grader.

          • source: String

            The source code of the python script.

          • type: :python

            The object type, which is always python.

            • :python

          • image_tag: String

            The image tag to use for the python script.

        • class ScoreModelGrader

          A ScoreModelGrader object that uses a model to assign a score to the input.

          • input: Array[Input{ content, role, type}]

            The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.

            • content: String | ResponseInputText | OutputText{ text, type} | 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • String = String

                A text input to the model.

              • class ResponseInputText

                A text input to the model.

                • text: String

                  The text input to the model.

                • type: :input_text

                  The type of the input item. Always input_text.

                  • :input_text

                • prompt_cache_breakpoint: PromptCacheBreakpoint{ mode}

                  Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

                  • mode: :explicit

                    The breakpoint mode. Always explicit.

                    • :explicit

              • class OutputText

                A text output from the model.

                • text: String

                  The text output from the model.

                • type: :output_text

                  The type of the output text. Always output_text.

                  • :output_text

              • class InputImage

                An image input block used within EvalItem content arrays.

                • image_url: String

                  The URL of the image input.

                • type: :input_image

                  The type of the image input. Always input_image.

                  • :input_image

                • detail: String

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • class ResponseInputAudio

                An audio input to the model.

                • input_audio: InputAudio{ data, format_}

                  • data: String

                    Base64-encoded audio data.

                  • format_: :mp3 | :wav

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • :mp3

                    • :wav

                • type: :input_audio

                  The type of the input item. Always input_audio.

                  • :input_audio

              • GraderInputs = Array[GraderInputItem]

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • String = String

                  A text input to the model.

                • class ResponseInputText

                  A text input to the model.

                • class OutputText

                  A text output from the model.

                  • text: String

                    The text output from the model.

                  • type: :output_text

                    The type of the output text. Always output_text.

                    • :output_text

                • class InputImage

                  An image input block used within EvalItem content arrays.

                  • image_url: String

                    The URL of the image input.

                  • type: :input_image

                    The type of the image input. Always input_image.

                    • :input_image

                  • detail: String

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • class ResponseInputAudio

                  An audio input to the model.

            • role: :user | :assistant | :system | :developer

              The role of the message input. One of user, assistant, system, or developer.

              • :user

              • :assistant

              • :system

              • :developer

            • type: :message

              The type of the message input. Always message.

              • :message

          • model: String

            The model to use for the evaluation.

          • name: String

            The name of the grader.

          • type: :score_model

            The object type, which is always score_model.

            • :score_model

          • range: Array[Float]

            The range of the score. Defaults to [0, 1].

          • sampling_params: SamplingParams{ max_completions_tokens, reasoning_effort, seed, 2 more}

            The sampling parameters for the model.

            • max_completions_tokens: Integer

              The maximum number of tokens the grader model may generate in its response.

            • reasoning_effort: ReasoningEffort

              Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, xhigh, and max. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. Not all reasoning models support every value. See the reasoning guide for model-specific support.

              • :none

              • :minimal

              • :low

              • :medium

              • :high

              • :xhigh

              • :max

            • seed: Integer

              A seed value to initialize the randomness, during sampling.

            • temperature: Float

              A higher temperature increases randomness in the outputs.

            • top_p: Float

              An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

        • class MultiGrader

          A MultiGrader object combines the output of multiple graders to produce a single score.

          • calculate_output: String

            A formula to calculate the output based on grader results.

          • graders: StringCheckGrader | TextSimilarityGrader | PythonGrader | 2 more

            A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • class StringCheckGrader

              A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

            • class TextSimilarityGrader

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • class PythonGrader

              A PythonGrader object that runs a python script on the input.

            • class ScoreModelGrader

              A ScoreModelGrader object that uses a model to assign a score to the input.

            • class LabelModelGrader

              A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

              • input: Array[Input{ content, role, type}]

                • content: String | ResponseInputText | OutputText{ text, type} | 3 more

                  Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                  • String = String

                    A text input to the model.

                  • class ResponseInputText

                    A text input to the model.

                  • class OutputText

                    A text output from the model.

                    • text: String

                      The text output from the model.

                    • type: :output_text

                      The type of the output text. Always output_text.

                      • :output_text

                  • class InputImage

                    An image input block used within EvalItem content arrays.

                    • image_url: String

                      The URL of the image input.

                    • type: :input_image

                      The type of the image input. Always input_image.

                      • :input_image

                    • detail: String

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • class ResponseInputAudio

                    An audio input to the model.

                  • GraderInputs = Array[GraderInputItem]

                    A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • role: :user | :assistant | :system | :developer

                  The role of the message input. One of user, assistant, system, or developer.

                  • :user

                  • :assistant

                  • :system

                  • :developer

                • type: :message

                  The type of the message input. Always message.

                  • :message

              • labels: Array[String]

                The labels to assign to each item in the evaluation.

              • model: String

                The model to use for the evaluation. Must support structured outputs.

              • name: String

                The name of the grader.

              • passing_labels: Array[String]

                The labels that indicate a passing result. Must be a subset of labels.

              • type: :label_model

                The object type, which is always label_model.

                • :label_model

          • name: String

            The name of the grader.

          • type: :multi

            The object type, which is always multi.

            • :multi

      • hyperparameters: ReinforcementHyperparameters

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • compute_multiplier: :auto | Float

          Multiplier on amount of compute used for exploring search space during training.

          • ComputeMultiplier = :auto

            • :auto

          • Float = Float

        • eval_interval: :auto | Integer

          The number of training steps between evaluation runs.

          • EvalInterval = :auto

            • :auto

          • Integer = Integer

        • eval_samples: :auto | Integer

          Number of evaluation samples to generate per training step.

          • EvalSamples = :auto

            • :auto

          • Integer = Integer

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer

        • reasoning_effort: :default | :low | :medium | :high

          Level of reasoning effort.

          • :default

          • :low

          • :medium

          • :high

    • supervised: SupervisedMethod

      Configuration for the supervised fine-tuning method.

      • hyperparameters: SupervisedHyperparameters

        The hyperparameters used for the fine-tuning job.

        • batch_size: :auto | Integer

          Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

          • BatchSize = :auto

            • :auto

          • Integer = Integer

        • learning_rate_multiplier: :auto | Float

          Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

          • LearningRateMultiplier = :auto

            • :auto

          • Float = Float

        • n_epochs: :auto | Integer

          The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

          • NEpochs = :auto

            • :auto

          • Integer = Integer