Fine Tuning

Methods

Domain Types

Dpo Hyperparameters

• type DpoHyperparametersResp struct{…}

  The hyperparameters used for the DPO fine-tuning job.

  • BatchSize DpoHyperparametersBatchSizeUnionResp

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

    • type Auto string

      • const AutoAuto Auto = "auto"

    • int64

  • Beta DpoHyperparametersBetaUnionResp

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

    • type Auto string

      • const AutoAuto Auto = "auto"

    • float64

  • LearningRateMultiplier DpoHyperparametersLearningRateMultiplierUnionResp

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

    • type Auto string

      • const AutoAuto Auto = "auto"

    • float64

  • NEpochs DpoHyperparametersNEpochsUnionResp

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

    • type Auto string

      • const AutoAuto Auto = "auto"

    • int64

Dpo Method

• type DpoMethod struct{…}

  Configuration for the DPO fine-tuning method.

  • Hyperparameters DpoHyperparametersResp

    The hyperparameters used for the DPO fine-tuning job.

    • BatchSize DpoHyperparametersBatchSizeUnionResp

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • Beta DpoHyperparametersBetaUnionResp

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • float64

    • LearningRateMultiplier DpoHyperparametersLearningRateMultiplierUnionResp

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • float64

    • NEpochs DpoHyperparametersNEpochsUnionResp

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

Reinforcement Hyperparameters

• type ReinforcementHyperparametersResp struct{…}

  The hyperparameters used for the reinforcement fine-tuning job.

  • BatchSize ReinforcementHyperparametersBatchSizeUnionResp

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

    • type Auto string

      • const AutoAuto Auto = "auto"

    • int64

  • ComputeMultiplier ReinforcementHyperparametersComputeMultiplierUnionResp

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

    • type Auto string

      • const AutoAuto Auto = "auto"

    • float64

  • EvalInterval ReinforcementHyperparametersEvalIntervalUnionResp

    The number of training steps between evaluation runs.

    • type Auto string

      • const AutoAuto Auto = "auto"

    • int64

  • EvalSamples ReinforcementHyperparametersEvalSamplesUnionResp

    Number of evaluation samples to generate per training step.

    • type Auto string

      • const AutoAuto Auto = "auto"

    • int64

  • LearningRateMultiplier ReinforcementHyperparametersLearningRateMultiplierUnionResp

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

    • type Auto string

      • const AutoAuto Auto = "auto"

    • float64

  • NEpochs ReinforcementHyperparametersNEpochsUnionResp

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

    • type Auto string

      • const AutoAuto Auto = "auto"

    • int64

  • ReasoningEffort ReinforcementHyperparametersReasoningEffort

    Level of reasoning effort.

    • const ReinforcementHyperparametersReasoningEffortDefault ReinforcementHyperparametersReasoningEffort = "default"

    • const ReinforcementHyperparametersReasoningEffortLow ReinforcementHyperparametersReasoningEffort = "low"

    • const ReinforcementHyperparametersReasoningEffortMedium ReinforcementHyperparametersReasoningEffort = "medium"

    • const ReinforcementHyperparametersReasoningEffortHigh ReinforcementHyperparametersReasoningEffort = "high"

Reinforcement Method

• type ReinforcementMethod struct{…}

  Configuration for the reinforcement fine-tuning method.

  • Grader ReinforcementMethodGraderUnion

    The grader used for the fine-tuning job.

    • type StringCheckGrader struct{…}

      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 StringCheckGraderOperation

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

        • const StringCheckGraderOperationEq StringCheckGraderOperation = "eq"

        • const StringCheckGraderOperationNe StringCheckGraderOperation = "ne"

        • const StringCheckGraderOperationLike StringCheckGraderOperation = "like"

        • const StringCheckGraderOperationIlike StringCheckGraderOperation = "ilike"

      • Reference string

        The reference text. This may include template strings.

      • Type StringCheck

        The object type, which is always string_check.

        • const StringCheckStringCheck StringCheck = "string_check"

    • type TextSimilarityGrader struct{…}

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • EvaluationMetric TextSimilarityGraderEvaluationMetric

        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.

        • const TextSimilarityGraderEvaluationMetricCosine TextSimilarityGraderEvaluationMetric = "cosine"

        • const TextSimilarityGraderEvaluationMetricFuzzyMatch TextSimilarityGraderEvaluationMetric = "fuzzy_match"

        • const TextSimilarityGraderEvaluationMetricBleu TextSimilarityGraderEvaluationMetric = "bleu"

        • const TextSimilarityGraderEvaluationMetricGleu TextSimilarityGraderEvaluationMetric = "gleu"

        • const TextSimilarityGraderEvaluationMetricMeteor TextSimilarityGraderEvaluationMetric = "meteor"

        • const TextSimilarityGraderEvaluationMetricRouge1 TextSimilarityGraderEvaluationMetric = "rouge_1"

        • const TextSimilarityGraderEvaluationMetricRouge2 TextSimilarityGraderEvaluationMetric = "rouge_2"

        • const TextSimilarityGraderEvaluationMetricRouge3 TextSimilarityGraderEvaluationMetric = "rouge_3"

        • const TextSimilarityGraderEvaluationMetricRouge4 TextSimilarityGraderEvaluationMetric = "rouge_4"

        • const TextSimilarityGraderEvaluationMetricRouge5 TextSimilarityGraderEvaluationMetric = "rouge_5"

        • const TextSimilarityGraderEvaluationMetricRougeL TextSimilarityGraderEvaluationMetric = "rouge_l"

      • Input string

        The text being graded.

      • Name string

        The name of the grader.

      • Reference string

        The text being graded against.

      • Type TextSimilarity

        The type of grader.

        • const TextSimilarityTextSimilarity TextSimilarity = "text_similarity"

    • type PythonGrader struct{…}

      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.

        • const PythonPython Python = "python"

      • ImageTag string

        The image tag to use for the python script.

    • type ScoreModelGrader struct{…}

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

      • Input []ScoreModelGraderInput

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

        • Content ScoreModelGraderInputContentUnion

          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

          • type ResponseInputText struct{…}

            A text input to the model.

            • Text string

              The text input to the model.

            • Type InputText

              The type of the input item. Always input_text.

              • const InputTextInputText InputText = "input_text"

            • PromptCacheBreakpoint ResponseInputTextPromptCacheBreakpoint

              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.

                • const ExplicitExplicit Explicit = "explicit"

          • type ScoreModelGraderInputContentOutputText struct{…}

            A text output from the model.

            • Text string

              The text output from the model.

            • Type OutputText

              The type of the output text. Always output_text.

              • const OutputTextOutputText OutputText = "output_text"

          • type ScoreModelGraderInputContentInputImage struct{…}

            An image input block used within EvalItem content arrays.

            • ImageURL string

              The URL of the image input.

            • Type InputImage

              The type of the image input. Always input_image.

              • const InputImageInputImage InputImage = "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.

          • type ResponseInputAudio struct{…}

            An audio input to the model.

            • InputAudio ResponseInputAudioInputAudio

              • Data string

                Base64-encoded audio data.

              • Format string

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

                • const ResponseInputAudioInputAudioFormatMP3 ResponseInputAudioInputAudioFormat = "mp3"

                • const ResponseInputAudioInputAudioFormatWAV ResponseInputAudioInputAudioFormat = "wav"

            • Type InputAudio

              The type of the input item. Always input_audio.

              • const InputAudioInputAudio InputAudio = "input_audio"

          • type GraderInputs []GraderInputUnion

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

            • string

            • type ResponseInputText struct{…}

              A text input to the model.

            • type GraderInputOutputText struct{…}

              A text output from the model.

              • Text string

                The text output from the model.

              • Type OutputText

                The type of the output text. Always output_text.

                • const OutputTextOutputText OutputText = "output_text"

            • type GraderInputInputImage struct{…}

              An image input block used within EvalItem content arrays.

              • ImageURL string

                The URL of the image input.

              • Type InputImage

                The type of the image input. Always input_image.

                • const InputImageInputImage InputImage = "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.

            • type ResponseInputAudio struct{…}

              An audio input to the model.

        • Role string

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

          • const ScoreModelGraderInputRoleUser ScoreModelGraderInputRole = "user"

          • const ScoreModelGraderInputRoleAssistant ScoreModelGraderInputRole = "assistant"

          • const ScoreModelGraderInputRoleSystem ScoreModelGraderInputRole = "system"

          • const ScoreModelGraderInputRoleDeveloper ScoreModelGraderInputRole = "developer"

        • Type string

          The type of the message input. Always message.

          • const ScoreModelGraderInputTypeMessage ScoreModelGraderInputType = "message"

      • Model string

        The model to use for the evaluation.

      • Name string

        The name of the grader.

      • Type ScoreModel

        The object type, which is always score_model.

        • const ScoreModelScoreModel ScoreModel = "score_model"

      • Range []float64

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

      • SamplingParams ScoreModelGraderSamplingParams

        The sampling parameters for the model.

        • MaxCompletionsTokens int64

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

        • ReasoningEffort 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.

          • const ReasoningEffortNone ReasoningEffort = "none"

          • const ReasoningEffortMinimal ReasoningEffort = "minimal"

          • const ReasoningEffortLow ReasoningEffort = "low"

          • const ReasoningEffortMedium ReasoningEffort = "medium"

          • const ReasoningEffortHigh ReasoningEffort = "high"

          • const ReasoningEffortXhigh ReasoningEffort = "xhigh"

          • const ReasoningEffortMax ReasoningEffort = "max"

        • Seed int64

          A seed value to initialize the randomness, during sampling.

        • Temperature float64

          A higher temperature increases randomness in the outputs.

        • TopP float64

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

    • type MultiGrader struct{…}

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

      • CalculateOutput string

        A formula to calculate the output based on grader results.

      • Graders MultiGraderGradersUnion

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

        • type StringCheckGrader struct{…}

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

        • type TextSimilarityGrader struct{…}

          A TextSimilarityGrader object which grades text based on similarity metrics.

        • type PythonGrader struct{…}

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

        • type ScoreModelGrader struct{…}

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

        • type LabelModelGrader struct{…}

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

          • Input []LabelModelGraderInput

            • Content LabelModelGraderInputContentUnion

              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

              • type ResponseInputText struct{…}

                A text input to the model.

              • type LabelModelGraderInputContentOutputText struct{…}

                A text output from the model.

                • Text string

                  The text output from the model.

                • Type OutputText

                  The type of the output text. Always output_text.

                  • const OutputTextOutputText OutputText = "output_text"

              • type LabelModelGraderInputContentInputImage struct{…}

                An image input block used within EvalItem content arrays.

                • ImageURL string

                  The URL of the image input.

                • Type InputImage

                  The type of the image input. Always input_image.

                  • const InputImageInputImage InputImage = "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.

              • type ResponseInputAudio struct{…}

                An audio input to the model.

              • type GraderInputs []GraderInputUnion

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

            • Role string

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

              • const LabelModelGraderInputRoleUser LabelModelGraderInputRole = "user"

              • const LabelModelGraderInputRoleAssistant LabelModelGraderInputRole = "assistant"

              • const LabelModelGraderInputRoleSystem LabelModelGraderInputRole = "system"

              • const LabelModelGraderInputRoleDeveloper LabelModelGraderInputRole = "developer"

            • Type string

              The type of the message input. Always message.

              • const LabelModelGraderInputTypeMessage LabelModelGraderInputType = "message"

          • Labels []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.

          • PassingLabels []string

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

          • Type LabelModel

            The object type, which is always label_model.

            • const LabelModelLabelModel LabelModel = "label_model"

      • Name string

        The name of the grader.

      • Type Multi

        The object type, which is always multi.

        • const MultiMulti Multi = "multi"

  • Hyperparameters ReinforcementHyperparametersResp

    The hyperparameters used for the reinforcement fine-tuning job.

    • BatchSize ReinforcementHyperparametersBatchSizeUnionResp

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • ComputeMultiplier ReinforcementHyperparametersComputeMultiplierUnionResp

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • float64

    • EvalInterval ReinforcementHyperparametersEvalIntervalUnionResp

      The number of training steps between evaluation runs.

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • EvalSamples ReinforcementHyperparametersEvalSamplesUnionResp

      Number of evaluation samples to generate per training step.

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • LearningRateMultiplier ReinforcementHyperparametersLearningRateMultiplierUnionResp

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • float64

    • NEpochs ReinforcementHyperparametersNEpochsUnionResp

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • ReasoningEffort ReinforcementHyperparametersReasoningEffort

      Level of reasoning effort.

      • const ReinforcementHyperparametersReasoningEffortDefault ReinforcementHyperparametersReasoningEffort = "default"

      • const ReinforcementHyperparametersReasoningEffortLow ReinforcementHyperparametersReasoningEffort = "low"

      • const ReinforcementHyperparametersReasoningEffortMedium ReinforcementHyperparametersReasoningEffort = "medium"

      • const ReinforcementHyperparametersReasoningEffortHigh ReinforcementHyperparametersReasoningEffort = "high"

Supervised Hyperparameters

• type SupervisedHyperparametersResp struct{…}

  The hyperparameters used for the fine-tuning job.

  • BatchSize SupervisedHyperparametersBatchSizeUnionResp

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

    • type Auto string

      • const AutoAuto Auto = "auto"

    • int64

  • LearningRateMultiplier SupervisedHyperparametersLearningRateMultiplierUnionResp

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

    • type Auto string

      • const AutoAuto Auto = "auto"

    • float64

  • NEpochs SupervisedHyperparametersNEpochsUnionResp

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

    • type Auto string

      • const AutoAuto Auto = "auto"

    • int64

Supervised Method

• type SupervisedMethod struct{…}

  Configuration for the supervised fine-tuning method.

  • Hyperparameters SupervisedHyperparametersResp

    The hyperparameters used for the fine-tuning job.

    • BatchSize SupervisedHyperparametersBatchSizeUnionResp

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • LearningRateMultiplier SupervisedHyperparametersLearningRateMultiplierUnionResp

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • float64

    • NEpochs SupervisedHyperparametersNEpochsUnionResp

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

Jobs

Create fine-tuning job

client.FineTuning.Jobs.New(ctx, body) (*FineTuningJob, error)

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

• body FineTuningJobNewParams

  • Model param.Field[FineTuningJobNewParamsModel]

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

    • string

    • type FineTuningJobNewParamsModel string

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

      • const FineTuningJobNewParamsModelBabbage002 FineTuningJobNewParamsModel = "babbage-002"

      • const FineTuningJobNewParamsModelDavinci002 FineTuningJobNewParamsModel = "davinci-002"

      • const FineTuningJobNewParamsModelGPT3_5Turbo FineTuningJobNewParamsModel = "gpt-3.5-turbo"

      • const FineTuningJobNewParamsModelGPT4oMini FineTuningJobNewParamsModel = "gpt-4o-mini"

  • TrainingFile param.Field[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 param.Field[FineTuningJobNewParamsHyperparameters]

    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.

    • BatchSize FineTuningJobNewParamsHyperparametersBatchSizeUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • LearningRateMultiplier FineTuningJobNewParamsHyperparametersLearningRateMultiplierUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • float64

    • NEpochs FineTuningJobNewParamsHyperparametersNEpochsUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

  • Integrations param.Field[[]FineTuningJobNewParamsIntegration]

    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.

      • const WandbWandb Wandb = "wandb"

    • Wandb FineTuningJobNewParamsIntegrationWandb

      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 []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 param.Field[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 param.Field[FineTuningJobNewParamsMethod]

    The method used for fine-tuning.

    • Type string

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

      • const FineTuningJobNewParamsMethodTypeSupervised FineTuningJobNewParamsMethodType = "supervised"

      • const FineTuningJobNewParamsMethodTypeDpo FineTuningJobNewParamsMethodType = "dpo"

      • const FineTuningJobNewParamsMethodTypeReinforcement FineTuningJobNewParamsMethodType = "reinforcement"

    • Dpo DpoMethod

      Configuration for the DPO fine-tuning method.

      • Hyperparameters DpoHyperparametersResp

        The hyperparameters used for the DPO fine-tuning job.

        • BatchSize DpoHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • Beta DpoHyperparametersBetaUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • LearningRateMultiplier DpoHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs DpoHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

    • Reinforcement ReinforcementMethod

      Configuration for the reinforcement fine-tuning method.

      • Grader ReinforcementMethodGraderUnion

        The grader used for the fine-tuning job.

        • type StringCheckGrader struct{…}

          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 StringCheckGraderOperation

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

            • const StringCheckGraderOperationEq StringCheckGraderOperation = "eq"

            • const StringCheckGraderOperationNe StringCheckGraderOperation = "ne"

            • const StringCheckGraderOperationLike StringCheckGraderOperation = "like"

            • const StringCheckGraderOperationIlike StringCheckGraderOperation = "ilike"

          • Reference string

            The reference text. This may include template strings.

          • Type StringCheck

            The object type, which is always string_check.

            • const StringCheckStringCheck StringCheck = "string_check"

        • type TextSimilarityGrader struct{…}

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • EvaluationMetric TextSimilarityGraderEvaluationMetric

            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.

            • const TextSimilarityGraderEvaluationMetricCosine TextSimilarityGraderEvaluationMetric = "cosine"

            • const TextSimilarityGraderEvaluationMetricFuzzyMatch TextSimilarityGraderEvaluationMetric = "fuzzy_match"

            • const TextSimilarityGraderEvaluationMetricBleu TextSimilarityGraderEvaluationMetric = "bleu"

            • const TextSimilarityGraderEvaluationMetricGleu TextSimilarityGraderEvaluationMetric = "gleu"

            • const TextSimilarityGraderEvaluationMetricMeteor TextSimilarityGraderEvaluationMetric = "meteor"

            • const TextSimilarityGraderEvaluationMetricRouge1 TextSimilarityGraderEvaluationMetric = "rouge_1"

            • const TextSimilarityGraderEvaluationMetricRouge2 TextSimilarityGraderEvaluationMetric = "rouge_2"

            • const TextSimilarityGraderEvaluationMetricRouge3 TextSimilarityGraderEvaluationMetric = "rouge_3"

            • const TextSimilarityGraderEvaluationMetricRouge4 TextSimilarityGraderEvaluationMetric = "rouge_4"

            • const TextSimilarityGraderEvaluationMetricRouge5 TextSimilarityGraderEvaluationMetric = "rouge_5"

            • const TextSimilarityGraderEvaluationMetricRougeL TextSimilarityGraderEvaluationMetric = "rouge_l"

          • Input string

            The text being graded.

          • Name string

            The name of the grader.

          • Reference string

            The text being graded against.

          • Type TextSimilarity

            The type of grader.

            • const TextSimilarityTextSimilarity TextSimilarity = "text_similarity"

        • type PythonGrader struct{…}

          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.

            • const PythonPython Python = "python"

          • ImageTag string

            The image tag to use for the python script.

        • type ScoreModelGrader struct{…}

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

          • Input []ScoreModelGraderInput

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

            • Content ScoreModelGraderInputContentUnion

              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

              • type ResponseInputText struct{…}

                A text input to the model.

                • Text string

                  The text input to the model.

                • Type InputText

                  The type of the input item. Always input_text.

                  • const InputTextInputText InputText = "input_text"

                • PromptCacheBreakpoint ResponseInputTextPromptCacheBreakpoint

                  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.

                    • const ExplicitExplicit Explicit = "explicit"

              • type ScoreModelGraderInputContentOutputText struct{…}

                A text output from the model.

                • Text string

                  The text output from the model.

                • Type OutputText

                  The type of the output text. Always output_text.

                  • const OutputTextOutputText OutputText = "output_text"

              • type ScoreModelGraderInputContentInputImage struct{…}

                An image input block used within EvalItem content arrays.

                • ImageURL string

                  The URL of the image input.

                • Type InputImage

                  The type of the image input. Always input_image.

                  • const InputImageInputImage InputImage = "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.

              • type ResponseInputAudio struct{…}

                An audio input to the model.

                • InputAudio ResponseInputAudioInputAudio

                  • Data string

                    Base64-encoded audio data.

                  • Format string

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

                    • const ResponseInputAudioInputAudioFormatMP3 ResponseInputAudioInputAudioFormat = "mp3"

                    • const ResponseInputAudioInputAudioFormatWAV ResponseInputAudioInputAudioFormat = "wav"

                • Type InputAudio

                  The type of the input item. Always input_audio.

                  • const InputAudioInputAudio InputAudio = "input_audio"

              • type GraderInputs []GraderInputUnion

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

                • string

                • type ResponseInputText struct{…}

                  A text input to the model.

                • type GraderInputOutputText struct{…}

                  A text output from the model.

                  • Text string

                    The text output from the model.

                  • Type OutputText

                    The type of the output text. Always output_text.

                    • const OutputTextOutputText OutputText = "output_text"

                • type GraderInputInputImage struct{…}

                  An image input block used within EvalItem content arrays.

                  • ImageURL string

                    The URL of the image input.

                  • Type InputImage

                    The type of the image input. Always input_image.

                    • const InputImageInputImage InputImage = "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.

                • type ResponseInputAudio struct{…}

                  An audio input to the model.

            • Role string

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

              • const ScoreModelGraderInputRoleUser ScoreModelGraderInputRole = "user"

              • const ScoreModelGraderInputRoleAssistant ScoreModelGraderInputRole = "assistant"

              • const ScoreModelGraderInputRoleSystem ScoreModelGraderInputRole = "system"

              • const ScoreModelGraderInputRoleDeveloper ScoreModelGraderInputRole = "developer"

            • Type string

              The type of the message input. Always message.

              • const ScoreModelGraderInputTypeMessage ScoreModelGraderInputType = "message"

          • Model string

            The model to use for the evaluation.

          • Name string

            The name of the grader.

          • Type ScoreModel

            The object type, which is always score_model.

            • const ScoreModelScoreModel ScoreModel = "score_model"

          • Range []float64

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

          • SamplingParams ScoreModelGraderSamplingParams

            The sampling parameters for the model.

            • MaxCompletionsTokens int64

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

            • ReasoningEffort 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.

              • const ReasoningEffortNone ReasoningEffort = "none"

              • const ReasoningEffortMinimal ReasoningEffort = "minimal"

              • const ReasoningEffortLow ReasoningEffort = "low"

              • const ReasoningEffortMedium ReasoningEffort = "medium"

              • const ReasoningEffortHigh ReasoningEffort = "high"

              • const ReasoningEffortXhigh ReasoningEffort = "xhigh"

              • const ReasoningEffortMax ReasoningEffort = "max"

            • Seed int64

              A seed value to initialize the randomness, during sampling.

            • Temperature float64

              A higher temperature increases randomness in the outputs.

            • TopP float64

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

        • type MultiGrader struct{…}

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

          • CalculateOutput string

            A formula to calculate the output based on grader results.

          • Graders MultiGraderGradersUnion

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

            • type StringCheckGrader struct{…}

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

            • type TextSimilarityGrader struct{…}

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • type PythonGrader struct{…}

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

            • type ScoreModelGrader struct{…}

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

            • type LabelModelGrader struct{…}

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

              • Input []LabelModelGraderInput

                • Content LabelModelGraderInputContentUnion

                  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

                  • type ResponseInputText struct{…}

                    A text input to the model.

                  • type LabelModelGraderInputContentOutputText struct{…}

                    A text output from the model.

                    • Text string

                      The text output from the model.

                    • Type OutputText

                      The type of the output text. Always output_text.

                      • const OutputTextOutputText OutputText = "output_text"

                  • type LabelModelGraderInputContentInputImage struct{…}

                    An image input block used within EvalItem content arrays.

                    • ImageURL string

                      The URL of the image input.

                    • Type InputImage

                      The type of the image input. Always input_image.

                      • const InputImageInputImage InputImage = "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.

                  • type ResponseInputAudio struct{…}

                    An audio input to the model.

                  • type GraderInputs []GraderInputUnion

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

                • Role string

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

                  • const LabelModelGraderInputRoleUser LabelModelGraderInputRole = "user"

                  • const LabelModelGraderInputRoleAssistant LabelModelGraderInputRole = "assistant"

                  • const LabelModelGraderInputRoleSystem LabelModelGraderInputRole = "system"

                  • const LabelModelGraderInputRoleDeveloper LabelModelGraderInputRole = "developer"

                • Type string

                  The type of the message input. Always message.

                  • const LabelModelGraderInputTypeMessage LabelModelGraderInputType = "message"

              • Labels []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.

              • PassingLabels []string

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

              • Type LabelModel

                The object type, which is always label_model.

                • const LabelModelLabelModel LabelModel = "label_model"

          • Name string

            The name of the grader.

          • Type Multi

            The object type, which is always multi.

            • const MultiMulti Multi = "multi"

      • Hyperparameters ReinforcementHyperparametersResp

        The hyperparameters used for the reinforcement fine-tuning job.

        • BatchSize ReinforcementHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ComputeMultiplier ReinforcementHyperparametersComputeMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • EvalInterval ReinforcementHyperparametersEvalIntervalUnionResp

          The number of training steps between evaluation runs.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • EvalSamples ReinforcementHyperparametersEvalSamplesUnionResp

          Number of evaluation samples to generate per training step.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier ReinforcementHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs ReinforcementHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ReasoningEffort ReinforcementHyperparametersReasoningEffort

          Level of reasoning effort.

          • const ReinforcementHyperparametersReasoningEffortDefault ReinforcementHyperparametersReasoningEffort = "default"

          • const ReinforcementHyperparametersReasoningEffortLow ReinforcementHyperparametersReasoningEffort = "low"

          • const ReinforcementHyperparametersReasoningEffortMedium ReinforcementHyperparametersReasoningEffort = "medium"

          • const ReinforcementHyperparametersReasoningEffortHigh ReinforcementHyperparametersReasoningEffort = "high"

    • Supervised SupervisedMethod

      Configuration for the supervised fine-tuning method.

      • Hyperparameters SupervisedHyperparametersResp

        The hyperparameters used for the fine-tuning job.

        • BatchSize SupervisedHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier SupervisedHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs SupervisedHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

  • Seed param.Field[int64]

    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 param.Field[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.

  • ValidationFile param.Field[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

• type FineTuningJob struct{…}

  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.

  • CreatedAt int64

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

  • Error FineTuningJobError

    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.

  • FineTunedModel 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.

  • FinishedAt int64

    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 FineTuningJobHyperparameters

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

    • BatchSize FineTuningJobHyperparametersBatchSizeUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • LearningRateMultiplier FineTuningJobHyperparametersLearningRateMultiplierUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • float64

    • NEpochs FineTuningJobHyperparametersNEpochsUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

  • Model string

    The base model that is being fine-tuned.

  • Object FineTuningJob

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

    • const FineTuningJobFineTuningJob FineTuningJob = "fine_tuning.job"

  • OrganizationID string

    The organization that owns the fine-tuning job.

  • ResultFiles []string

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

  • Seed int64

    The seed used for the fine-tuning job.

  • Status FineTuningJobStatus

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

    • const FineTuningJobStatusValidatingFiles FineTuningJobStatus = "validating_files"

    • const FineTuningJobStatusQueued FineTuningJobStatus = "queued"

    • const FineTuningJobStatusRunning FineTuningJobStatus = "running"

    • const FineTuningJobStatusSucceeded FineTuningJobStatus = "succeeded"

    • const FineTuningJobStatusFailed FineTuningJobStatus = "failed"

    • const FineTuningJobStatusCancelled FineTuningJobStatus = "cancelled"

  • TrainedTokens int64

    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.

  • TrainingFile string

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

  • ValidationFile string

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

  • EstimatedFinish int64

    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 []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

      • const WandbWandb Wandb = "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 []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 FineTuningJobMethod

    The method used for fine-tuning.

    • Type string

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

      • const FineTuningJobMethodTypeSupervised FineTuningJobMethodType = "supervised"

      • const FineTuningJobMethodTypeDpo FineTuningJobMethodType = "dpo"

      • const FineTuningJobMethodTypeReinforcement FineTuningJobMethodType = "reinforcement"

    • Dpo DpoMethod

      Configuration for the DPO fine-tuning method.

      • Hyperparameters DpoHyperparametersResp

        The hyperparameters used for the DPO fine-tuning job.

        • BatchSize DpoHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • Beta DpoHyperparametersBetaUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • LearningRateMultiplier DpoHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs DpoHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

    • Reinforcement ReinforcementMethod

      Configuration for the reinforcement fine-tuning method.

      • Grader ReinforcementMethodGraderUnion

        The grader used for the fine-tuning job.

        • type StringCheckGrader struct{…}

          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 StringCheckGraderOperation

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

            • const StringCheckGraderOperationEq StringCheckGraderOperation = "eq"

            • const StringCheckGraderOperationNe StringCheckGraderOperation = "ne"

            • const StringCheckGraderOperationLike StringCheckGraderOperation = "like"

            • const StringCheckGraderOperationIlike StringCheckGraderOperation = "ilike"

          • Reference string

            The reference text. This may include template strings.

          • Type StringCheck

            The object type, which is always string_check.

            • const StringCheckStringCheck StringCheck = "string_check"

        • type TextSimilarityGrader struct{…}

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • EvaluationMetric TextSimilarityGraderEvaluationMetric

            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.

            • const TextSimilarityGraderEvaluationMetricCosine TextSimilarityGraderEvaluationMetric = "cosine"

            • const TextSimilarityGraderEvaluationMetricFuzzyMatch TextSimilarityGraderEvaluationMetric = "fuzzy_match"

            • const TextSimilarityGraderEvaluationMetricBleu TextSimilarityGraderEvaluationMetric = "bleu"

            • const TextSimilarityGraderEvaluationMetricGleu TextSimilarityGraderEvaluationMetric = "gleu"

            • const TextSimilarityGraderEvaluationMetricMeteor TextSimilarityGraderEvaluationMetric = "meteor"

            • const TextSimilarityGraderEvaluationMetricRouge1 TextSimilarityGraderEvaluationMetric = "rouge_1"

            • const TextSimilarityGraderEvaluationMetricRouge2 TextSimilarityGraderEvaluationMetric = "rouge_2"

            • const TextSimilarityGraderEvaluationMetricRouge3 TextSimilarityGraderEvaluationMetric = "rouge_3"

            • const TextSimilarityGraderEvaluationMetricRouge4 TextSimilarityGraderEvaluationMetric = "rouge_4"

            • const TextSimilarityGraderEvaluationMetricRouge5 TextSimilarityGraderEvaluationMetric = "rouge_5"

            • const TextSimilarityGraderEvaluationMetricRougeL TextSimilarityGraderEvaluationMetric = "rouge_l"

          • Input string

            The text being graded.

          • Name string

            The name of the grader.

          • Reference string

            The text being graded against.

          • Type TextSimilarity

            The type of grader.

            • const TextSimilarityTextSimilarity TextSimilarity = "text_similarity"

        • type PythonGrader struct{…}

          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.

            • const PythonPython Python = "python"

          • ImageTag string

            The image tag to use for the python script.

        • type ScoreModelGrader struct{…}

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

          • Input []ScoreModelGraderInput

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

            • Content ScoreModelGraderInputContentUnion

              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

              • type ResponseInputText struct{…}

                A text input to the model.

                • Text string

                  The text input to the model.

                • Type InputText

                  The type of the input item. Always input_text.

                  • const InputTextInputText InputText = "input_text"

                • PromptCacheBreakpoint ResponseInputTextPromptCacheBreakpoint

                  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.

                    • const ExplicitExplicit Explicit = "explicit"

              • type ScoreModelGraderInputContentOutputText struct{…}

                A text output from the model.

                • Text string

                  The text output from the model.

                • Type OutputText

                  The type of the output text. Always output_text.

                  • const OutputTextOutputText OutputText = "output_text"

              • type ScoreModelGraderInputContentInputImage struct{…}

                An image input block used within EvalItem content arrays.

                • ImageURL string

                  The URL of the image input.

                • Type InputImage

                  The type of the image input. Always input_image.

                  • const InputImageInputImage InputImage = "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.

              • type ResponseInputAudio struct{…}

                An audio input to the model.

                • InputAudio ResponseInputAudioInputAudio

                  • Data string

                    Base64-encoded audio data.

                  • Format string

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

                    • const ResponseInputAudioInputAudioFormatMP3 ResponseInputAudioInputAudioFormat = "mp3"

                    • const ResponseInputAudioInputAudioFormatWAV ResponseInputAudioInputAudioFormat = "wav"

                • Type InputAudio

                  The type of the input item. Always input_audio.

                  • const InputAudioInputAudio InputAudio = "input_audio"

              • type GraderInputs []GraderInputUnion

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

                • string

                • type ResponseInputText struct{…}

                  A text input to the model.

                • type GraderInputOutputText struct{…}

                  A text output from the model.

                  • Text string

                    The text output from the model.

                  • Type OutputText

                    The type of the output text. Always output_text.

                    • const OutputTextOutputText OutputText = "output_text"

                • type GraderInputInputImage struct{…}

                  An image input block used within EvalItem content arrays.

                  • ImageURL string

                    The URL of the image input.

                  • Type InputImage

                    The type of the image input. Always input_image.

                    • const InputImageInputImage InputImage = "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.

                • type ResponseInputAudio struct{…}

                  An audio input to the model.

            • Role string

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

              • const ScoreModelGraderInputRoleUser ScoreModelGraderInputRole = "user"

              • const ScoreModelGraderInputRoleAssistant ScoreModelGraderInputRole = "assistant"

              • const ScoreModelGraderInputRoleSystem ScoreModelGraderInputRole = "system"

              • const ScoreModelGraderInputRoleDeveloper ScoreModelGraderInputRole = "developer"

            • Type string

              The type of the message input. Always message.

              • const ScoreModelGraderInputTypeMessage ScoreModelGraderInputType = "message"

          • Model string

            The model to use for the evaluation.

          • Name string

            The name of the grader.

          • Type ScoreModel

            The object type, which is always score_model.

            • const ScoreModelScoreModel ScoreModel = "score_model"

          • Range []float64

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

          • SamplingParams ScoreModelGraderSamplingParams

            The sampling parameters for the model.

            • MaxCompletionsTokens int64

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

            • ReasoningEffort 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.

              • const ReasoningEffortNone ReasoningEffort = "none"

              • const ReasoningEffortMinimal ReasoningEffort = "minimal"

              • const ReasoningEffortLow ReasoningEffort = "low"

              • const ReasoningEffortMedium ReasoningEffort = "medium"

              • const ReasoningEffortHigh ReasoningEffort = "high"

              • const ReasoningEffortXhigh ReasoningEffort = "xhigh"

              • const ReasoningEffortMax ReasoningEffort = "max"

            • Seed int64

              A seed value to initialize the randomness, during sampling.

            • Temperature float64

              A higher temperature increases randomness in the outputs.

            • TopP float64

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

        • type MultiGrader struct{…}

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

          • CalculateOutput string

            A formula to calculate the output based on grader results.

          • Graders MultiGraderGradersUnion

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

            • type StringCheckGrader struct{…}

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

            • type TextSimilarityGrader struct{…}

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • type PythonGrader struct{…}

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

            • type ScoreModelGrader struct{…}

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

            • type LabelModelGrader struct{…}

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

              • Input []LabelModelGraderInput

                • Content LabelModelGraderInputContentUnion

                  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

                  • type ResponseInputText struct{…}

                    A text input to the model.

                  • type LabelModelGraderInputContentOutputText struct{…}

                    A text output from the model.

                    • Text string

                      The text output from the model.

                    • Type OutputText

                      The type of the output text. Always output_text.

                      • const OutputTextOutputText OutputText = "output_text"

                  • type LabelModelGraderInputContentInputImage struct{…}

                    An image input block used within EvalItem content arrays.

                    • ImageURL string

                      The URL of the image input.

                    • Type InputImage

                      The type of the image input. Always input_image.

                      • const InputImageInputImage InputImage = "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.

                  • type ResponseInputAudio struct{…}

                    An audio input to the model.

                  • type GraderInputs []GraderInputUnion

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

                • Role string

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

                  • const LabelModelGraderInputRoleUser LabelModelGraderInputRole = "user"

                  • const LabelModelGraderInputRoleAssistant LabelModelGraderInputRole = "assistant"

                  • const LabelModelGraderInputRoleSystem LabelModelGraderInputRole = "system"

                  • const LabelModelGraderInputRoleDeveloper LabelModelGraderInputRole = "developer"

                • Type string

                  The type of the message input. Always message.

                  • const LabelModelGraderInputTypeMessage LabelModelGraderInputType = "message"

              • Labels []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.

              • PassingLabels []string

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

              • Type LabelModel

                The object type, which is always label_model.

                • const LabelModelLabelModel LabelModel = "label_model"

          • Name string

            The name of the grader.

          • Type Multi

            The object type, which is always multi.

            • const MultiMulti Multi = "multi"

      • Hyperparameters ReinforcementHyperparametersResp

        The hyperparameters used for the reinforcement fine-tuning job.

        • BatchSize ReinforcementHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ComputeMultiplier ReinforcementHyperparametersComputeMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • EvalInterval ReinforcementHyperparametersEvalIntervalUnionResp

          The number of training steps between evaluation runs.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • EvalSamples ReinforcementHyperparametersEvalSamplesUnionResp

          Number of evaluation samples to generate per training step.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier ReinforcementHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs ReinforcementHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ReasoningEffort ReinforcementHyperparametersReasoningEffort

          Level of reasoning effort.

          • const ReinforcementHyperparametersReasoningEffortDefault ReinforcementHyperparametersReasoningEffort = "default"

          • const ReinforcementHyperparametersReasoningEffortLow ReinforcementHyperparametersReasoningEffort = "low"

          • const ReinforcementHyperparametersReasoningEffortMedium ReinforcementHyperparametersReasoningEffort = "medium"

          • const ReinforcementHyperparametersReasoningEffortHigh ReinforcementHyperparametersReasoningEffort = "high"

    • Supervised SupervisedMethod

      Configuration for the supervised fine-tuning method.

      • Hyperparameters SupervisedHyperparametersResp

        The hyperparameters used for the fine-tuning job.

        • BatchSize SupervisedHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier SupervisedHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs SupervisedHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

Example

package main

import (
  "context"
  "fmt"

  "github.com/openai/openai-go"
  "github.com/openai/openai-go/option"
)

func main() {
  client := openai.NewClient(
    option.WithAPIKey("My API Key"),
  )
  fineTuningJob, err := client.FineTuning.Jobs.New(context.TODO(), openai.FineTuningJobNewParams{
    Model: openai.FineTuningJobNewParamsModelGPT4oMini,
    TrainingFile: "file-abc123",
  })
  if err != nil {
    panic(err.Error())
  }
  fmt.Printf("%+v\n", fineTuningJob.ID)
}

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

client.FineTuning.Jobs.List(ctx, query) (*CursorPage[FineTuningJob], error)

get /fine_tuning/jobs

List your organization's fine-tuning jobs

Parameters

• query FineTuningJobListParams

  • After param.Field[string]

    Identifier for the last job from the previous pagination request.

  • Limit param.Field[int64]

    Number of fine-tuning jobs to retrieve.

  • Metadata param.Field[map[string, string]]

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

Returns

• type FineTuningJob struct{…}

  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.

  • CreatedAt int64

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

  • Error FineTuningJobError

    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.

  • FineTunedModel 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.

  • FinishedAt int64

    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 FineTuningJobHyperparameters

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

    • BatchSize FineTuningJobHyperparametersBatchSizeUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • LearningRateMultiplier FineTuningJobHyperparametersLearningRateMultiplierUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • float64

    • NEpochs FineTuningJobHyperparametersNEpochsUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

  • Model string

    The base model that is being fine-tuned.

  • Object FineTuningJob

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

    • const FineTuningJobFineTuningJob FineTuningJob = "fine_tuning.job"

  • OrganizationID string

    The organization that owns the fine-tuning job.

  • ResultFiles []string

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

  • Seed int64

    The seed used for the fine-tuning job.

  • Status FineTuningJobStatus

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

    • const FineTuningJobStatusValidatingFiles FineTuningJobStatus = "validating_files"

    • const FineTuningJobStatusQueued FineTuningJobStatus = "queued"

    • const FineTuningJobStatusRunning FineTuningJobStatus = "running"

    • const FineTuningJobStatusSucceeded FineTuningJobStatus = "succeeded"

    • const FineTuningJobStatusFailed FineTuningJobStatus = "failed"

    • const FineTuningJobStatusCancelled FineTuningJobStatus = "cancelled"

  • TrainedTokens int64

    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.

  • TrainingFile string

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

  • ValidationFile string

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

  • EstimatedFinish int64

    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 []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

      • const WandbWandb Wandb = "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 []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 FineTuningJobMethod

    The method used for fine-tuning.

    • Type string

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

      • const FineTuningJobMethodTypeSupervised FineTuningJobMethodType = "supervised"

      • const FineTuningJobMethodTypeDpo FineTuningJobMethodType = "dpo"

      • const FineTuningJobMethodTypeReinforcement FineTuningJobMethodType = "reinforcement"

    • Dpo DpoMethod

      Configuration for the DPO fine-tuning method.

      • Hyperparameters DpoHyperparametersResp

        The hyperparameters used for the DPO fine-tuning job.

        • BatchSize DpoHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • Beta DpoHyperparametersBetaUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • LearningRateMultiplier DpoHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs DpoHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

    • Reinforcement ReinforcementMethod

      Configuration for the reinforcement fine-tuning method.

      • Grader ReinforcementMethodGraderUnion

        The grader used for the fine-tuning job.

        • type StringCheckGrader struct{…}

          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 StringCheckGraderOperation

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

            • const StringCheckGraderOperationEq StringCheckGraderOperation = "eq"

            • const StringCheckGraderOperationNe StringCheckGraderOperation = "ne"

            • const StringCheckGraderOperationLike StringCheckGraderOperation = "like"

            • const StringCheckGraderOperationIlike StringCheckGraderOperation = "ilike"

          • Reference string

            The reference text. This may include template strings.

          • Type StringCheck

            The object type, which is always string_check.

            • const StringCheckStringCheck StringCheck = "string_check"

        • type TextSimilarityGrader struct{…}

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • EvaluationMetric TextSimilarityGraderEvaluationMetric

            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.

            • const TextSimilarityGraderEvaluationMetricCosine TextSimilarityGraderEvaluationMetric = "cosine"

            • const TextSimilarityGraderEvaluationMetricFuzzyMatch TextSimilarityGraderEvaluationMetric = "fuzzy_match"

            • const TextSimilarityGraderEvaluationMetricBleu TextSimilarityGraderEvaluationMetric = "bleu"

            • const TextSimilarityGraderEvaluationMetricGleu TextSimilarityGraderEvaluationMetric = "gleu"

            • const TextSimilarityGraderEvaluationMetricMeteor TextSimilarityGraderEvaluationMetric = "meteor"

            • const TextSimilarityGraderEvaluationMetricRouge1 TextSimilarityGraderEvaluationMetric = "rouge_1"

            • const TextSimilarityGraderEvaluationMetricRouge2 TextSimilarityGraderEvaluationMetric = "rouge_2"

            • const TextSimilarityGraderEvaluationMetricRouge3 TextSimilarityGraderEvaluationMetric = "rouge_3"

            • const TextSimilarityGraderEvaluationMetricRouge4 TextSimilarityGraderEvaluationMetric = "rouge_4"

            • const TextSimilarityGraderEvaluationMetricRouge5 TextSimilarityGraderEvaluationMetric = "rouge_5"

            • const TextSimilarityGraderEvaluationMetricRougeL TextSimilarityGraderEvaluationMetric = "rouge_l"

          • Input string

            The text being graded.

          • Name string

            The name of the grader.

          • Reference string

            The text being graded against.

          • Type TextSimilarity

            The type of grader.

            • const TextSimilarityTextSimilarity TextSimilarity = "text_similarity"

        • type PythonGrader struct{…}

          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.

            • const PythonPython Python = "python"

          • ImageTag string

            The image tag to use for the python script.

        • type ScoreModelGrader struct{…}

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

          • Input []ScoreModelGraderInput

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

            • Content ScoreModelGraderInputContentUnion

              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

              • type ResponseInputText struct{…}

                A text input to the model.

                • Text string

                  The text input to the model.

                • Type InputText

                  The type of the input item. Always input_text.

                  • const InputTextInputText InputText = "input_text"

                • PromptCacheBreakpoint ResponseInputTextPromptCacheBreakpoint

                  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.

                    • const ExplicitExplicit Explicit = "explicit"

              • type ScoreModelGraderInputContentOutputText struct{…}

                A text output from the model.

                • Text string

                  The text output from the model.

                • Type OutputText

                  The type of the output text. Always output_text.

                  • const OutputTextOutputText OutputText = "output_text"

              • type ScoreModelGraderInputContentInputImage struct{…}

                An image input block used within EvalItem content arrays.

                • ImageURL string

                  The URL of the image input.

                • Type InputImage

                  The type of the image input. Always input_image.

                  • const InputImageInputImage InputImage = "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.

              • type ResponseInputAudio struct{…}

                An audio input to the model.

                • InputAudio ResponseInputAudioInputAudio

                  • Data string

                    Base64-encoded audio data.

                  • Format string

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

                    • const ResponseInputAudioInputAudioFormatMP3 ResponseInputAudioInputAudioFormat = "mp3"

                    • const ResponseInputAudioInputAudioFormatWAV ResponseInputAudioInputAudioFormat = "wav"

                • Type InputAudio

                  The type of the input item. Always input_audio.

                  • const InputAudioInputAudio InputAudio = "input_audio"

              • type GraderInputs []GraderInputUnion

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

                • string

                • type ResponseInputText struct{…}

                  A text input to the model.

                • type GraderInputOutputText struct{…}

                  A text output from the model.

                  • Text string

                    The text output from the model.

                  • Type OutputText

                    The type of the output text. Always output_text.

                    • const OutputTextOutputText OutputText = "output_text"

                • type GraderInputInputImage struct{…}

                  An image input block used within EvalItem content arrays.

                  • ImageURL string

                    The URL of the image input.

                  • Type InputImage

                    The type of the image input. Always input_image.

                    • const InputImageInputImage InputImage = "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.

                • type ResponseInputAudio struct{…}

                  An audio input to the model.

            • Role string

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

              • const ScoreModelGraderInputRoleUser ScoreModelGraderInputRole = "user"

              • const ScoreModelGraderInputRoleAssistant ScoreModelGraderInputRole = "assistant"

              • const ScoreModelGraderInputRoleSystem ScoreModelGraderInputRole = "system"

              • const ScoreModelGraderInputRoleDeveloper ScoreModelGraderInputRole = "developer"

            • Type string

              The type of the message input. Always message.

              • const ScoreModelGraderInputTypeMessage ScoreModelGraderInputType = "message"

          • Model string

            The model to use for the evaluation.

          • Name string

            The name of the grader.

          • Type ScoreModel

            The object type, which is always score_model.

            • const ScoreModelScoreModel ScoreModel = "score_model"

          • Range []float64

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

          • SamplingParams ScoreModelGraderSamplingParams

            The sampling parameters for the model.

            • MaxCompletionsTokens int64

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

            • ReasoningEffort 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.

              • const ReasoningEffortNone ReasoningEffort = "none"

              • const ReasoningEffortMinimal ReasoningEffort = "minimal"

              • const ReasoningEffortLow ReasoningEffort = "low"

              • const ReasoningEffortMedium ReasoningEffort = "medium"

              • const ReasoningEffortHigh ReasoningEffort = "high"

              • const ReasoningEffortXhigh ReasoningEffort = "xhigh"

              • const ReasoningEffortMax ReasoningEffort = "max"

            • Seed int64

              A seed value to initialize the randomness, during sampling.

            • Temperature float64

              A higher temperature increases randomness in the outputs.

            • TopP float64

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

        • type MultiGrader struct{…}

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

          • CalculateOutput string

            A formula to calculate the output based on grader results.

          • Graders MultiGraderGradersUnion

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

            • type StringCheckGrader struct{…}

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

            • type TextSimilarityGrader struct{…}

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • type PythonGrader struct{…}

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

            • type ScoreModelGrader struct{…}

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

            • type LabelModelGrader struct{…}

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

              • Input []LabelModelGraderInput

                • Content LabelModelGraderInputContentUnion

                  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

                  • type ResponseInputText struct{…}

                    A text input to the model.

                  • type LabelModelGraderInputContentOutputText struct{…}

                    A text output from the model.

                    • Text string

                      The text output from the model.

                    • Type OutputText

                      The type of the output text. Always output_text.

                      • const OutputTextOutputText OutputText = "output_text"

                  • type LabelModelGraderInputContentInputImage struct{…}

                    An image input block used within EvalItem content arrays.

                    • ImageURL string

                      The URL of the image input.

                    • Type InputImage

                      The type of the image input. Always input_image.

                      • const InputImageInputImage InputImage = "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.

                  • type ResponseInputAudio struct{…}

                    An audio input to the model.

                  • type GraderInputs []GraderInputUnion

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

                • Role string

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

                  • const LabelModelGraderInputRoleUser LabelModelGraderInputRole = "user"

                  • const LabelModelGraderInputRoleAssistant LabelModelGraderInputRole = "assistant"

                  • const LabelModelGraderInputRoleSystem LabelModelGraderInputRole = "system"

                  • const LabelModelGraderInputRoleDeveloper LabelModelGraderInputRole = "developer"

                • Type string

                  The type of the message input. Always message.

                  • const LabelModelGraderInputTypeMessage LabelModelGraderInputType = "message"

              • Labels []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.

              • PassingLabels []string

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

              • Type LabelModel

                The object type, which is always label_model.

                • const LabelModelLabelModel LabelModel = "label_model"

          • Name string

            The name of the grader.

          • Type Multi

            The object type, which is always multi.

            • const MultiMulti Multi = "multi"

      • Hyperparameters ReinforcementHyperparametersResp

        The hyperparameters used for the reinforcement fine-tuning job.

        • BatchSize ReinforcementHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ComputeMultiplier ReinforcementHyperparametersComputeMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • EvalInterval ReinforcementHyperparametersEvalIntervalUnionResp

          The number of training steps between evaluation runs.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • EvalSamples ReinforcementHyperparametersEvalSamplesUnionResp

          Number of evaluation samples to generate per training step.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier ReinforcementHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs ReinforcementHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ReasoningEffort ReinforcementHyperparametersReasoningEffort

          Level of reasoning effort.

          • const ReinforcementHyperparametersReasoningEffortDefault ReinforcementHyperparametersReasoningEffort = "default"

          • const ReinforcementHyperparametersReasoningEffortLow ReinforcementHyperparametersReasoningEffort = "low"

          • const ReinforcementHyperparametersReasoningEffortMedium ReinforcementHyperparametersReasoningEffort = "medium"

          • const ReinforcementHyperparametersReasoningEffortHigh ReinforcementHyperparametersReasoningEffort = "high"

    • Supervised SupervisedMethod

      Configuration for the supervised fine-tuning method.

      • Hyperparameters SupervisedHyperparametersResp

        The hyperparameters used for the fine-tuning job.

        • BatchSize SupervisedHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier SupervisedHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs SupervisedHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

Example

package main

import (
  "context"
  "fmt"

  "github.com/openai/openai-go"
  "github.com/openai/openai-go/option"
)

func main() {
  client := openai.NewClient(
    option.WithAPIKey("My API Key"),
  )
  page, err := client.FineTuning.Jobs.List(context.TODO(), openai.FineTuningJobListParams{

  })
  if err != nil {
    panic(err.Error())
  }
  fmt.Printf("%+v\n", 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

client.FineTuning.Jobs.Get(ctx, fineTuningJobID) (*FineTuningJob, error)

get /fine_tuning/jobs/{fine_tuning_job_id}

Get info about a fine-tuning job.

Learn more about fine-tuning

Parameters

• fineTuningJobID string

Returns

• type FineTuningJob struct{…}

  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.

  • CreatedAt int64

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

  • Error FineTuningJobError

    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.

  • FineTunedModel 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.

  • FinishedAt int64

    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 FineTuningJobHyperparameters

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

    • BatchSize FineTuningJobHyperparametersBatchSizeUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • LearningRateMultiplier FineTuningJobHyperparametersLearningRateMultiplierUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • float64

    • NEpochs FineTuningJobHyperparametersNEpochsUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

  • Model string

    The base model that is being fine-tuned.

  • Object FineTuningJob

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

    • const FineTuningJobFineTuningJob FineTuningJob = "fine_tuning.job"

  • OrganizationID string

    The organization that owns the fine-tuning job.

  • ResultFiles []string

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

  • Seed int64

    The seed used for the fine-tuning job.

  • Status FineTuningJobStatus

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

    • const FineTuningJobStatusValidatingFiles FineTuningJobStatus = "validating_files"

    • const FineTuningJobStatusQueued FineTuningJobStatus = "queued"

    • const FineTuningJobStatusRunning FineTuningJobStatus = "running"

    • const FineTuningJobStatusSucceeded FineTuningJobStatus = "succeeded"

    • const FineTuningJobStatusFailed FineTuningJobStatus = "failed"

    • const FineTuningJobStatusCancelled FineTuningJobStatus = "cancelled"

  • TrainedTokens int64

    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.

  • TrainingFile string

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

  • ValidationFile string

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

  • EstimatedFinish int64

    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 []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

      • const WandbWandb Wandb = "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 []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 FineTuningJobMethod

    The method used for fine-tuning.

    • Type string

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

      • const FineTuningJobMethodTypeSupervised FineTuningJobMethodType = "supervised"

      • const FineTuningJobMethodTypeDpo FineTuningJobMethodType = "dpo"

      • const FineTuningJobMethodTypeReinforcement FineTuningJobMethodType = "reinforcement"

    • Dpo DpoMethod

      Configuration for the DPO fine-tuning method.

      • Hyperparameters DpoHyperparametersResp

        The hyperparameters used for the DPO fine-tuning job.

        • BatchSize DpoHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • Beta DpoHyperparametersBetaUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • LearningRateMultiplier DpoHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs DpoHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

    • Reinforcement ReinforcementMethod

      Configuration for the reinforcement fine-tuning method.

      • Grader ReinforcementMethodGraderUnion

        The grader used for the fine-tuning job.

        • type StringCheckGrader struct{…}

          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 StringCheckGraderOperation

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

            • const StringCheckGraderOperationEq StringCheckGraderOperation = "eq"

            • const StringCheckGraderOperationNe StringCheckGraderOperation = "ne"

            • const StringCheckGraderOperationLike StringCheckGraderOperation = "like"

            • const StringCheckGraderOperationIlike StringCheckGraderOperation = "ilike"

          • Reference string

            The reference text. This may include template strings.

          • Type StringCheck

            The object type, which is always string_check.

            • const StringCheckStringCheck StringCheck = "string_check"

        • type TextSimilarityGrader struct{…}

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • EvaluationMetric TextSimilarityGraderEvaluationMetric

            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.

            • const TextSimilarityGraderEvaluationMetricCosine TextSimilarityGraderEvaluationMetric = "cosine"

            • const TextSimilarityGraderEvaluationMetricFuzzyMatch TextSimilarityGraderEvaluationMetric = "fuzzy_match"

            • const TextSimilarityGraderEvaluationMetricBleu TextSimilarityGraderEvaluationMetric = "bleu"

            • const TextSimilarityGraderEvaluationMetricGleu TextSimilarityGraderEvaluationMetric = "gleu"

            • const TextSimilarityGraderEvaluationMetricMeteor TextSimilarityGraderEvaluationMetric = "meteor"

            • const TextSimilarityGraderEvaluationMetricRouge1 TextSimilarityGraderEvaluationMetric = "rouge_1"

            • const TextSimilarityGraderEvaluationMetricRouge2 TextSimilarityGraderEvaluationMetric = "rouge_2"

            • const TextSimilarityGraderEvaluationMetricRouge3 TextSimilarityGraderEvaluationMetric = "rouge_3"

            • const TextSimilarityGraderEvaluationMetricRouge4 TextSimilarityGraderEvaluationMetric = "rouge_4"

            • const TextSimilarityGraderEvaluationMetricRouge5 TextSimilarityGraderEvaluationMetric = "rouge_5"

            • const TextSimilarityGraderEvaluationMetricRougeL TextSimilarityGraderEvaluationMetric = "rouge_l"

          • Input string

            The text being graded.

          • Name string

            The name of the grader.

          • Reference string

            The text being graded against.

          • Type TextSimilarity

            The type of grader.

            • const TextSimilarityTextSimilarity TextSimilarity = "text_similarity"

        • type PythonGrader struct{…}

          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.

            • const PythonPython Python = "python"

          • ImageTag string

            The image tag to use for the python script.

        • type ScoreModelGrader struct{…}

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

          • Input []ScoreModelGraderInput

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

            • Content ScoreModelGraderInputContentUnion

              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

              • type ResponseInputText struct{…}

                A text input to the model.

                • Text string

                  The text input to the model.

                • Type InputText

                  The type of the input item. Always input_text.

                  • const InputTextInputText InputText = "input_text"

                • PromptCacheBreakpoint ResponseInputTextPromptCacheBreakpoint

                  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.

                    • const ExplicitExplicit Explicit = "explicit"

              • type ScoreModelGraderInputContentOutputText struct{…}

                A text output from the model.

                • Text string

                  The text output from the model.

                • Type OutputText

                  The type of the output text. Always output_text.

                  • const OutputTextOutputText OutputText = "output_text"

              • type ScoreModelGraderInputContentInputImage struct{…}

                An image input block used within EvalItem content arrays.

                • ImageURL string

                  The URL of the image input.

                • Type InputImage

                  The type of the image input. Always input_image.

                  • const InputImageInputImage InputImage = "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.

              • type ResponseInputAudio struct{…}

                An audio input to the model.

                • InputAudio ResponseInputAudioInputAudio

                  • Data string

                    Base64-encoded audio data.

                  • Format string

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

                    • const ResponseInputAudioInputAudioFormatMP3 ResponseInputAudioInputAudioFormat = "mp3"

                    • const ResponseInputAudioInputAudioFormatWAV ResponseInputAudioInputAudioFormat = "wav"

                • Type InputAudio

                  The type of the input item. Always input_audio.

                  • const InputAudioInputAudio InputAudio = "input_audio"

              • type GraderInputs []GraderInputUnion

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

                • string

                • type ResponseInputText struct{…}

                  A text input to the model.

                • type GraderInputOutputText struct{…}

                  A text output from the model.

                  • Text string

                    The text output from the model.

                  • Type OutputText

                    The type of the output text. Always output_text.

                    • const OutputTextOutputText OutputText = "output_text"

                • type GraderInputInputImage struct{…}

                  An image input block used within EvalItem content arrays.

                  • ImageURL string

                    The URL of the image input.

                  • Type InputImage

                    The type of the image input. Always input_image.

                    • const InputImageInputImage InputImage = "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.

                • type ResponseInputAudio struct{…}

                  An audio input to the model.

            • Role string

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

              • const ScoreModelGraderInputRoleUser ScoreModelGraderInputRole = "user"

              • const ScoreModelGraderInputRoleAssistant ScoreModelGraderInputRole = "assistant"

              • const ScoreModelGraderInputRoleSystem ScoreModelGraderInputRole = "system"

              • const ScoreModelGraderInputRoleDeveloper ScoreModelGraderInputRole = "developer"

            • Type string

              The type of the message input. Always message.

              • const ScoreModelGraderInputTypeMessage ScoreModelGraderInputType = "message"

          • Model string

            The model to use for the evaluation.

          • Name string

            The name of the grader.

          • Type ScoreModel

            The object type, which is always score_model.

            • const ScoreModelScoreModel ScoreModel = "score_model"

          • Range []float64

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

          • SamplingParams ScoreModelGraderSamplingParams

            The sampling parameters for the model.

            • MaxCompletionsTokens int64

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

            • ReasoningEffort 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.

              • const ReasoningEffortNone ReasoningEffort = "none"

              • const ReasoningEffortMinimal ReasoningEffort = "minimal"

              • const ReasoningEffortLow ReasoningEffort = "low"

              • const ReasoningEffortMedium ReasoningEffort = "medium"

              • const ReasoningEffortHigh ReasoningEffort = "high"

              • const ReasoningEffortXhigh ReasoningEffort = "xhigh"

              • const ReasoningEffortMax ReasoningEffort = "max"

            • Seed int64

              A seed value to initialize the randomness, during sampling.

            • Temperature float64

              A higher temperature increases randomness in the outputs.

            • TopP float64

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

        • type MultiGrader struct{…}

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

          • CalculateOutput string

            A formula to calculate the output based on grader results.

          • Graders MultiGraderGradersUnion

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

            • type StringCheckGrader struct{…}

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

            • type TextSimilarityGrader struct{…}

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • type PythonGrader struct{…}

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

            • type ScoreModelGrader struct{…}

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

            • type LabelModelGrader struct{…}

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

              • Input []LabelModelGraderInput

                • Content LabelModelGraderInputContentUnion

                  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

                  • type ResponseInputText struct{…}

                    A text input to the model.

                  • type LabelModelGraderInputContentOutputText struct{…}

                    A text output from the model.

                    • Text string

                      The text output from the model.

                    • Type OutputText

                      The type of the output text. Always output_text.

                      • const OutputTextOutputText OutputText = "output_text"

                  • type LabelModelGraderInputContentInputImage struct{…}

                    An image input block used within EvalItem content arrays.

                    • ImageURL string

                      The URL of the image input.

                    • Type InputImage

                      The type of the image input. Always input_image.

                      • const InputImageInputImage InputImage = "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.

                  • type ResponseInputAudio struct{…}

                    An audio input to the model.

                  • type GraderInputs []GraderInputUnion

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

                • Role string

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

                  • const LabelModelGraderInputRoleUser LabelModelGraderInputRole = "user"

                  • const LabelModelGraderInputRoleAssistant LabelModelGraderInputRole = "assistant"

                  • const LabelModelGraderInputRoleSystem LabelModelGraderInputRole = "system"

                  • const LabelModelGraderInputRoleDeveloper LabelModelGraderInputRole = "developer"

                • Type string

                  The type of the message input. Always message.

                  • const LabelModelGraderInputTypeMessage LabelModelGraderInputType = "message"

              • Labels []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.

              • PassingLabels []string

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

              • Type LabelModel

                The object type, which is always label_model.

                • const LabelModelLabelModel LabelModel = "label_model"

          • Name string

            The name of the grader.

          • Type Multi

            The object type, which is always multi.

            • const MultiMulti Multi = "multi"

      • Hyperparameters ReinforcementHyperparametersResp

        The hyperparameters used for the reinforcement fine-tuning job.

        • BatchSize ReinforcementHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ComputeMultiplier ReinforcementHyperparametersComputeMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • EvalInterval ReinforcementHyperparametersEvalIntervalUnionResp

          The number of training steps between evaluation runs.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • EvalSamples ReinforcementHyperparametersEvalSamplesUnionResp

          Number of evaluation samples to generate per training step.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier ReinforcementHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs ReinforcementHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ReasoningEffort ReinforcementHyperparametersReasoningEffort

          Level of reasoning effort.

          • const ReinforcementHyperparametersReasoningEffortDefault ReinforcementHyperparametersReasoningEffort = "default"

          • const ReinforcementHyperparametersReasoningEffortLow ReinforcementHyperparametersReasoningEffort = "low"

          • const ReinforcementHyperparametersReasoningEffortMedium ReinforcementHyperparametersReasoningEffort = "medium"

          • const ReinforcementHyperparametersReasoningEffortHigh ReinforcementHyperparametersReasoningEffort = "high"

    • Supervised SupervisedMethod

      Configuration for the supervised fine-tuning method.

      • Hyperparameters SupervisedHyperparametersResp

        The hyperparameters used for the fine-tuning job.

        • BatchSize SupervisedHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier SupervisedHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs SupervisedHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

Example

package main

import (
  "context"
  "fmt"

  "github.com/openai/openai-go"
  "github.com/openai/openai-go/option"
)

func main() {
  client := openai.NewClient(
    option.WithAPIKey("My API Key"),
  )
  fineTuningJob, err := client.FineTuning.Jobs.Get(context.TODO(), "ft-AF1WoRqd3aJAHsqc9NY7iL8F")
  if err != nil {
    panic(err.Error())
  }
  fmt.Printf("%+v\n", fineTuningJob.ID)
}

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

client.FineTuning.Jobs.ListEvents(ctx, fineTuningJobID, query) (*CursorPage[FineTuningJobEvent], error)

get /fine_tuning/jobs/{fine_tuning_job_id}/events

Get status updates for a fine-tuning job.

Parameters

• fineTuningJobID string

• query FineTuningJobListEventsParams

  • After param.Field[string]

    Identifier for the last event from the previous pagination request.

  • Limit param.Field[int64]

    Number of events to retrieve.

Returns

• type FineTuningJobEvent struct{…}

  Fine-tuning job event object

  • ID string

    The object identifier.

  • CreatedAt int64

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

  • Level FineTuningJobEventLevel

    The log level of the event.

    • const FineTuningJobEventLevelInfo FineTuningJobEventLevel = "info"

    • const FineTuningJobEventLevelWarn FineTuningJobEventLevel = "warn"

    • const FineTuningJobEventLevelError FineTuningJobEventLevel = "error"

  • Message string

    The message of the event.

  • Object FineTuningJobEvent

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

    • const FineTuningJobEventFineTuningJobEvent FineTuningJobEvent = "fine_tuning.job.event"

  • Data any

    The data associated with the event.

  • Type FineTuningJobEventType

    The type of event.

    • const FineTuningJobEventTypeMessage FineTuningJobEventType = "message"

    • const FineTuningJobEventTypeMetrics FineTuningJobEventType = "metrics"

Example

package main

import (
  "context"
  "fmt"

  "github.com/openai/openai-go"
  "github.com/openai/openai-go/option"
)

func main() {
  client := openai.NewClient(
    option.WithAPIKey("My API Key"),
  )
  page, err := client.FineTuning.Jobs.ListEvents(
    context.TODO(),
    "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
    openai.FineTuningJobListEventsParams{

    },
  )
  if err != nil {
    panic(err.Error())
  }
  fmt.Printf("%+v\n", 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

client.FineTuning.Jobs.Cancel(ctx, fineTuningJobID) (*FineTuningJob, error)

post /fine_tuning/jobs/{fine_tuning_job_id}/cancel

Immediately cancel a fine-tune job.

Parameters

• fineTuningJobID string

Returns

• type FineTuningJob struct{…}

  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.

  • CreatedAt int64

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

  • Error FineTuningJobError

    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.

  • FineTunedModel 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.

  • FinishedAt int64

    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 FineTuningJobHyperparameters

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

    • BatchSize FineTuningJobHyperparametersBatchSizeUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • LearningRateMultiplier FineTuningJobHyperparametersLearningRateMultiplierUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • float64

    • NEpochs FineTuningJobHyperparametersNEpochsUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

  • Model string

    The base model that is being fine-tuned.

  • Object FineTuningJob

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

    • const FineTuningJobFineTuningJob FineTuningJob = "fine_tuning.job"

  • OrganizationID string

    The organization that owns the fine-tuning job.

  • ResultFiles []string

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

  • Seed int64

    The seed used for the fine-tuning job.

  • Status FineTuningJobStatus

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

    • const FineTuningJobStatusValidatingFiles FineTuningJobStatus = "validating_files"

    • const FineTuningJobStatusQueued FineTuningJobStatus = "queued"

    • const FineTuningJobStatusRunning FineTuningJobStatus = "running"

    • const FineTuningJobStatusSucceeded FineTuningJobStatus = "succeeded"

    • const FineTuningJobStatusFailed FineTuningJobStatus = "failed"

    • const FineTuningJobStatusCancelled FineTuningJobStatus = "cancelled"

  • TrainedTokens int64

    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.

  • TrainingFile string

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

  • ValidationFile string

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

  • EstimatedFinish int64

    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 []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

      • const WandbWandb Wandb = "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 []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 FineTuningJobMethod

    The method used for fine-tuning.

    • Type string

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

      • const FineTuningJobMethodTypeSupervised FineTuningJobMethodType = "supervised"

      • const FineTuningJobMethodTypeDpo FineTuningJobMethodType = "dpo"

      • const FineTuningJobMethodTypeReinforcement FineTuningJobMethodType = "reinforcement"

    • Dpo DpoMethod

      Configuration for the DPO fine-tuning method.

      • Hyperparameters DpoHyperparametersResp

        The hyperparameters used for the DPO fine-tuning job.

        • BatchSize DpoHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • Beta DpoHyperparametersBetaUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • LearningRateMultiplier DpoHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs DpoHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

    • Reinforcement ReinforcementMethod

      Configuration for the reinforcement fine-tuning method.

      • Grader ReinforcementMethodGraderUnion

        The grader used for the fine-tuning job.

        • type StringCheckGrader struct{…}

          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 StringCheckGraderOperation

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

            • const StringCheckGraderOperationEq StringCheckGraderOperation = "eq"

            • const StringCheckGraderOperationNe StringCheckGraderOperation = "ne"

            • const StringCheckGraderOperationLike StringCheckGraderOperation = "like"

            • const StringCheckGraderOperationIlike StringCheckGraderOperation = "ilike"

          • Reference string

            The reference text. This may include template strings.

          • Type StringCheck

            The object type, which is always string_check.

            • const StringCheckStringCheck StringCheck = "string_check"

        • type TextSimilarityGrader struct{…}

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • EvaluationMetric TextSimilarityGraderEvaluationMetric

            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.

            • const TextSimilarityGraderEvaluationMetricCosine TextSimilarityGraderEvaluationMetric = "cosine"

            • const TextSimilarityGraderEvaluationMetricFuzzyMatch TextSimilarityGraderEvaluationMetric = "fuzzy_match"

            • const TextSimilarityGraderEvaluationMetricBleu TextSimilarityGraderEvaluationMetric = "bleu"

            • const TextSimilarityGraderEvaluationMetricGleu TextSimilarityGraderEvaluationMetric = "gleu"

            • const TextSimilarityGraderEvaluationMetricMeteor TextSimilarityGraderEvaluationMetric = "meteor"

            • const TextSimilarityGraderEvaluationMetricRouge1 TextSimilarityGraderEvaluationMetric = "rouge_1"

            • const TextSimilarityGraderEvaluationMetricRouge2 TextSimilarityGraderEvaluationMetric = "rouge_2"

            • const TextSimilarityGraderEvaluationMetricRouge3 TextSimilarityGraderEvaluationMetric = "rouge_3"

            • const TextSimilarityGraderEvaluationMetricRouge4 TextSimilarityGraderEvaluationMetric = "rouge_4"

            • const TextSimilarityGraderEvaluationMetricRouge5 TextSimilarityGraderEvaluationMetric = "rouge_5"

            • const TextSimilarityGraderEvaluationMetricRougeL TextSimilarityGraderEvaluationMetric = "rouge_l"

          • Input string

            The text being graded.

          • Name string

            The name of the grader.

          • Reference string

            The text being graded against.

          • Type TextSimilarity

            The type of grader.

            • const TextSimilarityTextSimilarity TextSimilarity = "text_similarity"

        • type PythonGrader struct{…}

          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.

            • const PythonPython Python = "python"

          • ImageTag string

            The image tag to use for the python script.

        • type ScoreModelGrader struct{…}

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

          • Input []ScoreModelGraderInput

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

            • Content ScoreModelGraderInputContentUnion

              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

              • type ResponseInputText struct{…}

                A text input to the model.

                • Text string

                  The text input to the model.

                • Type InputText

                  The type of the input item. Always input_text.

                  • const InputTextInputText InputText = "input_text"

                • PromptCacheBreakpoint ResponseInputTextPromptCacheBreakpoint

                  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.

                    • const ExplicitExplicit Explicit = "explicit"

              • type ScoreModelGraderInputContentOutputText struct{…}

                A text output from the model.

                • Text string

                  The text output from the model.

                • Type OutputText

                  The type of the output text. Always output_text.

                  • const OutputTextOutputText OutputText = "output_text"

              • type ScoreModelGraderInputContentInputImage struct{…}

                An image input block used within EvalItem content arrays.

                • ImageURL string

                  The URL of the image input.

                • Type InputImage

                  The type of the image input. Always input_image.

                  • const InputImageInputImage InputImage = "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.

              • type ResponseInputAudio struct{…}

                An audio input to the model.

                • InputAudio ResponseInputAudioInputAudio

                  • Data string

                    Base64-encoded audio data.

                  • Format string

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

                    • const ResponseInputAudioInputAudioFormatMP3 ResponseInputAudioInputAudioFormat = "mp3"

                    • const ResponseInputAudioInputAudioFormatWAV ResponseInputAudioInputAudioFormat = "wav"

                • Type InputAudio

                  The type of the input item. Always input_audio.

                  • const InputAudioInputAudio InputAudio = "input_audio"

              • type GraderInputs []GraderInputUnion

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

                • string

                • type ResponseInputText struct{…}

                  A text input to the model.

                • type GraderInputOutputText struct{…}

                  A text output from the model.

                  • Text string

                    The text output from the model.

                  • Type OutputText

                    The type of the output text. Always output_text.

                    • const OutputTextOutputText OutputText = "output_text"

                • type GraderInputInputImage struct{…}

                  An image input block used within EvalItem content arrays.

                  • ImageURL string

                    The URL of the image input.

                  • Type InputImage

                    The type of the image input. Always input_image.

                    • const InputImageInputImage InputImage = "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.

                • type ResponseInputAudio struct{…}

                  An audio input to the model.

            • Role string

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

              • const ScoreModelGraderInputRoleUser ScoreModelGraderInputRole = "user"

              • const ScoreModelGraderInputRoleAssistant ScoreModelGraderInputRole = "assistant"

              • const ScoreModelGraderInputRoleSystem ScoreModelGraderInputRole = "system"

              • const ScoreModelGraderInputRoleDeveloper ScoreModelGraderInputRole = "developer"

            • Type string

              The type of the message input. Always message.

              • const ScoreModelGraderInputTypeMessage ScoreModelGraderInputType = "message"

          • Model string

            The model to use for the evaluation.

          • Name string

            The name of the grader.

          • Type ScoreModel

            The object type, which is always score_model.

            • const ScoreModelScoreModel ScoreModel = "score_model"

          • Range []float64

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

          • SamplingParams ScoreModelGraderSamplingParams

            The sampling parameters for the model.

            • MaxCompletionsTokens int64

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

            • ReasoningEffort 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.

              • const ReasoningEffortNone ReasoningEffort = "none"

              • const ReasoningEffortMinimal ReasoningEffort = "minimal"

              • const ReasoningEffortLow ReasoningEffort = "low"

              • const ReasoningEffortMedium ReasoningEffort = "medium"

              • const ReasoningEffortHigh ReasoningEffort = "high"

              • const ReasoningEffortXhigh ReasoningEffort = "xhigh"

              • const ReasoningEffortMax ReasoningEffort = "max"

            • Seed int64

              A seed value to initialize the randomness, during sampling.

            • Temperature float64

              A higher temperature increases randomness in the outputs.

            • TopP float64

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

        • type MultiGrader struct{…}

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

          • CalculateOutput string

            A formula to calculate the output based on grader results.

          • Graders MultiGraderGradersUnion

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

            • type StringCheckGrader struct{…}

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

            • type TextSimilarityGrader struct{…}

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • type PythonGrader struct{…}

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

            • type ScoreModelGrader struct{…}

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

            • type LabelModelGrader struct{…}

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

              • Input []LabelModelGraderInput

                • Content LabelModelGraderInputContentUnion

                  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

                  • type ResponseInputText struct{…}

                    A text input to the model.

                  • type LabelModelGraderInputContentOutputText struct{…}

                    A text output from the model.

                    • Text string

                      The text output from the model.

                    • Type OutputText

                      The type of the output text. Always output_text.

                      • const OutputTextOutputText OutputText = "output_text"

                  • type LabelModelGraderInputContentInputImage struct{…}

                    An image input block used within EvalItem content arrays.

                    • ImageURL string

                      The URL of the image input.

                    • Type InputImage

                      The type of the image input. Always input_image.

                      • const InputImageInputImage InputImage = "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.

                  • type ResponseInputAudio struct{…}

                    An audio input to the model.

                  • type GraderInputs []GraderInputUnion

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

                • Role string

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

                  • const LabelModelGraderInputRoleUser LabelModelGraderInputRole = "user"

                  • const LabelModelGraderInputRoleAssistant LabelModelGraderInputRole = "assistant"

                  • const LabelModelGraderInputRoleSystem LabelModelGraderInputRole = "system"

                  • const LabelModelGraderInputRoleDeveloper LabelModelGraderInputRole = "developer"

                • Type string

                  The type of the message input. Always message.

                  • const LabelModelGraderInputTypeMessage LabelModelGraderInputType = "message"

              • Labels []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.

              • PassingLabels []string

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

              • Type LabelModel

                The object type, which is always label_model.

                • const LabelModelLabelModel LabelModel = "label_model"

          • Name string

            The name of the grader.

          • Type Multi

            The object type, which is always multi.

            • const MultiMulti Multi = "multi"

      • Hyperparameters ReinforcementHyperparametersResp

        The hyperparameters used for the reinforcement fine-tuning job.

        • BatchSize ReinforcementHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ComputeMultiplier ReinforcementHyperparametersComputeMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • EvalInterval ReinforcementHyperparametersEvalIntervalUnionResp

          The number of training steps between evaluation runs.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • EvalSamples ReinforcementHyperparametersEvalSamplesUnionResp

          Number of evaluation samples to generate per training step.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier ReinforcementHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs ReinforcementHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ReasoningEffort ReinforcementHyperparametersReasoningEffort

          Level of reasoning effort.

          • const ReinforcementHyperparametersReasoningEffortDefault ReinforcementHyperparametersReasoningEffort = "default"

          • const ReinforcementHyperparametersReasoningEffortLow ReinforcementHyperparametersReasoningEffort = "low"

          • const ReinforcementHyperparametersReasoningEffortMedium ReinforcementHyperparametersReasoningEffort = "medium"

          • const ReinforcementHyperparametersReasoningEffortHigh ReinforcementHyperparametersReasoningEffort = "high"

    • Supervised SupervisedMethod

      Configuration for the supervised fine-tuning method.

      • Hyperparameters SupervisedHyperparametersResp

        The hyperparameters used for the fine-tuning job.

        • BatchSize SupervisedHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier SupervisedHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs SupervisedHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

Example

package main

import (
  "context"
  "fmt"

  "github.com/openai/openai-go"
  "github.com/openai/openai-go/option"
)

func main() {
  client := openai.NewClient(
    option.WithAPIKey("My API Key"),
  )
  fineTuningJob, err := client.FineTuning.Jobs.Cancel(context.TODO(), "ft-AF1WoRqd3aJAHsqc9NY7iL8F")
  if err != nil {
    panic(err.Error())
  }
  fmt.Printf("%+v\n", fineTuningJob.ID)
}

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

client.FineTuning.Jobs.Pause(ctx, fineTuningJobID) (*FineTuningJob, error)

post /fine_tuning/jobs/{fine_tuning_job_id}/pause

Pause a fine-tune job.

Parameters

• fineTuningJobID string

Returns

• type FineTuningJob struct{…}

  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.

  • CreatedAt int64

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

  • Error FineTuningJobError

    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.

  • FineTunedModel 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.

  • FinishedAt int64

    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 FineTuningJobHyperparameters

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

    • BatchSize FineTuningJobHyperparametersBatchSizeUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

    • LearningRateMultiplier FineTuningJobHyperparametersLearningRateMultiplierUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • float64

    • NEpochs FineTuningJobHyperparametersNEpochsUnion

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

      • type Auto string

        • const AutoAuto Auto = "auto"

      • int64

  • Model string

    The base model that is being fine-tuned.

  • Object FineTuningJob

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

    • const FineTuningJobFineTuningJob FineTuningJob = "fine_tuning.job"

  • OrganizationID string

    The organization that owns the fine-tuning job.

  • ResultFiles []string

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

  • Seed int64

    The seed used for the fine-tuning job.

  • Status FineTuningJobStatus

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

    • const FineTuningJobStatusValidatingFiles FineTuningJobStatus = "validating_files"

    • const FineTuningJobStatusQueued FineTuningJobStatus = "queued"

    • const FineTuningJobStatusRunning FineTuningJobStatus = "running"

    • const FineTuningJobStatusSucceeded FineTuningJobStatus = "succeeded"

    • const FineTuningJobStatusFailed FineTuningJobStatus = "failed"

    • const FineTuningJobStatusCancelled FineTuningJobStatus = "cancelled"

  • TrainedTokens int64

    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.

  • TrainingFile string

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

  • ValidationFile string

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

  • EstimatedFinish int64

    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 []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

      • const WandbWandb Wandb = "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 []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 FineTuningJobMethod

    The method used for fine-tuning.

    • Type string

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

      • const FineTuningJobMethodTypeSupervised FineTuningJobMethodType = "supervised"

      • const FineTuningJobMethodTypeDpo FineTuningJobMethodType = "dpo"

      • const FineTuningJobMethodTypeReinforcement FineTuningJobMethodType = "reinforcement"

    • Dpo DpoMethod

      Configuration for the DPO fine-tuning method.

      • Hyperparameters DpoHyperparametersResp

        The hyperparameters used for the DPO fine-tuning job.

        • BatchSize DpoHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • Beta DpoHyperparametersBetaUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • LearningRateMultiplier DpoHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs DpoHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

    • Reinforcement ReinforcementMethod

      Configuration for the reinforcement fine-tuning method.

      • Grader ReinforcementMethodGraderUnion

        The grader used for the fine-tuning job.

        • type StringCheckGrader struct{…}

          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 StringCheckGraderOperation

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

            • const StringCheckGraderOperationEq StringCheckGraderOperation = "eq"

            • const StringCheckGraderOperationNe StringCheckGraderOperation = "ne"

            • const StringCheckGraderOperationLike StringCheckGraderOperation = "like"

            • const StringCheckGraderOperationIlike StringCheckGraderOperation = "ilike"

          • Reference string

            The reference text. This may include template strings.

          • Type StringCheck

            The object type, which is always string_check.

            • const StringCheckStringCheck StringCheck = "string_check"

        • type TextSimilarityGrader struct{…}

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • EvaluationMetric TextSimilarityGraderEvaluationMetric

            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.

            • const TextSimilarityGraderEvaluationMetricCosine TextSimilarityGraderEvaluationMetric = "cosine"

            • const TextSimilarityGraderEvaluationMetricFuzzyMatch TextSimilarityGraderEvaluationMetric = "fuzzy_match"

            • const TextSimilarityGraderEvaluationMetricBleu TextSimilarityGraderEvaluationMetric = "bleu"

            • const TextSimilarityGraderEvaluationMetricGleu TextSimilarityGraderEvaluationMetric = "gleu"

            • const TextSimilarityGraderEvaluationMetricMeteor TextSimilarityGraderEvaluationMetric = "meteor"

            • const TextSimilarityGraderEvaluationMetricRouge1 TextSimilarityGraderEvaluationMetric = "rouge_1"

            • const TextSimilarityGraderEvaluationMetricRouge2 TextSimilarityGraderEvaluationMetric = "rouge_2"

            • const TextSimilarityGraderEvaluationMetricRouge3 TextSimilarityGraderEvaluationMetric = "rouge_3"

            • const TextSimilarityGraderEvaluationMetricRouge4 TextSimilarityGraderEvaluationMetric = "rouge_4"

            • const TextSimilarityGraderEvaluationMetricRouge5 TextSimilarityGraderEvaluationMetric = "rouge_5"

            • const TextSimilarityGraderEvaluationMetricRougeL TextSimilarityGraderEvaluationMetric = "rouge_l"

          • Input string

            The text being graded.

          • Name string

            The name of the grader.

          • Reference string

            The text being graded against.

          • Type TextSimilarity

            The type of grader.

            • const TextSimilarityTextSimilarity TextSimilarity = "text_similarity"

        • type PythonGrader struct{…}

          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.

            • const PythonPython Python = "python"

          • ImageTag string

            The image tag to use for the python script.

        • type ScoreModelGrader struct{…}

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

          • Input []ScoreModelGraderInput

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

            • Content ScoreModelGraderInputContentUnion

              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

              • type ResponseInputText struct{…}

                A text input to the model.

                • Text string

                  The text input to the model.

                • Type InputText

                  The type of the input item. Always input_text.

                  • const InputTextInputText InputText = "input_text"

                • PromptCacheBreakpoint ResponseInputTextPromptCacheBreakpoint

                  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.

                    • const ExplicitExplicit Explicit = "explicit"

              • type ScoreModelGraderInputContentOutputText struct{…}

                A text output from the model.

                • Text string

                  The text output from the model.

                • Type OutputText

                  The type of the output text. Always output_text.

                  • const OutputTextOutputText OutputText = "output_text"

              • type ScoreModelGraderInputContentInputImage struct{…}

                An image input block used within EvalItem content arrays.

                • ImageURL string

                  The URL of the image input.

                • Type InputImage

                  The type of the image input. Always input_image.

                  • const InputImageInputImage InputImage = "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.

              • type ResponseInputAudio struct{…}

                An audio input to the model.

                • InputAudio ResponseInputAudioInputAudio

                  • Data string

                    Base64-encoded audio data.

                  • Format string

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

                    • const ResponseInputAudioInputAudioFormatMP3 ResponseInputAudioInputAudioFormat = "mp3"

                    • const ResponseInputAudioInputAudioFormatWAV ResponseInputAudioInputAudioFormat = "wav"

                • Type InputAudio

                  The type of the input item. Always input_audio.

                  • const InputAudioInputAudio InputAudio = "input_audio"

              • type GraderInputs []GraderInputUnion

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

                • string

                • type ResponseInputText struct{…}

                  A text input to the model.

                • type GraderInputOutputText struct{…}

                  A text output from the model.

                  • Text string

                    The text output from the model.

                  • Type OutputText

                    The type of the output text. Always output_text.

                    • const OutputTextOutputText OutputText = "output_text"

                • type GraderInputInputImage struct{…}

                  An image input block used within EvalItem content arrays.

                  • ImageURL string

                    The URL of the image input.

                  • Type InputImage

                    The type of the image input. Always input_image.

                    • const InputImageInputImage InputImage = "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.

                • type ResponseInputAudio struct{…}

                  An audio input to the model.

            • Role string

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

              • const ScoreModelGraderInputRoleUser ScoreModelGraderInputRole = "user"

              • const ScoreModelGraderInputRoleAssistant ScoreModelGraderInputRole = "assistant"

              • const ScoreModelGraderInputRoleSystem ScoreModelGraderInputRole = "system"

              • const ScoreModelGraderInputRoleDeveloper ScoreModelGraderInputRole = "developer"

            • Type string

              The type of the message input. Always message.

              • const ScoreModelGraderInputTypeMessage ScoreModelGraderInputType = "message"

          • Model string

            The model to use for the evaluation.

          • Name string

            The name of the grader.

          • Type ScoreModel

            The object type, which is always score_model.

            • const ScoreModelScoreModel ScoreModel = "score_model"

          • Range []float64

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

          • SamplingParams ScoreModelGraderSamplingParams

            The sampling parameters for the model.

            • MaxCompletionsTokens int64

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

            • ReasoningEffort 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.

              • const ReasoningEffortNone ReasoningEffort = "none"

              • const ReasoningEffortMinimal ReasoningEffort = "minimal"

              • const ReasoningEffortLow ReasoningEffort = "low"

              • const ReasoningEffortMedium ReasoningEffort = "medium"

              • const ReasoningEffortHigh ReasoningEffort = "high"

              • const ReasoningEffortXhigh ReasoningEffort = "xhigh"

              • const ReasoningEffortMax ReasoningEffort = "max"

            • Seed int64

              A seed value to initialize the randomness, during sampling.

            • Temperature float64

              A higher temperature increases randomness in the outputs.

            • TopP float64

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

        • type MultiGrader struct{…}

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

          • CalculateOutput string

            A formula to calculate the output based on grader results.

          • Graders MultiGraderGradersUnion

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

            • type StringCheckGrader struct{…}

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

            • type TextSimilarityGrader struct{…}

              A TextSimilarityGrader object which grades text based on similarity metrics.

            • type PythonGrader struct{…}

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

            • type ScoreModelGrader struct{…}

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

            • type LabelModelGrader struct{…}

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

              • Input []LabelModelGraderInput

                • Content LabelModelGraderInputContentUnion

                  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

                  • type ResponseInputText struct{…}

                    A text input to the model.

                  • type LabelModelGraderInputContentOutputText struct{…}

                    A text output from the model.

                    • Text string

                      The text output from the model.

                    • Type OutputText

                      The type of the output text. Always output_text.

                      • const OutputTextOutputText OutputText = "output_text"

                  • type LabelModelGraderInputContentInputImage struct{…}

                    An image input block used within EvalItem content arrays.

                    • ImageURL string

                      The URL of the image input.

                    • Type InputImage

                      The type of the image input. Always input_image.

                      • const InputImageInputImage InputImage = "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.

                  • type ResponseInputAudio struct{…}

                    An audio input to the model.

                  • type GraderInputs []GraderInputUnion

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

                • Role string

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

                  • const LabelModelGraderInputRoleUser LabelModelGraderInputRole = "user"

                  • const LabelModelGraderInputRoleAssistant LabelModelGraderInputRole = "assistant"

                  • const LabelModelGraderInputRoleSystem LabelModelGraderInputRole = "system"

                  • const LabelModelGraderInputRoleDeveloper LabelModelGraderInputRole = "developer"

                • Type string

                  The type of the message input. Always message.

                  • const LabelModelGraderInputTypeMessage LabelModelGraderInputType = "message"

              • Labels []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.

              • PassingLabels []string

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

              • Type LabelModel

                The object type, which is always label_model.

                • const LabelModelLabelModel LabelModel = "label_model"

          • Name string

            The name of the grader.

          • Type Multi

            The object type, which is always multi.

            • const MultiMulti Multi = "multi"

      • Hyperparameters ReinforcementHyperparametersResp

        The hyperparameters used for the reinforcement fine-tuning job.

        • BatchSize ReinforcementHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ComputeMultiplier ReinforcementHyperparametersComputeMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • EvalInterval ReinforcementHyperparametersEvalIntervalUnionResp

          The number of training steps between evaluation runs.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • EvalSamples ReinforcementHyperparametersEvalSamplesUnionResp

          Number of evaluation samples to generate per training step.

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier ReinforcementHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs ReinforcementHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • ReasoningEffort ReinforcementHyperparametersReasoningEffort

          Level of reasoning effort.

          • const ReinforcementHyperparametersReasoningEffortDefault ReinforcementHyperparametersReasoningEffort = "default"

          • const ReinforcementHyperparametersReasoningEffortLow ReinforcementHyperparametersReasoningEffort = "low"

          • const ReinforcementHyperparametersReasoningEffortMedium ReinforcementHyperparametersReasoningEffort = "medium"

          • const ReinforcementHyperparametersReasoningEffortHigh ReinforcementHyperparametersReasoningEffort = "high"

    • Supervised SupervisedMethod

      Configuration for the supervised fine-tuning method.

      • Hyperparameters SupervisedHyperparametersResp

        The hyperparameters used for the fine-tuning job.

        • BatchSize SupervisedHyperparametersBatchSizeUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64

        • LearningRateMultiplier SupervisedHyperparametersLearningRateMultiplierUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • float64

        • NEpochs SupervisedHyperparametersNEpochsUnionResp

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

          • type Auto string

            • const AutoAuto Auto = "auto"

          • int64