Jobs

Create fine-tuning job

FineTuningJob fineTuning().jobs().create(JobCreateParamsparams, RequestOptionsrequestOptions = RequestOptions.none())

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

JobCreateParams params
- Model model
  
  The name of the model to fine-tune. You can select one of the supported models.
  - BABBAGE_002("babbage-002")
  - DAVINCI_002("davinci-002")
  - GPT_3_5_TURBO("gpt-3.5-turbo")
  - GPT_4O_MINI("gpt-4o-mini")
- String trainingFile
  
  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.
- Optional<Hyperparameters> hyperparameters
  
  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.
  - Optional<BatchSize> batchSize
    
    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
    - JsonValue;
      - AUTO("auto")
    - long
  - Optional<LearningRateMultiplier> learningRateMultiplier
    
    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
    - JsonValue;
      - AUTO("auto")
    - double
  - Optional<NEpochs> nEpochs
    
    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
    - JsonValue;
      - AUTO("auto")
    - long
- Optional<List<Integration>> integrations
  
  A list of integrations to enable for your fine-tuning job.
  - JsonValue; type "wandb"constant
    
    The type of integration to enable. Currently, only "wandb" (Weights and Biases) is supported.
    - WANDB("wandb")
  - Wandb wandb
    
    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.
    - String project
      
      The name of the project that the new run will be created under.
    - Optional<String> entity
      
      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.
    - Optional<String> name
      
      A display name to set for the run. If not set, we will use the Job ID as the name.
    - Optional<List<String>> tags
      
      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}".
- Optional<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.
- Optional<Method> method
  
  The method used for fine-tuning.
  - Type type
    
    The type of method. Is either supervised, dpo, or reinforcement.
    - SUPERVISED("supervised")
    - DPO("dpo")
    - REINFORCEMENT("reinforcement")
  - Optional<DpoMethod> dpo
    
    Configuration for the DPO fine-tuning method.
    - Optional<DpoHyperparameters> hyperparameters
      
      The hyperparameters used for the DPO fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<Beta> beta
        
        The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
  - Optional<ReinforcementMethod> reinforcement
    
    Configuration for the reinforcement fine-tuning method.
    - Grader grader
      
      The grader used for the fine-tuning job.
      - class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        String input
        
        The input text. This may include template strings.
        
        String name
        
        The name of the grader.
        
        Operation operation
        
        The string check operation to perform. One of eq, ne, like, or ilike.
        
        EQ("eq")
        
        NE("ne")
        
        LIKE("like")
        
        ILIKE("ilike")
        
        String reference
        
        The reference text. This may include template strings.
        
        JsonValue; type "string_check"constant
        
        The object type, which is always string_check.
        
        STRING_CHECK("string_check")
      - class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        EvaluationMetric evaluationMetric
        
        The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.
        
        COSINE("cosine")
        
        FUZZY_MATCH("fuzzy_match")
        
        BLEU("bleu")
        
        GLEU("gleu")
        
        METEOR("meteor")
        
        ROUGE_1("rouge_1")
        
        ROUGE_2("rouge_2")
        
        ROUGE_3("rouge_3")
        
        ROUGE_4("rouge_4")
        
        ROUGE_5("rouge_5")
        
        ROUGE_L("rouge_l")
        
        String input
        
        The text being graded.
        
        String name
        
        The name of the grader.
        
        String reference
        
        The text being graded against.
        
        JsonValue; type "text_similarity"constant
        
        The type of grader.
        
        TEXT_SIMILARITY("text_similarity")
      - class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        String name
        
        The name of the grader.
        
        String source
        
        The source code of the python script.
        
        JsonValue; type "python"constant
        
        The object type, which is always python.
        
        PYTHON("python")
        
        Optional<String> imageTag
        
        The image tag to use for the python script.
      - class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        List<Input> input
        
        The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        String text
        
        The text input to the model.
        
        JsonValue; type "input_text"constant
        
        The type of the input item. Always input_text.
        
        INPUT_TEXT("input_text")
        
        Optional<PromptCacheBreakpoint> promptCacheBreakpoint
        
        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.
        
        JsonValue; mode "explicit"constant
        
        The breakpoint mode. Always explicit.
        
        EXPLICIT("explicit")
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        InputAudio inputAudio
        
        String data
        
        Base64-encoded audio data.
        
        Format format
        
        The format of the audio data. Currently supported formats are mp3 and wav.
        
        MP3("mp3")
        
        WAV("wav")
        
        JsonValue; type "input_audio"constant
        
        The type of the input item. Always input_audio.
        
        INPUT_AUDIO("input_audio")
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        InputImage
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        String model
        
        The model to use for the evaluation.
        
        String name
        
        The name of the grader.
        
        JsonValue; type "score_model"constant
        
        The object type, which is always score_model.
        
        SCORE_MODEL("score_model")
        
        Optional<List<Double>> range
        
        The range of the score. Defaults to [0, 1].
        
        Optional<SamplingParams> samplingParams
        
        The sampling parameters for the model.
        
        Optional<Long> maxCompletionsTokens
        
        The maximum number of tokens the grader model may generate in its response.
        
        Optional<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.
        
        NONE("none")
        
        MINIMAL("minimal")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
        
        XHIGH("xhigh")
        
        MAX("max")
        
        Optional<Long> seed
        
        A seed value to initialize the randomness, during sampling.
        
        Optional<Double> temperature
        
        A higher temperature increases randomness in the outputs.
        
        Optional<Double> topP
        
        An alternative to temperature for nucleus sampling; 1.0 includes all tokens.
      - class MultiGrader:
        
        A MultiGrader object combines the output of multiple graders to produce a single score.
        
        String calculateOutput
        
        A formula to calculate the output based on grader results.
        
        Graders graders
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        class LabelModelGrader:
        
        A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.
        
        List<Input> input
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        InputImage
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        List<String> labels
        
        The labels to assign to each item in the evaluation.
        
        String model
        
        The model to use for the evaluation. Must support structured outputs.
        
        String name
        
        The name of the grader.
        
        List<String> passingLabels
        
        The labels that indicate a passing result. Must be a subset of labels.
        
        JsonValue; type "label_model"constant
        
        The object type, which is always label_model.
        
        LABEL_MODEL("label_model")
        
        String name
        
        The name of the grader.
        
        JsonValue; type "multi"constant
        
        The object type, which is always multi.
        
        MULTI("multi")
    - Optional<ReinforcementHyperparameters> hyperparameters
      
      The hyperparameters used for the reinforcement fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ComputeMultiplier> computeMultiplier
        
        Multiplier on amount of compute used for exploring search space during training.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<EvalInterval> evalInterval
        
        The number of training steps between evaluation runs.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<EvalSamples> evalSamples
        
        Number of evaluation samples to generate per training step.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ReasoningEffort> reasoningEffort
        
        Level of reasoning effort.
        
        DEFAULT("default")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
  - Optional<SupervisedMethod> supervised
    
    Configuration for the supervised fine-tuning method.
    - Optional<SupervisedHyperparameters> hyperparameters
      
      The hyperparameters used for the fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
- Optional<Long> seed
  
  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.
- Optional<String> suffix
  
  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.
- Optional<String> validationFile
  
  The ID of an uploaded file that contains validation data.
  
  If you provide this file, the data is used to generate validation metrics periodically during fine-tuning. These metrics can be viewed in the fine-tuning results file. The same data should not be present in both train and validation files.
  
  Your dataset must be formatted as a JSONL file. You must upload your file with the purpose fine-tune.
  
  See the fine-tuning guide for more details.

Returns

class FineTuningJob:

The fine_tuning.job object represents a fine-tuning job that has been created through the API.
- String id
  
  The object identifier, which can be referenced in the API endpoints.
- long createdAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was created.
- Optional<Error> error
  
  For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.
  - String code
    
    A machine-readable error code.
  - String message
    
    A human-readable error message.
  - Optional<String> param
    
    The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.
- Optional<String> fineTunedModel
  
  The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.
- Optional<Long> finishedAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.
- Hyperparameters hyperparameters
  
  The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.
  - Optional<BatchSize> batchSize
    
    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
    - JsonValue;
      - AUTO("auto")
    - long
  - Optional<LearningRateMultiplier> learningRateMultiplier
    
    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
    - JsonValue;
      - AUTO("auto")
    - double
  - Optional<NEpochs> nEpochs
    
    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
    - JsonValue;
      - AUTO("auto")
    - long
- String model
  
  The base model that is being fine-tuned.
- JsonValue; object_ "fine_tuning.job"constant
  
  The object type, which is always "fine_tuning.job".
  - FINE_TUNING_JOB("fine_tuning.job")
- String organizationId
  
  The organization that owns the fine-tuning job.
- List<String> resultFiles
  
  The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.
- long seed
  
  The seed used for the fine-tuning job.
- Status status
  
  The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.
  - VALIDATING_FILES("validating_files")
  - QUEUED("queued")
  - RUNNING("running")
  - SUCCEEDED("succeeded")
  - FAILED("failed")
  - CANCELLED("cancelled")
- Optional<Long> trainedTokens
  
  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.
- String trainingFile
  
  The file ID used for training. You can retrieve the training data with the Files API.
- Optional<String> validationFile
  
  The file ID used for validation. You can retrieve the validation results with the Files API.
- Optional<Long> estimatedFinish
  
  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.
- Optional<List<FineTuningJobWandbIntegrationObject>> integrations
  
  A list of integrations to enable for this fine-tuning job.
  - JsonValue; type "wandb"constant
    
    The type of the integration being enabled for the fine-tuning job
    - WANDB("wandb")
  - FineTuningJobWandbIntegration wandb
    
    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.
    - String project
      
      The name of the project that the new run will be created under.
    - Optional<String> entity
      
      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.
    - Optional<String> name
      
      A display name to set for the run. If not set, we will use the Job ID as the name.
    - Optional<List<String>> tags
      
      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}".
- Optional<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.
- Optional<Method> method
  
  The method used for fine-tuning.
  - Type type
    
    The type of method. Is either supervised, dpo, or reinforcement.
    - SUPERVISED("supervised")
    - DPO("dpo")
    - REINFORCEMENT("reinforcement")
  - Optional<DpoMethod> dpo
    
    Configuration for the DPO fine-tuning method.
    - Optional<DpoHyperparameters> hyperparameters
      
      The hyperparameters used for the DPO fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<Beta> beta
        
        The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
  - Optional<ReinforcementMethod> reinforcement
    
    Configuration for the reinforcement fine-tuning method.
    - Grader grader
      
      The grader used for the fine-tuning job.
      - class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        String input
        
        The input text. This may include template strings.
        
        String name
        
        The name of the grader.
        
        Operation operation
        
        The string check operation to perform. One of eq, ne, like, or ilike.
        
        EQ("eq")
        
        NE("ne")
        
        LIKE("like")
        
        ILIKE("ilike")
        
        String reference
        
        The reference text. This may include template strings.
        
        JsonValue; type "string_check"constant
        
        The object type, which is always string_check.
        
        STRING_CHECK("string_check")
      - class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        EvaluationMetric evaluationMetric
        
        The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.
        
        COSINE("cosine")
        
        FUZZY_MATCH("fuzzy_match")
        
        BLEU("bleu")
        
        GLEU("gleu")
        
        METEOR("meteor")
        
        ROUGE_1("rouge_1")
        
        ROUGE_2("rouge_2")
        
        ROUGE_3("rouge_3")
        
        ROUGE_4("rouge_4")
        
        ROUGE_5("rouge_5")
        
        ROUGE_L("rouge_l")
        
        String input
        
        The text being graded.
        
        String name
        
        The name of the grader.
        
        String reference
        
        The text being graded against.
        
        JsonValue; type "text_similarity"constant
        
        The type of grader.
        
        TEXT_SIMILARITY("text_similarity")
      - class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        String name
        
        The name of the grader.
        
        String source
        
        The source code of the python script.
        
        JsonValue; type "python"constant
        
        The object type, which is always python.
        
        PYTHON("python")
        
        Optional<String> imageTag
        
        The image tag to use for the python script.
      - class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        List<Input> input
        
        The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        String text
        
        The text input to the model.
        
        JsonValue; type "input_text"constant
        
        The type of the input item. Always input_text.
        
        INPUT_TEXT("input_text")
        
        Optional<PromptCacheBreakpoint> promptCacheBreakpoint
        
        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.
        
        JsonValue; mode "explicit"constant
        
        The breakpoint mode. Always explicit.
        
        EXPLICIT("explicit")
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        InputAudio inputAudio
        
        String data
        
        Base64-encoded audio data.
        
        Format format
        
        The format of the audio data. Currently supported formats are mp3 and wav.
        
        MP3("mp3")
        
        WAV("wav")
        
        JsonValue; type "input_audio"constant
        
        The type of the input item. Always input_audio.
        
        INPUT_AUDIO("input_audio")
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        InputImage
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        String model
        
        The model to use for the evaluation.
        
        String name
        
        The name of the grader.
        
        JsonValue; type "score_model"constant
        
        The object type, which is always score_model.
        
        SCORE_MODEL("score_model")
        
        Optional<List<Double>> range
        
        The range of the score. Defaults to [0, 1].
        
        Optional<SamplingParams> samplingParams
        
        The sampling parameters for the model.
        
        Optional<Long> maxCompletionsTokens
        
        The maximum number of tokens the grader model may generate in its response.
        
        Optional<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.
        
        NONE("none")
        
        MINIMAL("minimal")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
        
        XHIGH("xhigh")
        
        MAX("max")
        
        Optional<Long> seed
        
        A seed value to initialize the randomness, during sampling.
        
        Optional<Double> temperature
        
        A higher temperature increases randomness in the outputs.
        
        Optional<Double> topP
        
        An alternative to temperature for nucleus sampling; 1.0 includes all tokens.
      - class MultiGrader:
        
        A MultiGrader object combines the output of multiple graders to produce a single score.
        
        String calculateOutput
        
        A formula to calculate the output based on grader results.
        
        Graders graders
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        class LabelModelGrader:
        
        A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.
        
        List<Input> input
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        InputImage
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        List<String> labels
        
        The labels to assign to each item in the evaluation.
        
        String model
        
        The model to use for the evaluation. Must support structured outputs.
        
        String name
        
        The name of the grader.
        
        List<String> passingLabels
        
        The labels that indicate a passing result. Must be a subset of labels.
        
        JsonValue; type "label_model"constant
        
        The object type, which is always label_model.
        
        LABEL_MODEL("label_model")
        
        String name
        
        The name of the grader.
        
        JsonValue; type "multi"constant
        
        The object type, which is always multi.
        
        MULTI("multi")
    - Optional<ReinforcementHyperparameters> hyperparameters
      
      The hyperparameters used for the reinforcement fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ComputeMultiplier> computeMultiplier
        
        Multiplier on amount of compute used for exploring search space during training.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<EvalInterval> evalInterval
        
        The number of training steps between evaluation runs.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<EvalSamples> evalSamples
        
        Number of evaluation samples to generate per training step.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ReasoningEffort> reasoningEffort
        
        Level of reasoning effort.
        
        DEFAULT("default")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
  - Optional<SupervisedMethod> supervised
    
    Configuration for the supervised fine-tuning method.
    - Optional<SupervisedHyperparameters> hyperparameters
      
      The hyperparameters used for the fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long

Example

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.finetuning.jobs.FineTuningJob;
import com.openai.models.finetuning.jobs.JobCreateParams;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        JobCreateParams params = JobCreateParams.builder()
            .model(JobCreateParams.Model.GPT_4O_MINI)
            .trainingFile("file-abc123")
            .build();
        FineTuningJob fineTuningJob = client.fineTuning().jobs().create(params);
    }
}

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

JobListPage fineTuning().jobs().list(JobListParamsparams = JobListParams.none(), RequestOptionsrequestOptions = RequestOptions.none())

get /fine_tuning/jobs

List your organization's fine-tuning jobs

Parameters

JobListParams params
- Optional<String> after
  
  Identifier for the last job from the previous pagination request.
- Optional<Long> limit
  
  Number of fine-tuning jobs to retrieve.
- Optional<Metadata> metadata
  
  Optional metadata filter. To filter, use the syntax metadata[k]=v. Alternatively, set metadata=null to indicate no metadata.

Returns

class FineTuningJob:

The fine_tuning.job object represents a fine-tuning job that has been created through the API.
- String id
  
  The object identifier, which can be referenced in the API endpoints.
- long createdAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was created.
- Optional<Error> error
  
  For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.
  - String code
    
    A machine-readable error code.
  - String message
    
    A human-readable error message.
  - Optional<String> param
    
    The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.
- Optional<String> fineTunedModel
  
  The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.
- Optional<Long> finishedAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.
- Hyperparameters hyperparameters
  
  The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.
  - Optional<BatchSize> batchSize
    
    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
    - JsonValue;
      - AUTO("auto")
    - long
  - Optional<LearningRateMultiplier> learningRateMultiplier
    
    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
    - JsonValue;
      - AUTO("auto")
    - double
  - Optional<NEpochs> nEpochs
    
    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
    - JsonValue;
      - AUTO("auto")
    - long
- String model
  
  The base model that is being fine-tuned.
- JsonValue; object_ "fine_tuning.job"constant
  
  The object type, which is always "fine_tuning.job".
  - FINE_TUNING_JOB("fine_tuning.job")
- String organizationId
  
  The organization that owns the fine-tuning job.
- List<String> resultFiles
  
  The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.
- long seed
  
  The seed used for the fine-tuning job.
- Status status
  
  The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.
  - VALIDATING_FILES("validating_files")
  - QUEUED("queued")
  - RUNNING("running")
  - SUCCEEDED("succeeded")
  - FAILED("failed")
  - CANCELLED("cancelled")
- Optional<Long> trainedTokens
  
  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.
- String trainingFile
  
  The file ID used for training. You can retrieve the training data with the Files API.
- Optional<String> validationFile
  
  The file ID used for validation. You can retrieve the validation results with the Files API.
- Optional<Long> estimatedFinish
  
  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.
- Optional<List<FineTuningJobWandbIntegrationObject>> integrations
  
  A list of integrations to enable for this fine-tuning job.
  - JsonValue; type "wandb"constant
    
    The type of the integration being enabled for the fine-tuning job
    - WANDB("wandb")
  - FineTuningJobWandbIntegration wandb
    
    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.
    - String project
      
      The name of the project that the new run will be created under.
    - Optional<String> entity
      
      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.
    - Optional<String> name
      
      A display name to set for the run. If not set, we will use the Job ID as the name.
    - Optional<List<String>> tags
      
      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}".
- Optional<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.
- Optional<Method> method
  
  The method used for fine-tuning.
  - Type type
    
    The type of method. Is either supervised, dpo, or reinforcement.
    - SUPERVISED("supervised")
    - DPO("dpo")
    - REINFORCEMENT("reinforcement")
  - Optional<DpoMethod> dpo
    
    Configuration for the DPO fine-tuning method.
    - Optional<DpoHyperparameters> hyperparameters
      
      The hyperparameters used for the DPO fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<Beta> beta
        
        The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
  - Optional<ReinforcementMethod> reinforcement
    
    Configuration for the reinforcement fine-tuning method.
    - Grader grader
      
      The grader used for the fine-tuning job.
      - class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        String input
        
        The input text. This may include template strings.
        
        String name
        
        The name of the grader.
        
        Operation operation
        
        The string check operation to perform. One of eq, ne, like, or ilike.
        
        EQ("eq")
        
        NE("ne")
        
        LIKE("like")
        
        ILIKE("ilike")
        
        String reference
        
        The reference text. This may include template strings.
        
        JsonValue; type "string_check"constant
        
        The object type, which is always string_check.
        
        STRING_CHECK("string_check")
      - class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        EvaluationMetric evaluationMetric
        
        The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.
        
        COSINE("cosine")
        
        FUZZY_MATCH("fuzzy_match")
        
        BLEU("bleu")
        
        GLEU("gleu")
        
        METEOR("meteor")
        
        ROUGE_1("rouge_1")
        
        ROUGE_2("rouge_2")
        
        ROUGE_3("rouge_3")
        
        ROUGE_4("rouge_4")
        
        ROUGE_5("rouge_5")
        
        ROUGE_L("rouge_l")
        
        String input
        
        The text being graded.
        
        String name
        
        The name of the grader.
        
        String reference
        
        The text being graded against.
        
        JsonValue; type "text_similarity"constant
        
        The type of grader.
        
        TEXT_SIMILARITY("text_similarity")
      - class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        String name
        
        The name of the grader.
        
        String source
        
        The source code of the python script.
        
        JsonValue; type "python"constant
        
        The object type, which is always python.
        
        PYTHON("python")
        
        Optional<String> imageTag
        
        The image tag to use for the python script.
      - class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        List<Input> input
        
        The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        String text
        
        The text input to the model.
        
        JsonValue; type "input_text"constant
        
        The type of the input item. Always input_text.
        
        INPUT_TEXT("input_text")
        
        Optional<PromptCacheBreakpoint> promptCacheBreakpoint
        
        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.
        
        JsonValue; mode "explicit"constant
        
        The breakpoint mode. Always explicit.
        
        EXPLICIT("explicit")
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        InputAudio inputAudio
        
        String data
        
        Base64-encoded audio data.
        
        Format format
        
        The format of the audio data. Currently supported formats are mp3 and wav.
        
        MP3("mp3")
        
        WAV("wav")
        
        JsonValue; type "input_audio"constant
        
        The type of the input item. Always input_audio.
        
        INPUT_AUDIO("input_audio")
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        InputImage
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        String model
        
        The model to use for the evaluation.
        
        String name
        
        The name of the grader.
        
        JsonValue; type "score_model"constant
        
        The object type, which is always score_model.
        
        SCORE_MODEL("score_model")
        
        Optional<List<Double>> range
        
        The range of the score. Defaults to [0, 1].
        
        Optional<SamplingParams> samplingParams
        
        The sampling parameters for the model.
        
        Optional<Long> maxCompletionsTokens
        
        The maximum number of tokens the grader model may generate in its response.
        
        Optional<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.
        
        NONE("none")
        
        MINIMAL("minimal")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
        
        XHIGH("xhigh")
        
        MAX("max")
        
        Optional<Long> seed
        
        A seed value to initialize the randomness, during sampling.
        
        Optional<Double> temperature
        
        A higher temperature increases randomness in the outputs.
        
        Optional<Double> topP
        
        An alternative to temperature for nucleus sampling; 1.0 includes all tokens.
      - class MultiGrader:
        
        A MultiGrader object combines the output of multiple graders to produce a single score.
        
        String calculateOutput
        
        A formula to calculate the output based on grader results.
        
        Graders graders
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        class LabelModelGrader:
        
        A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.
        
        List<Input> input
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        InputImage
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        List<String> labels
        
        The labels to assign to each item in the evaluation.
        
        String model
        
        The model to use for the evaluation. Must support structured outputs.
        
        String name
        
        The name of the grader.
        
        List<String> passingLabels
        
        The labels that indicate a passing result. Must be a subset of labels.
        
        JsonValue; type "label_model"constant
        
        The object type, which is always label_model.
        
        LABEL_MODEL("label_model")
        
        String name
        
        The name of the grader.
        
        JsonValue; type "multi"constant
        
        The object type, which is always multi.
        
        MULTI("multi")
    - Optional<ReinforcementHyperparameters> hyperparameters
      
      The hyperparameters used for the reinforcement fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ComputeMultiplier> computeMultiplier
        
        Multiplier on amount of compute used for exploring search space during training.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<EvalInterval> evalInterval
        
        The number of training steps between evaluation runs.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<EvalSamples> evalSamples
        
        Number of evaluation samples to generate per training step.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ReasoningEffort> reasoningEffort
        
        Level of reasoning effort.
        
        DEFAULT("default")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
  - Optional<SupervisedMethod> supervised
    
    Configuration for the supervised fine-tuning method.
    - Optional<SupervisedHyperparameters> hyperparameters
      
      The hyperparameters used for the fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long

Example

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.finetuning.jobs.JobListPage;
import com.openai.models.finetuning.jobs.JobListParams;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        JobListPage page = client.fineTuning().jobs().list();
    }
}

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

FineTuningJob fineTuning().jobs().retrieve(JobRetrieveParamsparams = JobRetrieveParams.none(), RequestOptionsrequestOptions = RequestOptions.none())

get /fine_tuning/jobs/{fine_tuning_job_id}

Get info about a fine-tuning job.

Learn more about fine-tuning

Parameters

JobRetrieveParams params
- Optional<String> fineTuningJobId

Returns

class FineTuningJob:

The fine_tuning.job object represents a fine-tuning job that has been created through the API.
- String id
  
  The object identifier, which can be referenced in the API endpoints.
- long createdAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was created.
- Optional<Error> error
  
  For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.
  - String code
    
    A machine-readable error code.
  - String message
    
    A human-readable error message.
  - Optional<String> param
    
    The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.
- Optional<String> fineTunedModel
  
  The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.
- Optional<Long> finishedAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.
- Hyperparameters hyperparameters
  
  The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.
  - Optional<BatchSize> batchSize
    
    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
    - JsonValue;
      - AUTO("auto")
    - long
  - Optional<LearningRateMultiplier> learningRateMultiplier
    
    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
    - JsonValue;
      - AUTO("auto")
    - double
  - Optional<NEpochs> nEpochs
    
    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
    - JsonValue;
      - AUTO("auto")
    - long
- String model
  
  The base model that is being fine-tuned.
- JsonValue; object_ "fine_tuning.job"constant
  
  The object type, which is always "fine_tuning.job".
  - FINE_TUNING_JOB("fine_tuning.job")
- String organizationId
  
  The organization that owns the fine-tuning job.
- List<String> resultFiles
  
  The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.
- long seed
  
  The seed used for the fine-tuning job.
- Status status
  
  The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.
  - VALIDATING_FILES("validating_files")
  - QUEUED("queued")
  - RUNNING("running")
  - SUCCEEDED("succeeded")
  - FAILED("failed")
  - CANCELLED("cancelled")
- Optional<Long> trainedTokens
  
  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.
- String trainingFile
  
  The file ID used for training. You can retrieve the training data with the Files API.
- Optional<String> validationFile
  
  The file ID used for validation. You can retrieve the validation results with the Files API.
- Optional<Long> estimatedFinish
  
  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.
- Optional<List<FineTuningJobWandbIntegrationObject>> integrations
  
  A list of integrations to enable for this fine-tuning job.
  - JsonValue; type "wandb"constant
    
    The type of the integration being enabled for the fine-tuning job
    - WANDB("wandb")
  - FineTuningJobWandbIntegration wandb
    
    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.
    - String project
      
      The name of the project that the new run will be created under.
    - Optional<String> entity
      
      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.
    - Optional<String> name
      
      A display name to set for the run. If not set, we will use the Job ID as the name.
    - Optional<List<String>> tags
      
      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}".
- Optional<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.
- Optional<Method> method
  
  The method used for fine-tuning.
  - Type type
    
    The type of method. Is either supervised, dpo, or reinforcement.
    - SUPERVISED("supervised")
    - DPO("dpo")
    - REINFORCEMENT("reinforcement")
  - Optional<DpoMethod> dpo
    
    Configuration for the DPO fine-tuning method.
    - Optional<DpoHyperparameters> hyperparameters
      
      The hyperparameters used for the DPO fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<Beta> beta
        
        The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
  - Optional<ReinforcementMethod> reinforcement
    
    Configuration for the reinforcement fine-tuning method.
    - Grader grader
      
      The grader used for the fine-tuning job.
      - class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        String input
        
        The input text. This may include template strings.
        
        String name
        
        The name of the grader.
        
        Operation operation
        
        The string check operation to perform. One of eq, ne, like, or ilike.
        
        EQ("eq")
        
        NE("ne")
        
        LIKE("like")
        
        ILIKE("ilike")
        
        String reference
        
        The reference text. This may include template strings.
        
        JsonValue; type "string_check"constant
        
        The object type, which is always string_check.
        
        STRING_CHECK("string_check")
      - class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        EvaluationMetric evaluationMetric
        
        The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.
        
        COSINE("cosine")
        
        FUZZY_MATCH("fuzzy_match")
        
        BLEU("bleu")
        
        GLEU("gleu")
        
        METEOR("meteor")
        
        ROUGE_1("rouge_1")
        
        ROUGE_2("rouge_2")
        
        ROUGE_3("rouge_3")
        
        ROUGE_4("rouge_4")
        
        ROUGE_5("rouge_5")
        
        ROUGE_L("rouge_l")
        
        String input
        
        The text being graded.
        
        String name
        
        The name of the grader.
        
        String reference
        
        The text being graded against.
        
        JsonValue; type "text_similarity"constant
        
        The type of grader.
        
        TEXT_SIMILARITY("text_similarity")
      - class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        String name
        
        The name of the grader.
        
        String source
        
        The source code of the python script.
        
        JsonValue; type "python"constant
        
        The object type, which is always python.
        
        PYTHON("python")
        
        Optional<String> imageTag
        
        The image tag to use for the python script.
      - class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        List<Input> input
        
        The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        String text
        
        The text input to the model.
        
        JsonValue; type "input_text"constant
        
        The type of the input item. Always input_text.
        
        INPUT_TEXT("input_text")
        
        Optional<PromptCacheBreakpoint> promptCacheBreakpoint
        
        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.
        
        JsonValue; mode "explicit"constant
        
        The breakpoint mode. Always explicit.
        
        EXPLICIT("explicit")
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        InputAudio inputAudio
        
        String data
        
        Base64-encoded audio data.
        
        Format format
        
        The format of the audio data. Currently supported formats are mp3 and wav.
        
        MP3("mp3")
        
        WAV("wav")
        
        JsonValue; type "input_audio"constant
        
        The type of the input item. Always input_audio.
        
        INPUT_AUDIO("input_audio")
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        InputImage
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        String model
        
        The model to use for the evaluation.
        
        String name
        
        The name of the grader.
        
        JsonValue; type "score_model"constant
        
        The object type, which is always score_model.
        
        SCORE_MODEL("score_model")
        
        Optional<List<Double>> range
        
        The range of the score. Defaults to [0, 1].
        
        Optional<SamplingParams> samplingParams
        
        The sampling parameters for the model.
        
        Optional<Long> maxCompletionsTokens
        
        The maximum number of tokens the grader model may generate in its response.
        
        Optional<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.
        
        NONE("none")
        
        MINIMAL("minimal")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
        
        XHIGH("xhigh")
        
        MAX("max")
        
        Optional<Long> seed
        
        A seed value to initialize the randomness, during sampling.
        
        Optional<Double> temperature
        
        A higher temperature increases randomness in the outputs.
        
        Optional<Double> topP
        
        An alternative to temperature for nucleus sampling; 1.0 includes all tokens.
      - class MultiGrader:
        
        A MultiGrader object combines the output of multiple graders to produce a single score.
        
        String calculateOutput
        
        A formula to calculate the output based on grader results.
        
        Graders graders
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        class LabelModelGrader:
        
        A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.
        
        List<Input> input
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        InputImage
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        List<String> labels
        
        The labels to assign to each item in the evaluation.
        
        String model
        
        The model to use for the evaluation. Must support structured outputs.
        
        String name
        
        The name of the grader.
        
        List<String> passingLabels
        
        The labels that indicate a passing result. Must be a subset of labels.
        
        JsonValue; type "label_model"constant
        
        The object type, which is always label_model.
        
        LABEL_MODEL("label_model")
        
        String name
        
        The name of the grader.
        
        JsonValue; type "multi"constant
        
        The object type, which is always multi.
        
        MULTI("multi")
    - Optional<ReinforcementHyperparameters> hyperparameters
      
      The hyperparameters used for the reinforcement fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ComputeMultiplier> computeMultiplier
        
        Multiplier on amount of compute used for exploring search space during training.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<EvalInterval> evalInterval
        
        The number of training steps between evaluation runs.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<EvalSamples> evalSamples
        
        Number of evaluation samples to generate per training step.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ReasoningEffort> reasoningEffort
        
        Level of reasoning effort.
        
        DEFAULT("default")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
  - Optional<SupervisedMethod> supervised
    
    Configuration for the supervised fine-tuning method.
    - Optional<SupervisedHyperparameters> hyperparameters
      
      The hyperparameters used for the fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long

Example

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.finetuning.jobs.FineTuningJob;
import com.openai.models.finetuning.jobs.JobRetrieveParams;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        FineTuningJob fineTuningJob = client.fineTuning().jobs().retrieve("ft-AF1WoRqd3aJAHsqc9NY7iL8F");
    }
}

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

JobListEventsPage fineTuning().jobs().listEvents(JobListEventsParamsparams = JobListEventsParams.none(), RequestOptionsrequestOptions = RequestOptions.none())

get /fine_tuning/jobs/{fine_tuning_job_id}/events

Get status updates for a fine-tuning job.

Parameters

JobListEventsParams params
- Optional<String> fineTuningJobId
- Optional<String> after
  
  Identifier for the last event from the previous pagination request.
- Optional<Long> limit
  
  Number of events to retrieve.

Returns

class FineTuningJobEvent:

Fine-tuning job event object
- String id
  
  The object identifier.
- long createdAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was created.
- Level level
  
  The log level of the event.
  - INFO("info")
  - WARN("warn")
  - ERROR("error")
- String message
  
  The message of the event.
- JsonValue; object_ "fine_tuning.job.event"constant
  
  The object type, which is always "fine_tuning.job.event".
  - FINE_TUNING_JOB_EVENT("fine_tuning.job.event")
- Optional<JsonValue> data
  
  The data associated with the event.
- Optional<Type> type
  
  The type of event.
  - MESSAGE("message")
  - METRICS("metrics")

Example

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.finetuning.jobs.JobListEventsPage;
import com.openai.models.finetuning.jobs.JobListEventsParams;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        JobListEventsPage page = client.fineTuning().jobs().listEvents("ft-AF1WoRqd3aJAHsqc9NY7iL8F");
    }
}

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

FineTuningJob fineTuning().jobs().cancel(JobCancelParamsparams = JobCancelParams.none(), RequestOptionsrequestOptions = RequestOptions.none())

post /fine_tuning/jobs/{fine_tuning_job_id}/cancel

Immediately cancel a fine-tune job.

Parameters

JobCancelParams params
- Optional<String> fineTuningJobId

Returns

class FineTuningJob:

The fine_tuning.job object represents a fine-tuning job that has been created through the API.
- String id
  
  The object identifier, which can be referenced in the API endpoints.
- long createdAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was created.
- Optional<Error> error
  
  For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.
  - String code
    
    A machine-readable error code.
  - String message
    
    A human-readable error message.
  - Optional<String> param
    
    The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.
- Optional<String> fineTunedModel
  
  The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.
- Optional<Long> finishedAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.
- Hyperparameters hyperparameters
  
  The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.
  - Optional<BatchSize> batchSize
    
    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
    - JsonValue;
      - AUTO("auto")
    - long
  - Optional<LearningRateMultiplier> learningRateMultiplier
    
    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
    - JsonValue;
      - AUTO("auto")
    - double
  - Optional<NEpochs> nEpochs
    
    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
    - JsonValue;
      - AUTO("auto")
    - long
- String model
  
  The base model that is being fine-tuned.
- JsonValue; object_ "fine_tuning.job"constant
  
  The object type, which is always "fine_tuning.job".
  - FINE_TUNING_JOB("fine_tuning.job")
- String organizationId
  
  The organization that owns the fine-tuning job.
- List<String> resultFiles
  
  The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.
- long seed
  
  The seed used for the fine-tuning job.
- Status status
  
  The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.
  - VALIDATING_FILES("validating_files")
  - QUEUED("queued")
  - RUNNING("running")
  - SUCCEEDED("succeeded")
  - FAILED("failed")
  - CANCELLED("cancelled")
- Optional<Long> trainedTokens
  
  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.
- String trainingFile
  
  The file ID used for training. You can retrieve the training data with the Files API.
- Optional<String> validationFile
  
  The file ID used for validation. You can retrieve the validation results with the Files API.
- Optional<Long> estimatedFinish
  
  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.
- Optional<List<FineTuningJobWandbIntegrationObject>> integrations
  
  A list of integrations to enable for this fine-tuning job.
  - JsonValue; type "wandb"constant
    
    The type of the integration being enabled for the fine-tuning job
    - WANDB("wandb")
  - FineTuningJobWandbIntegration wandb
    
    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.
    - String project
      
      The name of the project that the new run will be created under.
    - Optional<String> entity
      
      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.
    - Optional<String> name
      
      A display name to set for the run. If not set, we will use the Job ID as the name.
    - Optional<List<String>> tags
      
      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}".
- Optional<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.
- Optional<Method> method
  
  The method used for fine-tuning.
  - Type type
    
    The type of method. Is either supervised, dpo, or reinforcement.
    - SUPERVISED("supervised")
    - DPO("dpo")
    - REINFORCEMENT("reinforcement")
  - Optional<DpoMethod> dpo
    
    Configuration for the DPO fine-tuning method.
    - Optional<DpoHyperparameters> hyperparameters
      
      The hyperparameters used for the DPO fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<Beta> beta
        
        The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
  - Optional<ReinforcementMethod> reinforcement
    
    Configuration for the reinforcement fine-tuning method.
    - Grader grader
      
      The grader used for the fine-tuning job.
      - class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        String input
        
        The input text. This may include template strings.
        
        String name
        
        The name of the grader.
        
        Operation operation
        
        The string check operation to perform. One of eq, ne, like, or ilike.
        
        EQ("eq")
        
        NE("ne")
        
        LIKE("like")
        
        ILIKE("ilike")
        
        String reference
        
        The reference text. This may include template strings.
        
        JsonValue; type "string_check"constant
        
        The object type, which is always string_check.
        
        STRING_CHECK("string_check")
      - class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        EvaluationMetric evaluationMetric
        
        The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.
        
        COSINE("cosine")
        
        FUZZY_MATCH("fuzzy_match")
        
        BLEU("bleu")
        
        GLEU("gleu")
        
        METEOR("meteor")
        
        ROUGE_1("rouge_1")
        
        ROUGE_2("rouge_2")
        
        ROUGE_3("rouge_3")
        
        ROUGE_4("rouge_4")
        
        ROUGE_5("rouge_5")
        
        ROUGE_L("rouge_l")
        
        String input
        
        The text being graded.
        
        String name
        
        The name of the grader.
        
        String reference
        
        The text being graded against.
        
        JsonValue; type "text_similarity"constant
        
        The type of grader.
        
        TEXT_SIMILARITY("text_similarity")
      - class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        String name
        
        The name of the grader.
        
        String source
        
        The source code of the python script.
        
        JsonValue; type "python"constant
        
        The object type, which is always python.
        
        PYTHON("python")
        
        Optional<String> imageTag
        
        The image tag to use for the python script.
      - class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        List<Input> input
        
        The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        String text
        
        The text input to the model.
        
        JsonValue; type "input_text"constant
        
        The type of the input item. Always input_text.
        
        INPUT_TEXT("input_text")
        
        Optional<PromptCacheBreakpoint> promptCacheBreakpoint
        
        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.
        
        JsonValue; mode "explicit"constant
        
        The breakpoint mode. Always explicit.
        
        EXPLICIT("explicit")
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        InputAudio inputAudio
        
        String data
        
        Base64-encoded audio data.
        
        Format format
        
        The format of the audio data. Currently supported formats are mp3 and wav.
        
        MP3("mp3")
        
        WAV("wav")
        
        JsonValue; type "input_audio"constant
        
        The type of the input item. Always input_audio.
        
        INPUT_AUDIO("input_audio")
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        InputImage
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        String model
        
        The model to use for the evaluation.
        
        String name
        
        The name of the grader.
        
        JsonValue; type "score_model"constant
        
        The object type, which is always score_model.
        
        SCORE_MODEL("score_model")
        
        Optional<List<Double>> range
        
        The range of the score. Defaults to [0, 1].
        
        Optional<SamplingParams> samplingParams
        
        The sampling parameters for the model.
        
        Optional<Long> maxCompletionsTokens
        
        The maximum number of tokens the grader model may generate in its response.
        
        Optional<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.
        
        NONE("none")
        
        MINIMAL("minimal")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
        
        XHIGH("xhigh")
        
        MAX("max")
        
        Optional<Long> seed
        
        A seed value to initialize the randomness, during sampling.
        
        Optional<Double> temperature
        
        A higher temperature increases randomness in the outputs.
        
        Optional<Double> topP
        
        An alternative to temperature for nucleus sampling; 1.0 includes all tokens.
      - class MultiGrader:
        
        A MultiGrader object combines the output of multiple graders to produce a single score.
        
        String calculateOutput
        
        A formula to calculate the output based on grader results.
        
        Graders graders
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        class LabelModelGrader:
        
        A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.
        
        List<Input> input
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        InputImage
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        List<String> labels
        
        The labels to assign to each item in the evaluation.
        
        String model
        
        The model to use for the evaluation. Must support structured outputs.
        
        String name
        
        The name of the grader.
        
        List<String> passingLabels
        
        The labels that indicate a passing result. Must be a subset of labels.
        
        JsonValue; type "label_model"constant
        
        The object type, which is always label_model.
        
        LABEL_MODEL("label_model")
        
        String name
        
        The name of the grader.
        
        JsonValue; type "multi"constant
        
        The object type, which is always multi.
        
        MULTI("multi")
    - Optional<ReinforcementHyperparameters> hyperparameters
      
      The hyperparameters used for the reinforcement fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ComputeMultiplier> computeMultiplier
        
        Multiplier on amount of compute used for exploring search space during training.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<EvalInterval> evalInterval
        
        The number of training steps between evaluation runs.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<EvalSamples> evalSamples
        
        Number of evaluation samples to generate per training step.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ReasoningEffort> reasoningEffort
        
        Level of reasoning effort.
        
        DEFAULT("default")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
  - Optional<SupervisedMethod> supervised
    
    Configuration for the supervised fine-tuning method.
    - Optional<SupervisedHyperparameters> hyperparameters
      
      The hyperparameters used for the fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long

Example

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.finetuning.jobs.FineTuningJob;
import com.openai.models.finetuning.jobs.JobCancelParams;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        FineTuningJob fineTuningJob = client.fineTuning().jobs().cancel("ft-AF1WoRqd3aJAHsqc9NY7iL8F");
    }
}

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

FineTuningJob fineTuning().jobs().pause(JobPauseParamsparams = JobPauseParams.none(), RequestOptionsrequestOptions = RequestOptions.none())

post /fine_tuning/jobs/{fine_tuning_job_id}/pause

Pause a fine-tune job.

Parameters

JobPauseParams params
- Optional<String> fineTuningJobId

Returns

class FineTuningJob:

The fine_tuning.job object represents a fine-tuning job that has been created through the API.
- String id
  
  The object identifier, which can be referenced in the API endpoints.
- long createdAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was created.
- Optional<Error> error
  
  For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.
  - String code
    
    A machine-readable error code.
  - String message
    
    A human-readable error message.
  - Optional<String> param
    
    The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.
- Optional<String> fineTunedModel
  
  The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.
- Optional<Long> finishedAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.
- Hyperparameters hyperparameters
  
  The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.
  - Optional<BatchSize> batchSize
    
    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
    - JsonValue;
      - AUTO("auto")
    - long
  - Optional<LearningRateMultiplier> learningRateMultiplier
    
    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
    - JsonValue;
      - AUTO("auto")
    - double
  - Optional<NEpochs> nEpochs
    
    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
    - JsonValue;
      - AUTO("auto")
    - long
- String model
  
  The base model that is being fine-tuned.
- JsonValue; object_ "fine_tuning.job"constant
  
  The object type, which is always "fine_tuning.job".
  - FINE_TUNING_JOB("fine_tuning.job")
- String organizationId
  
  The organization that owns the fine-tuning job.
- List<String> resultFiles
  
  The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.
- long seed
  
  The seed used for the fine-tuning job.
- Status status
  
  The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.
  - VALIDATING_FILES("validating_files")
  - QUEUED("queued")
  - RUNNING("running")
  - SUCCEEDED("succeeded")
  - FAILED("failed")
  - CANCELLED("cancelled")
- Optional<Long> trainedTokens
  
  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.
- String trainingFile
  
  The file ID used for training. You can retrieve the training data with the Files API.
- Optional<String> validationFile
  
  The file ID used for validation. You can retrieve the validation results with the Files API.
- Optional<Long> estimatedFinish
  
  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.
- Optional<List<FineTuningJobWandbIntegrationObject>> integrations
  
  A list of integrations to enable for this fine-tuning job.
  - JsonValue; type "wandb"constant
    
    The type of the integration being enabled for the fine-tuning job
    - WANDB("wandb")
  - FineTuningJobWandbIntegration wandb
    
    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.
    - String project
      
      The name of the project that the new run will be created under.
    - Optional<String> entity
      
      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.
    - Optional<String> name
      
      A display name to set for the run. If not set, we will use the Job ID as the name.
    - Optional<List<String>> tags
      
      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}".
- Optional<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.
- Optional<Method> method
  
  The method used for fine-tuning.
  - Type type
    
    The type of method. Is either supervised, dpo, or reinforcement.
    - SUPERVISED("supervised")
    - DPO("dpo")
    - REINFORCEMENT("reinforcement")
  - Optional<DpoMethod> dpo
    
    Configuration for the DPO fine-tuning method.
    - Optional<DpoHyperparameters> hyperparameters
      
      The hyperparameters used for the DPO fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<Beta> beta
        
        The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
  - Optional<ReinforcementMethod> reinforcement
    
    Configuration for the reinforcement fine-tuning method.
    - Grader grader
      
      The grader used for the fine-tuning job.
      - class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        String input
        
        The input text. This may include template strings.
        
        String name
        
        The name of the grader.
        
        Operation operation
        
        The string check operation to perform. One of eq, ne, like, or ilike.
        
        EQ("eq")
        
        NE("ne")
        
        LIKE("like")
        
        ILIKE("ilike")
        
        String reference
        
        The reference text. This may include template strings.
        
        JsonValue; type "string_check"constant
        
        The object type, which is always string_check.
        
        STRING_CHECK("string_check")
      - class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        EvaluationMetric evaluationMetric
        
        The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.
        
        COSINE("cosine")
        
        FUZZY_MATCH("fuzzy_match")
        
        BLEU("bleu")
        
        GLEU("gleu")
        
        METEOR("meteor")
        
        ROUGE_1("rouge_1")
        
        ROUGE_2("rouge_2")
        
        ROUGE_3("rouge_3")
        
        ROUGE_4("rouge_4")
        
        ROUGE_5("rouge_5")
        
        ROUGE_L("rouge_l")
        
        String input
        
        The text being graded.
        
        String name
        
        The name of the grader.
        
        String reference
        
        The text being graded against.
        
        JsonValue; type "text_similarity"constant
        
        The type of grader.
        
        TEXT_SIMILARITY("text_similarity")
      - class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        String name
        
        The name of the grader.
        
        String source
        
        The source code of the python script.
        
        JsonValue; type "python"constant
        
        The object type, which is always python.
        
        PYTHON("python")
        
        Optional<String> imageTag
        
        The image tag to use for the python script.
      - class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        List<Input> input
        
        The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        String text
        
        The text input to the model.
        
        JsonValue; type "input_text"constant
        
        The type of the input item. Always input_text.
        
        INPUT_TEXT("input_text")
        
        Optional<PromptCacheBreakpoint> promptCacheBreakpoint
        
        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.
        
        JsonValue; mode "explicit"constant
        
        The breakpoint mode. Always explicit.
        
        EXPLICIT("explicit")
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        InputAudio inputAudio
        
        String data
        
        Base64-encoded audio data.
        
        Format format
        
        The format of the audio data. Currently supported formats are mp3 and wav.
        
        MP3("mp3")
        
        WAV("wav")
        
        JsonValue; type "input_audio"constant
        
        The type of the input item. Always input_audio.
        
        INPUT_AUDIO("input_audio")
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        InputImage
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        String model
        
        The model to use for the evaluation.
        
        String name
        
        The name of the grader.
        
        JsonValue; type "score_model"constant
        
        The object type, which is always score_model.
        
        SCORE_MODEL("score_model")
        
        Optional<List<Double>> range
        
        The range of the score. Defaults to [0, 1].
        
        Optional<SamplingParams> samplingParams
        
        The sampling parameters for the model.
        
        Optional<Long> maxCompletionsTokens
        
        The maximum number of tokens the grader model may generate in its response.
        
        Optional<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.
        
        NONE("none")
        
        MINIMAL("minimal")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
        
        XHIGH("xhigh")
        
        MAX("max")
        
        Optional<Long> seed
        
        A seed value to initialize the randomness, during sampling.
        
        Optional<Double> temperature
        
        A higher temperature increases randomness in the outputs.
        
        Optional<Double> topP
        
        An alternative to temperature for nucleus sampling; 1.0 includes all tokens.
      - class MultiGrader:
        
        A MultiGrader object combines the output of multiple graders to produce a single score.
        
        String calculateOutput
        
        A formula to calculate the output based on grader results.
        
        Graders graders
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        class LabelModelGrader:
        
        A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.
        
        List<Input> input
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        InputImage
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        List<String> labels
        
        The labels to assign to each item in the evaluation.
        
        String model
        
        The model to use for the evaluation. Must support structured outputs.
        
        String name
        
        The name of the grader.
        
        List<String> passingLabels
        
        The labels that indicate a passing result. Must be a subset of labels.
        
        JsonValue; type "label_model"constant
        
        The object type, which is always label_model.
        
        LABEL_MODEL("label_model")
        
        String name
        
        The name of the grader.
        
        JsonValue; type "multi"constant
        
        The object type, which is always multi.
        
        MULTI("multi")
    - Optional<ReinforcementHyperparameters> hyperparameters
      
      The hyperparameters used for the reinforcement fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ComputeMultiplier> computeMultiplier
        
        Multiplier on amount of compute used for exploring search space during training.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<EvalInterval> evalInterval
        
        The number of training steps between evaluation runs.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<EvalSamples> evalSamples
        
        Number of evaluation samples to generate per training step.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ReasoningEffort> reasoningEffort
        
        Level of reasoning effort.
        
        DEFAULT("default")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
  - Optional<SupervisedMethod> supervised
    
    Configuration for the supervised fine-tuning method.
    - Optional<SupervisedHyperparameters> hyperparameters
      
      The hyperparameters used for the fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long

Example

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.finetuning.jobs.FineTuningJob;
import com.openai.models.finetuning.jobs.JobPauseParams;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        FineTuningJob fineTuningJob = client.fineTuning().jobs().pause("ft-AF1WoRqd3aJAHsqc9NY7iL8F");
    }
}

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"
      }
    }
  }
}

Resume fine-tuning

FineTuningJob fineTuning().jobs().resume(JobResumeParamsparams = JobResumeParams.none(), RequestOptionsrequestOptions = RequestOptions.none())

post /fine_tuning/jobs/{fine_tuning_job_id}/resume

Resume a fine-tune job.

Parameters

JobResumeParams params
- Optional<String> fineTuningJobId

Returns

class FineTuningJob:

The fine_tuning.job object represents a fine-tuning job that has been created through the API.
- String id
  
  The object identifier, which can be referenced in the API endpoints.
- long createdAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was created.
- Optional<Error> error
  
  For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.
  - String code
    
    A machine-readable error code.
  - String message
    
    A human-readable error message.
  - Optional<String> param
    
    The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.
- Optional<String> fineTunedModel
  
  The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.
- Optional<Long> finishedAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.
- Hyperparameters hyperparameters
  
  The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.
  - Optional<BatchSize> batchSize
    
    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
    - JsonValue;
      - AUTO("auto")
    - long
  - Optional<LearningRateMultiplier> learningRateMultiplier
    
    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
    - JsonValue;
      - AUTO("auto")
    - double
  - Optional<NEpochs> nEpochs
    
    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
    - JsonValue;
      - AUTO("auto")
    - long
- String model
  
  The base model that is being fine-tuned.
- JsonValue; object_ "fine_tuning.job"constant
  
  The object type, which is always "fine_tuning.job".
  - FINE_TUNING_JOB("fine_tuning.job")
- String organizationId
  
  The organization that owns the fine-tuning job.
- List<String> resultFiles
  
  The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.
- long seed
  
  The seed used for the fine-tuning job.
- Status status
  
  The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.
  - VALIDATING_FILES("validating_files")
  - QUEUED("queued")
  - RUNNING("running")
  - SUCCEEDED("succeeded")
  - FAILED("failed")
  - CANCELLED("cancelled")
- Optional<Long> trainedTokens
  
  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.
- String trainingFile
  
  The file ID used for training. You can retrieve the training data with the Files API.
- Optional<String> validationFile
  
  The file ID used for validation. You can retrieve the validation results with the Files API.
- Optional<Long> estimatedFinish
  
  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.
- Optional<List<FineTuningJobWandbIntegrationObject>> integrations
  
  A list of integrations to enable for this fine-tuning job.
  - JsonValue; type "wandb"constant
    
    The type of the integration being enabled for the fine-tuning job
    - WANDB("wandb")
  - FineTuningJobWandbIntegration wandb
    
    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.
    - String project
      
      The name of the project that the new run will be created under.
    - Optional<String> entity
      
      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.
    - Optional<String> name
      
      A display name to set for the run. If not set, we will use the Job ID as the name.
    - Optional<List<String>> tags
      
      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}".
- Optional<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.
- Optional<Method> method
  
  The method used for fine-tuning.
  - Type type
    
    The type of method. Is either supervised, dpo, or reinforcement.
    - SUPERVISED("supervised")
    - DPO("dpo")
    - REINFORCEMENT("reinforcement")
  - Optional<DpoMethod> dpo
    
    Configuration for the DPO fine-tuning method.
    - Optional<DpoHyperparameters> hyperparameters
      
      The hyperparameters used for the DPO fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<Beta> beta
        
        The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
  - Optional<ReinforcementMethod> reinforcement
    
    Configuration for the reinforcement fine-tuning method.
    - Grader grader
      
      The grader used for the fine-tuning job.
      - class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        String input
        
        The input text. This may include template strings.
        
        String name
        
        The name of the grader.
        
        Operation operation
        
        The string check operation to perform. One of eq, ne, like, or ilike.
        
        EQ("eq")
        
        NE("ne")
        
        LIKE("like")
        
        ILIKE("ilike")
        
        String reference
        
        The reference text. This may include template strings.
        
        JsonValue; type "string_check"constant
        
        The object type, which is always string_check.
        
        STRING_CHECK("string_check")
      - class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        EvaluationMetric evaluationMetric
        
        The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.
        
        COSINE("cosine")
        
        FUZZY_MATCH("fuzzy_match")
        
        BLEU("bleu")
        
        GLEU("gleu")
        
        METEOR("meteor")
        
        ROUGE_1("rouge_1")
        
        ROUGE_2("rouge_2")
        
        ROUGE_3("rouge_3")
        
        ROUGE_4("rouge_4")
        
        ROUGE_5("rouge_5")
        
        ROUGE_L("rouge_l")
        
        String input
        
        The text being graded.
        
        String name
        
        The name of the grader.
        
        String reference
        
        The text being graded against.
        
        JsonValue; type "text_similarity"constant
        
        The type of grader.
        
        TEXT_SIMILARITY("text_similarity")
      - class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        String name
        
        The name of the grader.
        
        String source
        
        The source code of the python script.
        
        JsonValue; type "python"constant
        
        The object type, which is always python.
        
        PYTHON("python")
        
        Optional<String> imageTag
        
        The image tag to use for the python script.
      - class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        List<Input> input
        
        The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        String text
        
        The text input to the model.
        
        JsonValue; type "input_text"constant
        
        The type of the input item. Always input_text.
        
        INPUT_TEXT("input_text")
        
        Optional<PromptCacheBreakpoint> promptCacheBreakpoint
        
        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.
        
        JsonValue; mode "explicit"constant
        
        The breakpoint mode. Always explicit.
        
        EXPLICIT("explicit")
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        InputAudio inputAudio
        
        String data
        
        Base64-encoded audio data.
        
        Format format
        
        The format of the audio data. Currently supported formats are mp3 and wav.
        
        MP3("mp3")
        
        WAV("wav")
        
        JsonValue; type "input_audio"constant
        
        The type of the input item. Always input_audio.
        
        INPUT_AUDIO("input_audio")
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        InputImage
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        String model
        
        The model to use for the evaluation.
        
        String name
        
        The name of the grader.
        
        JsonValue; type "score_model"constant
        
        The object type, which is always score_model.
        
        SCORE_MODEL("score_model")
        
        Optional<List<Double>> range
        
        The range of the score. Defaults to [0, 1].
        
        Optional<SamplingParams> samplingParams
        
        The sampling parameters for the model.
        
        Optional<Long> maxCompletionsTokens
        
        The maximum number of tokens the grader model may generate in its response.
        
        Optional<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.
        
        NONE("none")
        
        MINIMAL("minimal")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
        
        XHIGH("xhigh")
        
        MAX("max")
        
        Optional<Long> seed
        
        A seed value to initialize the randomness, during sampling.
        
        Optional<Double> temperature
        
        A higher temperature increases randomness in the outputs.
        
        Optional<Double> topP
        
        An alternative to temperature for nucleus sampling; 1.0 includes all tokens.
      - class MultiGrader:
        
        A MultiGrader object combines the output of multiple graders to produce a single score.
        
        String calculateOutput
        
        A formula to calculate the output based on grader results.
        
        Graders graders
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        class LabelModelGrader:
        
        A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.
        
        List<Input> input
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        InputImage
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        List<String> labels
        
        The labels to assign to each item in the evaluation.
        
        String model
        
        The model to use for the evaluation. Must support structured outputs.
        
        String name
        
        The name of the grader.
        
        List<String> passingLabels
        
        The labels that indicate a passing result. Must be a subset of labels.
        
        JsonValue; type "label_model"constant
        
        The object type, which is always label_model.
        
        LABEL_MODEL("label_model")
        
        String name
        
        The name of the grader.
        
        JsonValue; type "multi"constant
        
        The object type, which is always multi.
        
        MULTI("multi")
    - Optional<ReinforcementHyperparameters> hyperparameters
      
      The hyperparameters used for the reinforcement fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ComputeMultiplier> computeMultiplier
        
        Multiplier on amount of compute used for exploring search space during training.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<EvalInterval> evalInterval
        
        The number of training steps between evaluation runs.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<EvalSamples> evalSamples
        
        Number of evaluation samples to generate per training step.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ReasoningEffort> reasoningEffort
        
        Level of reasoning effort.
        
        DEFAULT("default")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
  - Optional<SupervisedMethod> supervised
    
    Configuration for the supervised fine-tuning method.
    - Optional<SupervisedHyperparameters> hyperparameters
      
      The hyperparameters used for the fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long

Example

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.finetuning.jobs.FineTuningJob;
import com.openai.models.finetuning.jobs.JobResumeParams;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        FineTuningJob fineTuningJob = client.fineTuning().jobs().resume("ft-AF1WoRqd3aJAHsqc9NY7iL8F");
    }
}

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"
      }
    }
  }
}

Domain Types

Fine Tuning Job

class FineTuningJob:

The fine_tuning.job object represents a fine-tuning job that has been created through the API.
- String id
  
  The object identifier, which can be referenced in the API endpoints.
- long createdAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was created.
- Optional<Error> error
  
  For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.
  - String code
    
    A machine-readable error code.
  - String message
    
    A human-readable error message.
  - Optional<String> param
    
    The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.
- Optional<String> fineTunedModel
  
  The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.
- Optional<Long> finishedAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was finished. The value will be null if the fine-tuning job is still running.
- Hyperparameters hyperparameters
  
  The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.
  - Optional<BatchSize> batchSize
    
    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
    - JsonValue;
      - AUTO("auto")
    - long
  - Optional<LearningRateMultiplier> learningRateMultiplier
    
    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
    - JsonValue;
      - AUTO("auto")
    - double
  - Optional<NEpochs> nEpochs
    
    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
    - JsonValue;
      - AUTO("auto")
    - long
- String model
  
  The base model that is being fine-tuned.
- JsonValue; object_ "fine_tuning.job"constant
  
  The object type, which is always "fine_tuning.job".
  - FINE_TUNING_JOB("fine_tuning.job")
- String organizationId
  
  The organization that owns the fine-tuning job.
- List<String> resultFiles
  
  The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.
- long seed
  
  The seed used for the fine-tuning job.
- Status status
  
  The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.
  - VALIDATING_FILES("validating_files")
  - QUEUED("queued")
  - RUNNING("running")
  - SUCCEEDED("succeeded")
  - FAILED("failed")
  - CANCELLED("cancelled")
- Optional<Long> trainedTokens
  
  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.
- String trainingFile
  
  The file ID used for training. You can retrieve the training data with the Files API.
- Optional<String> validationFile
  
  The file ID used for validation. You can retrieve the validation results with the Files API.
- Optional<Long> estimatedFinish
  
  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.
- Optional<List<FineTuningJobWandbIntegrationObject>> integrations
  
  A list of integrations to enable for this fine-tuning job.
  - JsonValue; type "wandb"constant
    
    The type of the integration being enabled for the fine-tuning job
    - WANDB("wandb")
  - FineTuningJobWandbIntegration wandb
    
    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.
    - String project
      
      The name of the project that the new run will be created under.
    - Optional<String> entity
      
      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.
    - Optional<String> name
      
      A display name to set for the run. If not set, we will use the Job ID as the name.
    - Optional<List<String>> tags
      
      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}".
- Optional<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.
- Optional<Method> method
  
  The method used for fine-tuning.
  - Type type
    
    The type of method. Is either supervised, dpo, or reinforcement.
    - SUPERVISED("supervised")
    - DPO("dpo")
    - REINFORCEMENT("reinforcement")
  - Optional<DpoMethod> dpo
    
    Configuration for the DPO fine-tuning method.
    - Optional<DpoHyperparameters> hyperparameters
      
      The hyperparameters used for the DPO fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<Beta> beta
        
        The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
  - Optional<ReinforcementMethod> reinforcement
    
    Configuration for the reinforcement fine-tuning method.
    - Grader grader
      
      The grader used for the fine-tuning job.
      - class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        String input
        
        The input text. This may include template strings.
        
        String name
        
        The name of the grader.
        
        Operation operation
        
        The string check operation to perform. One of eq, ne, like, or ilike.
        
        EQ("eq")
        
        NE("ne")
        
        LIKE("like")
        
        ILIKE("ilike")
        
        String reference
        
        The reference text. This may include template strings.
        
        JsonValue; type "string_check"constant
        
        The object type, which is always string_check.
        
        STRING_CHECK("string_check")
      - class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        EvaluationMetric evaluationMetric
        
        The evaluation metric to use. One of cosine, fuzzy_match, bleu, gleu, meteor, rouge_1, rouge_2, rouge_3, rouge_4, rouge_5, or rouge_l.
        
        COSINE("cosine")
        
        FUZZY_MATCH("fuzzy_match")
        
        BLEU("bleu")
        
        GLEU("gleu")
        
        METEOR("meteor")
        
        ROUGE_1("rouge_1")
        
        ROUGE_2("rouge_2")
        
        ROUGE_3("rouge_3")
        
        ROUGE_4("rouge_4")
        
        ROUGE_5("rouge_5")
        
        ROUGE_L("rouge_l")
        
        String input
        
        The text being graded.
        
        String name
        
        The name of the grader.
        
        String reference
        
        The text being graded against.
        
        JsonValue; type "text_similarity"constant
        
        The type of grader.
        
        TEXT_SIMILARITY("text_similarity")
      - class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        String name
        
        The name of the grader.
        
        String source
        
        The source code of the python script.
        
        JsonValue; type "python"constant
        
        The object type, which is always python.
        
        PYTHON("python")
        
        Optional<String> imageTag
        
        The image tag to use for the python script.
      - class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        List<Input> input
        
        The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings.
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        String text
        
        The text input to the model.
        
        JsonValue; type "input_text"constant
        
        The type of the input item. Always input_text.
        
        INPUT_TEXT("input_text")
        
        Optional<PromptCacheBreakpoint> promptCacheBreakpoint
        
        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.
        
        JsonValue; mode "explicit"constant
        
        The breakpoint mode. Always explicit.
        
        EXPLICIT("explicit")
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        InputAudio inputAudio
        
        String data
        
        Base64-encoded audio data.
        
        Format format
        
        The format of the audio data. Currently supported formats are mp3 and wav.
        
        MP3("mp3")
        
        WAV("wav")
        
        JsonValue; type "input_audio"constant
        
        The type of the input item. Always input_audio.
        
        INPUT_AUDIO("input_audio")
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        InputImage
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        String model
        
        The model to use for the evaluation.
        
        String name
        
        The name of the grader.
        
        JsonValue; type "score_model"constant
        
        The object type, which is always score_model.
        
        SCORE_MODEL("score_model")
        
        Optional<List<Double>> range
        
        The range of the score. Defaults to [0, 1].
        
        Optional<SamplingParams> samplingParams
        
        The sampling parameters for the model.
        
        Optional<Long> maxCompletionsTokens
        
        The maximum number of tokens the grader model may generate in its response.
        
        Optional<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.
        
        NONE("none")
        
        MINIMAL("minimal")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
        
        XHIGH("xhigh")
        
        MAX("max")
        
        Optional<Long> seed
        
        A seed value to initialize the randomness, during sampling.
        
        Optional<Double> temperature
        
        A higher temperature increases randomness in the outputs.
        
        Optional<Double> topP
        
        An alternative to temperature for nucleus sampling; 1.0 includes all tokens.
      - class MultiGrader:
        
        A MultiGrader object combines the output of multiple graders to produce a single score.
        
        String calculateOutput
        
        A formula to calculate the output based on grader results.
        
        Graders graders
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class StringCheckGrader:
        
        A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.
        
        class TextSimilarityGrader:
        
        A TextSimilarityGrader object which grades text based on similarity metrics.
        
        class PythonGrader:
        
        A PythonGrader object that runs a python script on the input.
        
        class ScoreModelGrader:
        
        A ScoreModelGrader object that uses a model to assign a score to the input.
        
        class LabelModelGrader:
        
        A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.
        
        List<Input> input
        
        Content content
        
        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
        
        class ResponseInputText:
        
        A text input to the model.
        
        class OutputText:
        
        A text output from the model.
        
        String text
        
        The text output from the model.
        
        JsonValue; type "output_text"constant
        
        The type of the output text. Always output_text.
        
        OUTPUT_TEXT("output_text")
        
        class InputImage:
        
        An image input block used within EvalItem content arrays.
        
        String imageUrl
        
        The URL of the image input.
        
        JsonValue; type "input_image"constant
        
        The type of the image input. Always input_image.
        
        INPUT_IMAGE("input_image")
        
        Optional<String> detail
        
        The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        List<EvalContentItem>
        
        String
        
        class ResponseInputText:
        
        A text input to the model.
        
        OutputText
        
        InputImage
        
        class ResponseInputAudio:
        
        An audio input to the model.
        
        Role role
        
        The role of the message input. One of user, assistant, system, or developer.
        
        USER("user")
        
        ASSISTANT("assistant")
        
        SYSTEM("system")
        
        DEVELOPER("developer")
        
        Optional<Type> type
        
        The type of the message input. Always message.
        
        MESSAGE("message")
        
        List<String> labels
        
        The labels to assign to each item in the evaluation.
        
        String model
        
        The model to use for the evaluation. Must support structured outputs.
        
        String name
        
        The name of the grader.
        
        List<String> passingLabels
        
        The labels that indicate a passing result. Must be a subset of labels.
        
        JsonValue; type "label_model"constant
        
        The object type, which is always label_model.
        
        LABEL_MODEL("label_model")
        
        String name
        
        The name of the grader.
        
        JsonValue; type "multi"constant
        
        The object type, which is always multi.
        
        MULTI("multi")
    - Optional<ReinforcementHyperparameters> hyperparameters
      
      The hyperparameters used for the reinforcement fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ComputeMultiplier> computeMultiplier
        
        Multiplier on amount of compute used for exploring search space during training.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<EvalInterval> evalInterval
        
        The number of training steps between evaluation runs.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<EvalSamples> evalSamples
        
        Number of evaluation samples to generate per training step.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<ReasoningEffort> reasoningEffort
        
        Level of reasoning effort.
        
        DEFAULT("default")
        
        LOW("low")
        
        MEDIUM("medium")
        
        HIGH("high")
  - Optional<SupervisedMethod> supervised
    
    Configuration for the supervised fine-tuning method.
    - Optional<SupervisedHyperparameters> hyperparameters
      
      The hyperparameters used for the fine-tuning job.
      - Optional<BatchSize> batchSize
        
        Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
        
        JsonValue;
        
        AUTO("auto")
        
        long
      - Optional<LearningRateMultiplier> learningRateMultiplier
        
        Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
        
        JsonValue;
        
        AUTO("auto")
        
        double
      - Optional<NEpochs> nEpochs
        
        The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
        
        JsonValue;
        
        AUTO("auto")
        
        long

Fine Tuning Job Event

class FineTuningJobEvent:

Fine-tuning job event object
- String id
  
  The object identifier.
- long createdAt
  
  The Unix timestamp (in seconds) for when the fine-tuning job was created.
- Level level
  
  The log level of the event.
  - INFO("info")
  - WARN("warn")
  - ERROR("error")
- String message
  
  The message of the event.
- JsonValue; object_ "fine_tuning.job.event"constant
  
  The object type, which is always "fine_tuning.job.event".
  - FINE_TUNING_JOB_EVENT("fine_tuning.job.event")
- Optional<JsonValue> data
  
  The data associated with the event.
- Optional<Type> type
  
  The type of event.
  - MESSAGE("message")
  - METRICS("metrics")

Fine Tuning Job Wandb Integration

class 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.
- String project
  
  The name of the project that the new run will be created under.
- Optional<String> entity
  
  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.
- Optional<String> name
  
  A display name to set for the run. If not set, we will use the Job ID as the name.
- Optional<List<String>> tags
  
  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}".

Fine Tuning Job Wandb Integration Object

class FineTuningJobWandbIntegrationObject:
- JsonValue; type "wandb"constant
  
  The type of the integration being enabled for the fine-tuning job
  - WANDB("wandb")
- FineTuningJobWandbIntegration wandb
  
  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.
  - String project
    
    The name of the project that the new run will be created under.
  - Optional<String> entity
    
    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.
  - Optional<String> name
    
    A display name to set for the run. If not set, we will use the Job ID as the name.
  - Optional<List<String>> tags
    
    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}".

Checkpoints

List fine-tuning checkpoints

CheckpointListPage fineTuning().jobs().checkpoints().list(CheckpointListParamsparams = CheckpointListParams.none(), RequestOptionsrequestOptions = RequestOptions.none())

get /fine_tuning/jobs/{fine_tuning_job_id}/checkpoints

List checkpoints for a fine-tuning job.

Parameters

CheckpointListParams params
- Optional<String> fineTuningJobId
- Optional<String> after
  
  Identifier for the last checkpoint ID from the previous pagination request.
- Optional<Long> limit
  
  Number of checkpoints to retrieve.

Returns

class FineTuningJobCheckpoint:

The fine_tuning.job.checkpoint object represents a model checkpoint for a fine-tuning job that is ready to use.
- String id
  
  The checkpoint identifier, which can be referenced in the API endpoints.
- long createdAt
  
  The Unix timestamp (in seconds) for when the checkpoint was created.
- String fineTunedModelCheckpoint
  
  The name of the fine-tuned checkpoint model that is created.
- String fineTuningJobId
  
  The name of the fine-tuning job that this checkpoint was created from.
- Metrics metrics
  
  Metrics at the step number during the fine-tuning job.
  - Optional<Double> fullValidLoss
  - Optional<Double> fullValidMeanTokenAccuracy
  - Optional<Double> step
  - Optional<Double> trainLoss
  - Optional<Double> trainMeanTokenAccuracy
  - Optional<Double> validLoss
  - Optional<Double> validMeanTokenAccuracy
- JsonValue; object_ "fine_tuning.job.checkpoint"constant
  
  The object type, which is always "fine_tuning.job.checkpoint".
  - FINE_TUNING_JOB_CHECKPOINT("fine_tuning.job.checkpoint")
- long stepNumber
  
  The step number that the checkpoint was created at.

Example

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.finetuning.jobs.checkpoints.CheckpointListPage;
import com.openai.models.finetuning.jobs.checkpoints.CheckpointListParams;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        CheckpointListPage page = client.fineTuning().jobs().checkpoints().list("ft-AF1WoRqd3aJAHsqc9NY7iL8F");
    }
}

Response

{
  "data": [
    {
      "id": "id",
      "created_at": 0,
      "fine_tuned_model_checkpoint": "fine_tuned_model_checkpoint",
      "fine_tuning_job_id": "fine_tuning_job_id",
      "metrics": {
        "full_valid_loss": 0,
        "full_valid_mean_token_accuracy": 0,
        "step": 0,
        "train_loss": 0,
        "train_mean_token_accuracy": 0,
        "valid_loss": 0,
        "valid_mean_token_accuracy": 0
      },
      "object": "fine_tuning.job.checkpoint",
      "step_number": 0
    }
  ],
  "has_more": true,
  "object": "list",
  "first_id": "first_id",
  "last_id": "last_id"
}

Domain Types

Fine Tuning Job Checkpoint

class FineTuningJobCheckpoint:

The fine_tuning.job.checkpoint object represents a model checkpoint for a fine-tuning job that is ready to use.
- String id
  
  The checkpoint identifier, which can be referenced in the API endpoints.
- long createdAt
  
  The Unix timestamp (in seconds) for when the checkpoint was created.
- String fineTunedModelCheckpoint
  
  The name of the fine-tuned checkpoint model that is created.
- String fineTuningJobId
  
  The name of the fine-tuning job that this checkpoint was created from.
- Metrics metrics
  
  Metrics at the step number during the fine-tuning job.
  - Optional<Double> fullValidLoss
  - Optional<Double> fullValidMeanTokenAccuracy
  - Optional<Double> step
  - Optional<Double> trainLoss
  - Optional<Double> trainMeanTokenAccuracy
  - Optional<Double> validLoss
  - Optional<Double> validMeanTokenAccuracy
- JsonValue; object_ "fine_tuning.job.checkpoint"constant
  
  The object type, which is always "fine_tuning.job.checkpoint".
  - FINE_TUNING_JOB_CHECKPOINT("fine_tuning.job.checkpoint")
- long stepNumber
  
  The step number that the checkpoint was created at.

java/resources/fine_tuning/subresources/jobs/index.md +14 −8

6 6

7**post** `/fine_tuning/jobs`7**post** `/fine_tuning/jobs`

8 8

~~9Create fine-tuning job~~9Creates a fine-tuning job which begins the process of creating a new model from a given dataset.

11Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.

13[Learn more about fine-tuning](https://platform.openai.com/docs/guides/model-optimization)

10 14

11### Parameters15### Parameters

12 16

1738 1742

1739**get** `/fine_tuning/jobs`1743**get** `/fine_tuning/jobs`

1740 1744

1741List fine-tuning jobs1745List your organization's fine-tuning jobs

1742 1746

1743### Parameters1747### Parameters

1744 1748

2700 2704

2701**get** `/fine_tuning/jobs/{fine_tuning_job_id}`2705**get** `/fine_tuning/jobs/{fine_tuning_job_id}`

2702 2706

2703Retrieve fine-tuning job2707Get info about a fine-tuning job.

2708

2709[Learn more about fine-tuning](https://platform.openai.com/docs/guides/model-optimization)

2704 2710

2705### Parameters2711### Parameters

2706 2712

3646 3652

3647**get** `/fine_tuning/jobs/{fine_tuning_job_id}/events`3653**get** `/fine_tuning/jobs/{fine_tuning_job_id}/events`

3648 3654

3649List fine-tuning events3655Get status updates for a fine-tuning job.

3650 3656

3651### Parameters3657### Parameters

3652 3658

3755 3761

3756**post** `/fine_tuning/jobs/{fine_tuning_job_id}/cancel`3762**post** `/fine_tuning/jobs/{fine_tuning_job_id}/cancel`

3757 3763

3758Cancel fine-tuning3764Immediately cancel a fine-tune job.

3759 3765

3760### Parameters3766### Parameters

3761 3767

4701 4707

4702**post** `/fine_tuning/jobs/{fine_tuning_job_id}/pause`4708**post** `/fine_tuning/jobs/{fine_tuning_job_id}/pause`

4703 4709

4704Pause fine-tuning4710Pause a fine-tune job.

4705 4711

4706### Parameters4712### Parameters

4707 4713

5647 5653

5648**post** `/fine_tuning/jobs/{fine_tuning_job_id}/resume`5654**post** `/fine_tuning/jobs/{fine_tuning_job_id}/resume`

5649 5655

5650Resume fine-tuning5656Resume a fine-tune job.

5651 5657

5652### Parameters5658### Parameters

5653 5659

7529 7535

7530**get** `/fine_tuning/jobs/{fine_tuning_job_id}/checkpoints`7536**get** `/fine_tuning/jobs/{fine_tuning_job_id}/checkpoints`

7531 7537

7532List fine-tuning checkpoints7538List checkpoints for a fine-tuning job.

7533 7539

7534### Parameters7540### Parameters

7535 7541

java/resources/fine_tuning/subresources/jobs/index.md 2026-07-10 23:02 UTC to 2026-07-12 06:58 UTC

Jobs

Create fine-tuning job

Parameters

Returns

Example

Response

List fine-tuning jobs

Parameters

Returns

Example

Response

Retrieve fine-tuning job

Parameters

Returns

Example

Response

List fine-tuning events

Parameters

Returns

Example

Response

Cancel fine-tuning

Parameters

Returns

Example

Response

Pause fine-tuning

Parameters

Returns

Example

Response

Resume fine-tuning

Parameters

Returns

Example

Response

Domain Types

Fine Tuning Job

Fine Tuning Job Event

Fine Tuning Job Wandb Integration

Fine Tuning Job Wandb Integration Object

Checkpoints

List fine-tuning checkpoints

Parameters

Returns

Example

Response

Domain Types

Fine Tuning Job Checkpoint