Jobs

Create fine-tuning job

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

post /fine_tuning/jobs

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

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

Learn more about fine-tuning

Parameters

• model: Union[str, Literal["babbage-002", "davinci-002", "gpt-3.5-turbo", "gpt-4o-mini"]]

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

  • str

  • Literal["babbage-002", "davinci-002", "gpt-3.5-turbo", "gpt-4o-mini"]

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

    • "babbage-002"

    • "davinci-002"

    • "gpt-3.5-turbo"

    • "gpt-4o-mini"

• training_file: str

  The ID of an uploaded file that contains training data.

  See upload file for how to upload a file.

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

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

  See the fine-tuning guide for more details.

• hyperparameters: Optional[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.

  • batch_size: Optional[Union[Literal["auto"], int]]

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

    • Literal["auto"]

      • "auto"

    • int

  • learning_rate_multiplier: Optional[Union[Literal["auto"], float]]

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

    • Literal["auto"]

      • "auto"

    • float

  • n_epochs: Optional[Union[Literal["auto"], int]]

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

    • Literal["auto"]

      • "auto"

    • int

• integrations: Optional[Iterable[Integration]]

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

  • type: Literal["wandb"]

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

    • "wandb"

  • wandb: IntegrationWandb

    The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

    • project: str

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

    • entity: Optional[str]

      The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

    • name: Optional[str]

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

    • tags: Optional[Sequence[str]]

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

• metadata: Optional[Metadata]

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

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

• method: Optional[Method]

  The method used for fine-tuning.

  • type: Literal["supervised", "dpo", "reinforcement"]

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

    • "supervised"

    • "dpo"

    • "reinforcement"

  • dpo: Optional[DpoMethodParam]

    Configuration for the DPO fine-tuning method.

    • hyperparameters: Optional[DpoHyperparameters]

      The hyperparameters used for the DPO fine-tuning job.

      • batch_size: Optional[Union[Literal["auto"], int, null]]

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

        • Literal["auto"]

          • "auto"

        • int

      • beta: Optional[Union[Literal["auto"], float, null]]

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

        • Literal["auto"]

          • "auto"

        • float

      • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

        • Literal["auto"]

          • "auto"

        • float

      • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

        • Literal["auto"]

          • "auto"

        • int

  • reinforcement: Optional[ReinforcementMethodParam]

    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.

        • input: str

          The input text. This may include template strings.

        • name: str

          The name of the grader.

        • operation: Literal["eq", "ne", "like", "ilike"]

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

          • "eq"

          • "ne"

          • "like"

          • "ilike"

        • reference: str

          The reference text. This may include template strings.

        • type: Literal["string_check"]

          The object type, which is always string_check.

          • "string_check"

      • class TextSimilarityGrader: …

        A TextSimilarityGrader object which grades text based on similarity metrics.

        • evaluation_metric: Literal["cosine", "fuzzy_match", "bleu", 8 more]

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

          • "cosine"

          • "fuzzy_match"

          • "bleu"

          • "gleu"

          • "meteor"

          • "rouge_1"

          • "rouge_2"

          • "rouge_3"

          • "rouge_4"

          • "rouge_5"

          • "rouge_l"

        • input: str

          The text being graded.

        • name: str

          The name of the grader.

        • reference: str

          The text being graded against.

        • type: Literal["text_similarity"]

          The type of grader.

          • "text_similarity"

      • class PythonGrader: …

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

        • name: str

          The name of the grader.

        • source: str

          The source code of the python script.

        • type: Literal["python"]

          The object type, which is always python.

          • "python"

        • image_tag: Optional[str]

          The image tag to use for the python script.

      • class ScoreModelGrader: …

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

        • input: List[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: InputContent

            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.

            • str

              A text input to the model.

            • class ResponseInputText: …

              A text input to the model.

              • text: str

                The text input to the model.

              • type: Literal["input_text"]

                The type of the input item. Always input_text.

                • "input_text"

              • prompt_cache_breakpoint: Optional[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.

                • mode: Literal["explicit"]

                  The breakpoint mode. Always explicit.

                  • "explicit"

            • class InputContentOutputText: …

              A text output from the model.

              • text: str

                The text output from the model.

              • type: Literal["output_text"]

                The type of the output text. Always output_text.

                • "output_text"

            • class InputContentInputImage: …

              An image input block used within EvalItem content arrays.

              • image_url: str

                The URL of the image input.

              • type: Literal["input_image"]

                The type of the image input. Always input_image.

                • "input_image"

              • detail: Optional[str]

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

            • class ResponseInputAudio: …

              An audio input to the model.

              • input_audio: InputAudio

                • data: str

                  Base64-encoded audio data.

                • format: Literal["mp3", "wav"]

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

                  • "mp3"

                  • "wav"

              • type: Literal["input_audio"]

                The type of the input item. Always input_audio.

                • "input_audio"

            • List[GraderInputItem]

              • str

                A text input to the model.

              • class ResponseInputText: …

                A text input to the model.

              • class GraderInputItemOutputText: …

                A text output from the model.

                • text: str

                  The text output from the model.

                • type: Literal["output_text"]

                  The type of the output text. Always output_text.

                  • "output_text"

              • class GraderInputItemInputImage: …

                An image input block used within EvalItem content arrays.

                • image_url: str

                  The URL of the image input.

                • type: Literal["input_image"]

                  The type of the image input. Always input_image.

                  • "input_image"

                • detail: Optional[str]

                  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: Literal["user", "assistant", "system", "developer"]

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

            • "user"

            • "assistant"

            • "system"

            • "developer"

          • type: Optional[Literal["message"]]

            The type of the message input. Always message.

            • "message"

        • model: str

          The model to use for the evaluation.

        • name: str

          The name of the grader.

        • type: Literal["score_model"]

          The object type, which is always score_model.

          • "score_model"

        • range: Optional[List[float]]

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

        • sampling_params: Optional[SamplingParams]

          The sampling parameters for the model.

          • max_completions_tokens: Optional[int]

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

          • reasoning_effort: Optional[ReasoningEffort]

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

            • "none"

            • "minimal"

            • "low"

            • "medium"

            • "high"

            • "xhigh"

            • "max"

          • seed: Optional[int]

            A seed value to initialize the randomness, during sampling.

          • temperature: Optional[float]

            A higher temperature increases randomness in the outputs.

          • top_p: Optional[float]

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

      • class MultiGrader: …

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

        • calculate_output: str

          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.

            • input: List[Input]

              • content: InputContent

                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.

                • str

                  A text input to the model.

                • class ResponseInputText: …

                  A text input to the model.

                • class InputContentOutputText: …

                  A text output from the model.

                  • text: str

                    The text output from the model.

                  • type: Literal["output_text"]

                    The type of the output text. Always output_text.

                    • "output_text"

                • class InputContentInputImage: …

                  An image input block used within EvalItem content arrays.

                  • image_url: str

                    The URL of the image input.

                  • type: Literal["input_image"]

                    The type of the image input. Always input_image.

                    • "input_image"

                  • detail: Optional[str]

                    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[GraderInputItem]

                  • str

                    A text input to the model.

                  • class ResponseInputText: …

                    A text input to the model.

                  • class GraderInputItemOutputText: …

                    A text output from the model.

                  • class GraderInputItemInputImage: …

                    An image input block used within EvalItem content arrays.

                  • class ResponseInputAudio: …

                    An audio input to the model.

              • role: Literal["user", "assistant", "system", "developer"]

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

                • "user"

                • "assistant"

                • "system"

                • "developer"

              • type: Optional[Literal["message"]]

                The type of the message input. Always message.

                • "message"

            • labels: List[str]

              The labels to assign to each item in the evaluation.

            • model: str

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

            • name: str

              The name of the grader.

            • passing_labels: List[str]

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

            • type: Literal["label_model"]

              The object type, which is always label_model.

              • "label_model"

        • name: str

          The name of the grader.

        • type: Literal["multi"]

          The object type, which is always multi.

          • "multi"

    • hyperparameters: Optional[ReinforcementHyperparameters]

      The hyperparameters used for the reinforcement fine-tuning job.

      • batch_size: Optional[Union[Literal["auto"], int, null]]

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

        • Literal["auto"]

          • "auto"

        • int

      • compute_multiplier: Optional[Union[Literal["auto"], float, null]]

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

        • Literal["auto"]

          • "auto"

        • float

      • eval_interval: Optional[Union[Literal["auto"], int, null]]

        The number of training steps between evaluation runs.

        • Literal["auto"]

          • "auto"

        • int

      • eval_samples: Optional[Union[Literal["auto"], int, null]]

        Number of evaluation samples to generate per training step.

        • Literal["auto"]

          • "auto"

        • int

      • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

        • Literal["auto"]

          • "auto"

        • float

      • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

        • Literal["auto"]

          • "auto"

        • int

      • reasoning_effort: Optional[Literal["default", "low", "medium", "high"]]

        Level of reasoning effort.

        • "default"

        • "low"

        • "medium"

        • "high"

  • supervised: Optional[SupervisedMethodParam]

    Configuration for the supervised fine-tuning method.

    • hyperparameters: Optional[SupervisedHyperparameters]

      The hyperparameters used for the fine-tuning job.

      • batch_size: Optional[Union[Literal["auto"], int, null]]

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

        • Literal["auto"]

          • "auto"

        • int

      • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

        • Literal["auto"]

          • "auto"

        • float

      • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

        • Literal["auto"]

          • "auto"

        • int

• seed: Optional[int]

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

• suffix: Optional[str]

  A string of up to 64 characters that will be added to your fine-tuned model name.

  For example, a suffix of "custom-model-name" would produce a model name like ft:gpt-4o-mini:openai:custom-model-name:7p4lURel.

• validation_file: Optional[str]

  The ID of an uploaded file that contains validation data.

  If you provide this file, the data is used to generate validation metrics periodically during fine-tuning. These metrics can be viewed in the fine-tuning results file. The same data should not be present in both train and validation files.

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

  See the fine-tuning guide for more details.

Returns

• class FineTuningJob: …

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

  • id: str

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

  • created_at: int

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

  • error: Optional[Error]

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

    • code: str

      A machine-readable error code.

    • message: str

      A human-readable error message.

    • param: Optional[str]

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

  • fine_tuned_model: Optional[str]

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

  • finished_at: Optional[int]

    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.

    • batch_size: Optional[Union[Literal["auto"], int, null]]

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

      • Literal["auto"]

        • "auto"

      • int

    • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

      • Literal["auto"]

        • "auto"

      • float

    • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

      • Literal["auto"]

        • "auto"

      • int

  • model: str

    The base model that is being fine-tuned.

  • object: Literal["fine_tuning.job"]

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

    • "fine_tuning.job"

  • organization_id: str

    The organization that owns the fine-tuning job.

  • result_files: List[str]

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

  • seed: int

    The seed used for the fine-tuning job.

  • status: Literal["validating_files", "queued", "running", 3 more]

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

    • "validating_files"

    • "queued"

    • "running"

    • "succeeded"

    • "failed"

    • "cancelled"

  • trained_tokens: Optional[int]

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

  • training_file: str

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

  • validation_file: Optional[str]

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

  • estimated_finish: Optional[int]

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

  • integrations: Optional[List[FineTuningJobWandbIntegrationObject]]

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

    • type: Literal["wandb"]

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

      • "wandb"

    • wandb: FineTuningJobWandbIntegration

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

      • project: str

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

      • entity: Optional[str]

        The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

      • name: Optional[str]

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

      • tags: Optional[List[str]]

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

  • metadata: Optional[Metadata]

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

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

  • method: Optional[Method]

    The method used for fine-tuning.

    • type: Literal["supervised", "dpo", "reinforcement"]

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

      • "supervised"

      • "dpo"

      • "reinforcement"

    • dpo: Optional[DpoMethod]

      Configuration for the DPO fine-tuning method.

      • hyperparameters: Optional[DpoHyperparameters]

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • beta: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

    • reinforcement: Optional[ReinforcementMethod]

      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.

          • input: str

            The input text. This may include template strings.

          • name: str

            The name of the grader.

          • operation: Literal["eq", "ne", "like", "ilike"]

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

            • "eq"

            • "ne"

            • "like"

            • "ilike"

          • reference: str

            The reference text. This may include template strings.

          • type: Literal["string_check"]

            The object type, which is always string_check.

            • "string_check"

        • class TextSimilarityGrader: …

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: Literal["cosine", "fuzzy_match", "bleu", 8 more]

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

            • "cosine"

            • "fuzzy_match"

            • "bleu"

            • "gleu"

            • "meteor"

            • "rouge_1"

            • "rouge_2"

            • "rouge_3"

            • "rouge_4"

            • "rouge_5"

            • "rouge_l"

          • input: str

            The text being graded.

          • name: str

            The name of the grader.

          • reference: str

            The text being graded against.

          • type: Literal["text_similarity"]

            The type of grader.

            • "text_similarity"

        • class PythonGrader: …

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

          • name: str

            The name of the grader.

          • source: str

            The source code of the python script.

          • type: Literal["python"]

            The object type, which is always python.

            • "python"

          • image_tag: Optional[str]

            The image tag to use for the python script.

        • class ScoreModelGrader: …

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

          • input: List[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: InputContent

              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.

              • str

                A text input to the model.

              • class ResponseInputText: …

                A text input to the model.

                • text: str

                  The text input to the model.

                • type: Literal["input_text"]

                  The type of the input item. Always input_text.

                  • "input_text"

                • prompt_cache_breakpoint: Optional[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.

                  • mode: Literal["explicit"]

                    The breakpoint mode. Always explicit.

                    • "explicit"

              • class InputContentOutputText: …

                A text output from the model.

                • text: str

                  The text output from the model.

                • type: Literal["output_text"]

                  The type of the output text. Always output_text.

                  • "output_text"

              • class InputContentInputImage: …

                An image input block used within EvalItem content arrays.

                • image_url: str

                  The URL of the image input.

                • type: Literal["input_image"]

                  The type of the image input. Always input_image.

                  • "input_image"

                • detail: Optional[str]

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

              • class ResponseInputAudio: …

                An audio input to the model.

                • input_audio: InputAudio

                  • data: str

                    Base64-encoded audio data.

                  • format: Literal["mp3", "wav"]

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

                    • "mp3"

                    • "wav"

                • type: Literal["input_audio"]

                  The type of the input item. Always input_audio.

                  • "input_audio"

              • List[GraderInputItem]

                • str

                  A text input to the model.

                • class ResponseInputText: …

                  A text input to the model.

                • class GraderInputItemOutputText: …

                  A text output from the model.

                  • text: str

                    The text output from the model.

                  • type: Literal["output_text"]

                    The type of the output text. Always output_text.

                    • "output_text"

                • class GraderInputItemInputImage: …

                  An image input block used within EvalItem content arrays.

                  • image_url: str

                    The URL of the image input.

                  • type: Literal["input_image"]

                    The type of the image input. Always input_image.

                    • "input_image"

                  • detail: Optional[str]

                    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: Literal["user", "assistant", "system", "developer"]

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

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: Optional[Literal["message"]]

              The type of the message input. Always message.

              • "message"

          • model: str

            The model to use for the evaluation.

          • name: str

            The name of the grader.

          • type: Literal["score_model"]

            The object type, which is always score_model.

            • "score_model"

          • range: Optional[List[float]]

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

          • sampling_params: Optional[SamplingParams]

            The sampling parameters for the model.

            • max_completions_tokens: Optional[int]

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

            • reasoning_effort: Optional[ReasoningEffort]

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

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

              • "max"

            • seed: Optional[int]

              A seed value to initialize the randomness, during sampling.

            • temperature: Optional[float]

              A higher temperature increases randomness in the outputs.

            • top_p: Optional[float]

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

        • class MultiGrader: …

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

          • calculate_output: str

            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.

              • input: List[Input]

                • content: InputContent

                  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.

                  • str

                    A text input to the model.

                  • class ResponseInputText: …

                    A text input to the model.

                  • class InputContentOutputText: …

                    A text output from the model.

                    • text: str

                      The text output from the model.

                    • type: Literal["output_text"]

                      The type of the output text. Always output_text.

                      • "output_text"

                  • class InputContentInputImage: …

                    An image input block used within EvalItem content arrays.

                    • image_url: str

                      The URL of the image input.

                    • type: Literal["input_image"]

                      The type of the image input. Always input_image.

                      • "input_image"

                    • detail: Optional[str]

                      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[GraderInputItem]

                    • str

                      A text input to the model.

                    • class ResponseInputText: …

                      A text input to the model.

                    • class GraderInputItemOutputText: …

                      A text output from the model.

                    • class GraderInputItemInputImage: …

                      An image input block used within EvalItem content arrays.

                    • class ResponseInputAudio: …

                      An audio input to the model.

                • role: Literal["user", "assistant", "system", "developer"]

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

                  • "user"

                  • "assistant"

                  • "system"

                  • "developer"

                • type: Optional[Literal["message"]]

                  The type of the message input. Always message.

                  • "message"

              • labels: List[str]

                The labels to assign to each item in the evaluation.

              • model: str

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

              • name: str

                The name of the grader.

              • passing_labels: List[str]

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

              • type: Literal["label_model"]

                The object type, which is always label_model.

                • "label_model"

          • name: str

            The name of the grader.

          • type: Literal["multi"]

            The object type, which is always multi.

            • "multi"

      • hyperparameters: Optional[ReinforcementHyperparameters]

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • compute_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • eval_interval: Optional[Union[Literal["auto"], int, null]]

          The number of training steps between evaluation runs.

          • Literal["auto"]

            • "auto"

          • int

        • eval_samples: Optional[Union[Literal["auto"], int, null]]

          Number of evaluation samples to generate per training step.

          • Literal["auto"]

            • "auto"

          • int

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • reasoning_effort: Optional[Literal["default", "low", "medium", "high"]]

          Level of reasoning effort.

          • "default"

          • "low"

          • "medium"

          • "high"

    • supervised: Optional[SupervisedMethod]

      Configuration for the supervised fine-tuning method.

      • hyperparameters: Optional[SupervisedHyperparameters]

        The hyperparameters used for the fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
fine_tuning_job = client.fine_tuning.jobs.create(
    model="gpt-4o-mini",
    training_file="file-abc123",
)
print(fine_tuning_job.id)

Response

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

Example

from openai import OpenAI
client = OpenAI()

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

Response

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "gpt-4o-mini-2024-07-18",
  "created_at": 1721764800,
  "fine_tuned_model": null,
  "organization_id": "org-123",
  "result_files": [],
  "status": "queued",
  "validation_file": null,
  "training_file": "file-abc123",
  "method": {
    "type": "supervised",
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
      }
    }
  },
  "metadata": null
}

Epochs

from openai import OpenAI
from openai.types.fine_tuning import SupervisedMethod, SupervisedHyperparameters

client = OpenAI()

client.fine_tuning.jobs.create(
  training_file="file-abc123",
  model="gpt-4o-mini",
  method={
    "type": "supervised",
    "supervised": SupervisedMethod(
      hyperparameters=SupervisedHyperparameters(
        n_epochs=2
      )
    )
  }
)

Response

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "gpt-4o-mini",
  "created_at": 1721764800,
  "fine_tuned_model": null,
  "organization_id": "org-123",
  "result_files": [],
  "status": "queued",
  "validation_file": null,
  "training_file": "file-abc123",
  "hyperparameters": {
    "batch_size": "auto",
    "learning_rate_multiplier": "auto",
    "n_epochs": 2
  },
  "method": {
    "type": "supervised",
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": 2
      }
    }
  },
  "metadata": null,
  "error": {
    "code": null,
    "message": null,
    "param": null
  },
  "finished_at": null,
  "seed": 683058546,
  "trained_tokens": null,
  "estimated_finish": null,
  "integrations": [],
  "user_provided_suffix": null,
  "usage_metrics": null,
  "shared_with_openai": false
}

DPO

from openai import OpenAI
from openai.types.fine_tuning import DpoMethod, DpoHyperparameters

client = OpenAI()

client.fine_tuning.jobs.create(
  training_file="file-abc",
  validation_file="file-123",
  model="gpt-4o-mini",
  method={
    "type": "dpo",
    "dpo": DpoMethod(
      hyperparameters=DpoHyperparameters(beta=0.1)
    )
  }
)

Response

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc",
  "model": "gpt-4o-mini",
  "created_at": 1746130590,
  "fine_tuned_model": null,
  "organization_id": "org-abc",
  "result_files": [],
  "status": "queued",
  "validation_file": "file-123",
  "training_file": "file-abc",
  "method": {
    "type": "dpo",
    "dpo": {
      "hyperparameters": {
        "beta": 0.1,
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto"
      }
    }
  },
  "metadata": null,
  "error": {
    "code": null,
    "message": null,
    "param": null
  },
  "finished_at": null,
  "hyperparameters": null,
  "seed": 1036326793,
  "estimated_finish": null,
  "integrations": [],
  "user_provided_suffix": null,
  "usage_metrics": null,
  "shared_with_openai": false
}

Reinforcement

from openai import OpenAI
from openai.types.fine_tuning import ReinforcementMethod, ReinforcementHyperparameters
from openai.types.graders import StringCheckGrader

client = OpenAI()

client.fine_tuning.jobs.create(
  training_file="file-abc",
  validation_file="file-123",
  model="o4-mini",
  method={
    "type": "reinforcement",
    "reinforcement": ReinforcementMethod(
      grader=StringCheckGrader(
        name="Example string check grader",
        type="string_check",
        input="{{item.label}}",
        operation="eq",
        reference="{{sample.output_text}}"
      ),
      hyperparameters=ReinforcementHyperparameters(
          reasoning_effort="medium",
      )
    )
  }, 
  seed=42,
)

Response

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "o4-mini",
  "created_at": 1721764800,
  "finished_at": null,
  "fine_tuned_model": null,
  "organization_id": "org-123",
  "result_files": [],
  "status": "validating_files",
  "validation_file": "file-123",
  "training_file": "file-abc",
  "trained_tokens": null,
  "error": {},
  "user_provided_suffix": null,
  "seed": 950189191,
  "estimated_finish": null,
  "integrations": [],
  "method": {
    "type": "reinforcement",
    "reinforcement": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
        "eval_interval": "auto",
        "eval_samples": "auto",
        "compute_multiplier": "auto",
        "reasoning_effort": "medium"
      },
      "grader": {
        "type": "string_check",
        "name": "Example string check grader",
        "input": "{{sample.output_text}}",
        "reference": "{{item.label}}",
        "operation": "eq"
      },
      "response_format": null
    }
  },
  "metadata": null,
  "usage_metrics": null,
  "shared_with_openai": false
}
      

Validation file

from openai import OpenAI
client = OpenAI()

client.fine_tuning.jobs.create(
  training_file="file-abc123",
  validation_file="file-def456",
  model="gpt-4o-mini"
)

Response

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "gpt-4o-mini-2024-07-18",
  "created_at": 1721764800,
  "fine_tuned_model": null,
  "organization_id": "org-123",
  "result_files": [],
  "status": "queued",
  "validation_file": "file-abc123",
  "training_file": "file-abc123",
  "method": {
    "type": "supervised",
    "supervised": {
      "hyperparameters": {
        "batch_size": "auto",
        "learning_rate_multiplier": "auto",
        "n_epochs": "auto",
      }
    }
  },
  "metadata": null
}

List fine-tuning jobs

fine_tuning.jobs.list(JobListParams**kwargs) -> SyncCursorPage[FineTuningJob]

get /fine_tuning/jobs

List your organization's fine-tuning jobs

Parameters

• after: Optional[str]

  Identifier for the last job from the previous pagination request.

• limit: Optional[int]

  Number of fine-tuning jobs to retrieve.

• metadata: Optional[Dict[str, str]]

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

Returns

• class FineTuningJob: …

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

  • id: str

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

  • created_at: int

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

  • error: Optional[Error]

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

    • code: str

      A machine-readable error code.

    • message: str

      A human-readable error message.

    • param: Optional[str]

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

  • fine_tuned_model: Optional[str]

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

  • finished_at: Optional[int]

    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.

    • batch_size: Optional[Union[Literal["auto"], int, null]]

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

      • Literal["auto"]

        • "auto"

      • int

    • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

      • Literal["auto"]

        • "auto"

      • float

    • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

      • Literal["auto"]

        • "auto"

      • int

  • model: str

    The base model that is being fine-tuned.

  • object: Literal["fine_tuning.job"]

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

    • "fine_tuning.job"

  • organization_id: str

    The organization that owns the fine-tuning job.

  • result_files: List[str]

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

  • seed: int

    The seed used for the fine-tuning job.

  • status: Literal["validating_files", "queued", "running", 3 more]

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

    • "validating_files"

    • "queued"

    • "running"

    • "succeeded"

    • "failed"

    • "cancelled"

  • trained_tokens: Optional[int]

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

  • training_file: str

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

  • validation_file: Optional[str]

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

  • estimated_finish: Optional[int]

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

  • integrations: Optional[List[FineTuningJobWandbIntegrationObject]]

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

    • type: Literal["wandb"]

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

      • "wandb"

    • wandb: FineTuningJobWandbIntegration

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

      • project: str

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

      • entity: Optional[str]

        The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

      • name: Optional[str]

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

      • tags: Optional[List[str]]

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

  • metadata: Optional[Metadata]

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

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

  • method: Optional[Method]

    The method used for fine-tuning.

    • type: Literal["supervised", "dpo", "reinforcement"]

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

      • "supervised"

      • "dpo"

      • "reinforcement"

    • dpo: Optional[DpoMethod]

      Configuration for the DPO fine-tuning method.

      • hyperparameters: Optional[DpoHyperparameters]

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • beta: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

    • reinforcement: Optional[ReinforcementMethod]

      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.

          • input: str

            The input text. This may include template strings.

          • name: str

            The name of the grader.

          • operation: Literal["eq", "ne", "like", "ilike"]

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

            • "eq"

            • "ne"

            • "like"

            • "ilike"

          • reference: str

            The reference text. This may include template strings.

          • type: Literal["string_check"]

            The object type, which is always string_check.

            • "string_check"

        • class TextSimilarityGrader: …

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: Literal["cosine", "fuzzy_match", "bleu", 8 more]

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

            • "cosine"

            • "fuzzy_match"

            • "bleu"

            • "gleu"

            • "meteor"

            • "rouge_1"

            • "rouge_2"

            • "rouge_3"

            • "rouge_4"

            • "rouge_5"

            • "rouge_l"

          • input: str

            The text being graded.

          • name: str

            The name of the grader.

          • reference: str

            The text being graded against.

          • type: Literal["text_similarity"]

            The type of grader.

            • "text_similarity"

        • class PythonGrader: …

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

          • name: str

            The name of the grader.

          • source: str

            The source code of the python script.

          • type: Literal["python"]

            The object type, which is always python.

            • "python"

          • image_tag: Optional[str]

            The image tag to use for the python script.

        • class ScoreModelGrader: …

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

          • input: List[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: InputContent

              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.

              • str

                A text input to the model.

              • class ResponseInputText: …

                A text input to the model.

                • text: str

                  The text input to the model.

                • type: Literal["input_text"]

                  The type of the input item. Always input_text.

                  • "input_text"

                • prompt_cache_breakpoint: Optional[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.

                  • mode: Literal["explicit"]

                    The breakpoint mode. Always explicit.

                    • "explicit"

              • class InputContentOutputText: …

                A text output from the model.

                • text: str

                  The text output from the model.

                • type: Literal["output_text"]

                  The type of the output text. Always output_text.

                  • "output_text"

              • class InputContentInputImage: …

                An image input block used within EvalItem content arrays.

                • image_url: str

                  The URL of the image input.

                • type: Literal["input_image"]

                  The type of the image input. Always input_image.

                  • "input_image"

                • detail: Optional[str]

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

              • class ResponseInputAudio: …

                An audio input to the model.

                • input_audio: InputAudio

                  • data: str

                    Base64-encoded audio data.

                  • format: Literal["mp3", "wav"]

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

                    • "mp3"

                    • "wav"

                • type: Literal["input_audio"]

                  The type of the input item. Always input_audio.

                  • "input_audio"

              • List[GraderInputItem]

                • str

                  A text input to the model.

                • class ResponseInputText: …

                  A text input to the model.

                • class GraderInputItemOutputText: …

                  A text output from the model.

                  • text: str

                    The text output from the model.

                  • type: Literal["output_text"]

                    The type of the output text. Always output_text.

                    • "output_text"

                • class GraderInputItemInputImage: …

                  An image input block used within EvalItem content arrays.

                  • image_url: str

                    The URL of the image input.

                  • type: Literal["input_image"]

                    The type of the image input. Always input_image.

                    • "input_image"

                  • detail: Optional[str]

                    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: Literal["user", "assistant", "system", "developer"]

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

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: Optional[Literal["message"]]

              The type of the message input. Always message.

              • "message"

          • model: str

            The model to use for the evaluation.

          • name: str

            The name of the grader.

          • type: Literal["score_model"]

            The object type, which is always score_model.

            • "score_model"

          • range: Optional[List[float]]

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

          • sampling_params: Optional[SamplingParams]

            The sampling parameters for the model.

            • max_completions_tokens: Optional[int]

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

            • reasoning_effort: Optional[ReasoningEffort]

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

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

              • "max"

            • seed: Optional[int]

              A seed value to initialize the randomness, during sampling.

            • temperature: Optional[float]

              A higher temperature increases randomness in the outputs.

            • top_p: Optional[float]

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

        • class MultiGrader: …

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

          • calculate_output: str

            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.

              • input: List[Input]

                • content: InputContent

                  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.

                  • str

                    A text input to the model.

                  • class ResponseInputText: …

                    A text input to the model.

                  • class InputContentOutputText: …

                    A text output from the model.

                    • text: str

                      The text output from the model.

                    • type: Literal["output_text"]

                      The type of the output text. Always output_text.

                      • "output_text"

                  • class InputContentInputImage: …

                    An image input block used within EvalItem content arrays.

                    • image_url: str

                      The URL of the image input.

                    • type: Literal["input_image"]

                      The type of the image input. Always input_image.

                      • "input_image"

                    • detail: Optional[str]

                      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[GraderInputItem]

                    • str

                      A text input to the model.

                    • class ResponseInputText: …

                      A text input to the model.

                    • class GraderInputItemOutputText: …

                      A text output from the model.

                    • class GraderInputItemInputImage: …

                      An image input block used within EvalItem content arrays.

                    • class ResponseInputAudio: …

                      An audio input to the model.

                • role: Literal["user", "assistant", "system", "developer"]

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

                  • "user"

                  • "assistant"

                  • "system"

                  • "developer"

                • type: Optional[Literal["message"]]

                  The type of the message input. Always message.

                  • "message"

              • labels: List[str]

                The labels to assign to each item in the evaluation.

              • model: str

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

              • name: str

                The name of the grader.

              • passing_labels: List[str]

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

              • type: Literal["label_model"]

                The object type, which is always label_model.

                • "label_model"

          • name: str

            The name of the grader.

          • type: Literal["multi"]

            The object type, which is always multi.

            • "multi"

      • hyperparameters: Optional[ReinforcementHyperparameters]

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • compute_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • eval_interval: Optional[Union[Literal["auto"], int, null]]

          The number of training steps between evaluation runs.

          • Literal["auto"]

            • "auto"

          • int

        • eval_samples: Optional[Union[Literal["auto"], int, null]]

          Number of evaluation samples to generate per training step.

          • Literal["auto"]

            • "auto"

          • int

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • reasoning_effort: Optional[Literal["default", "low", "medium", "high"]]

          Level of reasoning effort.

          • "default"

          • "low"

          • "medium"

          • "high"

    • supervised: Optional[SupervisedMethod]

      Configuration for the supervised fine-tuning method.

      • hyperparameters: Optional[SupervisedHyperparameters]

        The hyperparameters used for the fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
page = client.fine_tuning.jobs.list()
page = page.data[0]
print(page.id)

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"
}

Example

from openai import OpenAI
client = OpenAI()

client.fine_tuning.jobs.list()

Response

{
  "object": "list",
  "data": [
    {
      "object": "fine_tuning.job",
      "id": "ftjob-abc123",
      "model": "gpt-4o-mini-2024-07-18",
      "created_at": 1721764800,
      "fine_tuned_model": null,
      "organization_id": "org-123",
      "result_files": [],
      "status": "queued",
      "validation_file": null,
      "training_file": "file-abc123",
      "metadata": {
        "key": "value"
      }
    },
    { ... },
    { ... }
  ], "has_more": true
}

Retrieve fine-tuning job

fine_tuning.jobs.retrieve(strfine_tuning_job_id) -> FineTuningJob

get /fine_tuning/jobs/{fine_tuning_job_id}

Get info about a fine-tuning job.

Learn more about fine-tuning

Parameters

• fine_tuning_job_id: str

Returns

• class FineTuningJob: …

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

  • id: str

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

  • created_at: int

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

  • error: Optional[Error]

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

    • code: str

      A machine-readable error code.

    • message: str

      A human-readable error message.

    • param: Optional[str]

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

  • fine_tuned_model: Optional[str]

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

  • finished_at: Optional[int]

    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.

    • batch_size: Optional[Union[Literal["auto"], int, null]]

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

      • Literal["auto"]

        • "auto"

      • int

    • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

      • Literal["auto"]

        • "auto"

      • float

    • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

      • Literal["auto"]

        • "auto"

      • int

  • model: str

    The base model that is being fine-tuned.

  • object: Literal["fine_tuning.job"]

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

    • "fine_tuning.job"

  • organization_id: str

    The organization that owns the fine-tuning job.

  • result_files: List[str]

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

  • seed: int

    The seed used for the fine-tuning job.

  • status: Literal["validating_files", "queued", "running", 3 more]

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

    • "validating_files"

    • "queued"

    • "running"

    • "succeeded"

    • "failed"

    • "cancelled"

  • trained_tokens: Optional[int]

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

  • training_file: str

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

  • validation_file: Optional[str]

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

  • estimated_finish: Optional[int]

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

  • integrations: Optional[List[FineTuningJobWandbIntegrationObject]]

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

    • type: Literal["wandb"]

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

      • "wandb"

    • wandb: FineTuningJobWandbIntegration

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

      • project: str

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

      • entity: Optional[str]

        The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

      • name: Optional[str]

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

      • tags: Optional[List[str]]

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

  • metadata: Optional[Metadata]

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

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

  • method: Optional[Method]

    The method used for fine-tuning.

    • type: Literal["supervised", "dpo", "reinforcement"]

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

      • "supervised"

      • "dpo"

      • "reinforcement"

    • dpo: Optional[DpoMethod]

      Configuration for the DPO fine-tuning method.

      • hyperparameters: Optional[DpoHyperparameters]

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • beta: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

    • reinforcement: Optional[ReinforcementMethod]

      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.

          • input: str

            The input text. This may include template strings.

          • name: str

            The name of the grader.

          • operation: Literal["eq", "ne", "like", "ilike"]

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

            • "eq"

            • "ne"

            • "like"

            • "ilike"

          • reference: str

            The reference text. This may include template strings.

          • type: Literal["string_check"]

            The object type, which is always string_check.

            • "string_check"

        • class TextSimilarityGrader: …

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: Literal["cosine", "fuzzy_match", "bleu", 8 more]

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

            • "cosine"

            • "fuzzy_match"

            • "bleu"

            • "gleu"

            • "meteor"

            • "rouge_1"

            • "rouge_2"

            • "rouge_3"

            • "rouge_4"

            • "rouge_5"

            • "rouge_l"

          • input: str

            The text being graded.

          • name: str

            The name of the grader.

          • reference: str

            The text being graded against.

          • type: Literal["text_similarity"]

            The type of grader.

            • "text_similarity"

        • class PythonGrader: …

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

          • name: str

            The name of the grader.

          • source: str

            The source code of the python script.

          • type: Literal["python"]

            The object type, which is always python.

            • "python"

          • image_tag: Optional[str]

            The image tag to use for the python script.

        • class ScoreModelGrader: …

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

          • input: List[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: InputContent

              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.

              • str

                A text input to the model.

              • class ResponseInputText: …

                A text input to the model.

                • text: str

                  The text input to the model.

                • type: Literal["input_text"]

                  The type of the input item. Always input_text.

                  • "input_text"

                • prompt_cache_breakpoint: Optional[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.

                  • mode: Literal["explicit"]

                    The breakpoint mode. Always explicit.

                    • "explicit"

              • class InputContentOutputText: …

                A text output from the model.

                • text: str

                  The text output from the model.

                • type: Literal["output_text"]

                  The type of the output text. Always output_text.

                  • "output_text"

              • class InputContentInputImage: …

                An image input block used within EvalItem content arrays.

                • image_url: str

                  The URL of the image input.

                • type: Literal["input_image"]

                  The type of the image input. Always input_image.

                  • "input_image"

                • detail: Optional[str]

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

              • class ResponseInputAudio: …

                An audio input to the model.

                • input_audio: InputAudio

                  • data: str

                    Base64-encoded audio data.

                  • format: Literal["mp3", "wav"]

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

                    • "mp3"

                    • "wav"

                • type: Literal["input_audio"]

                  The type of the input item. Always input_audio.

                  • "input_audio"

              • List[GraderInputItem]

                • str

                  A text input to the model.

                • class ResponseInputText: …

                  A text input to the model.

                • class GraderInputItemOutputText: …

                  A text output from the model.

                  • text: str

                    The text output from the model.

                  • type: Literal["output_text"]

                    The type of the output text. Always output_text.

                    • "output_text"

                • class GraderInputItemInputImage: …

                  An image input block used within EvalItem content arrays.

                  • image_url: str

                    The URL of the image input.

                  • type: Literal["input_image"]

                    The type of the image input. Always input_image.

                    • "input_image"

                  • detail: Optional[str]

                    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: Literal["user", "assistant", "system", "developer"]

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

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: Optional[Literal["message"]]

              The type of the message input. Always message.

              • "message"

          • model: str

            The model to use for the evaluation.

          • name: str

            The name of the grader.

          • type: Literal["score_model"]

            The object type, which is always score_model.

            • "score_model"

          • range: Optional[List[float]]

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

          • sampling_params: Optional[SamplingParams]

            The sampling parameters for the model.

            • max_completions_tokens: Optional[int]

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

            • reasoning_effort: Optional[ReasoningEffort]

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

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

              • "max"

            • seed: Optional[int]

              A seed value to initialize the randomness, during sampling.

            • temperature: Optional[float]

              A higher temperature increases randomness in the outputs.

            • top_p: Optional[float]

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

        • class MultiGrader: …

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

          • calculate_output: str

            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.

              • input: List[Input]

                • content: InputContent

                  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.

                  • str

                    A text input to the model.

                  • class ResponseInputText: …

                    A text input to the model.

                  • class InputContentOutputText: …

                    A text output from the model.

                    • text: str

                      The text output from the model.

                    • type: Literal["output_text"]

                      The type of the output text. Always output_text.

                      • "output_text"

                  • class InputContentInputImage: …

                    An image input block used within EvalItem content arrays.

                    • image_url: str

                      The URL of the image input.

                    • type: Literal["input_image"]

                      The type of the image input. Always input_image.

                      • "input_image"

                    • detail: Optional[str]

                      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[GraderInputItem]

                    • str

                      A text input to the model.

                    • class ResponseInputText: …

                      A text input to the model.

                    • class GraderInputItemOutputText: …

                      A text output from the model.

                    • class GraderInputItemInputImage: …

                      An image input block used within EvalItem content arrays.

                    • class ResponseInputAudio: …

                      An audio input to the model.

                • role: Literal["user", "assistant", "system", "developer"]

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

                  • "user"

                  • "assistant"

                  • "system"

                  • "developer"

                • type: Optional[Literal["message"]]

                  The type of the message input. Always message.

                  • "message"

              • labels: List[str]

                The labels to assign to each item in the evaluation.

              • model: str

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

              • name: str

                The name of the grader.

              • passing_labels: List[str]

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

              • type: Literal["label_model"]

                The object type, which is always label_model.

                • "label_model"

          • name: str

            The name of the grader.

          • type: Literal["multi"]

            The object type, which is always multi.

            • "multi"

      • hyperparameters: Optional[ReinforcementHyperparameters]

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • compute_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • eval_interval: Optional[Union[Literal["auto"], int, null]]

          The number of training steps between evaluation runs.

          • Literal["auto"]

            • "auto"

          • int

        • eval_samples: Optional[Union[Literal["auto"], int, null]]

          Number of evaluation samples to generate per training step.

          • Literal["auto"]

            • "auto"

          • int

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • reasoning_effort: Optional[Literal["default", "low", "medium", "high"]]

          Level of reasoning effort.

          • "default"

          • "low"

          • "medium"

          • "high"

    • supervised: Optional[SupervisedMethod]

      Configuration for the supervised fine-tuning method.

      • hyperparameters: Optional[SupervisedHyperparameters]

        The hyperparameters used for the fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
fine_tuning_job = client.fine_tuning.jobs.retrieve(
    "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
)
print(fine_tuning_job.id)

Response

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

Example

from openai import OpenAI
client = OpenAI()

client.fine_tuning.jobs.retrieve("ftjob-abc123")

Response

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "davinci-002",
  "created_at": 1692661014,
  "finished_at": 1692661190,
  "fine_tuned_model": "ft:davinci-002:my-org:custom_suffix:7q8mpxmy",
  "organization_id": "org-123",
  "result_files": [
      "file-abc123"
  ],
  "status": "succeeded",
  "validation_file": null,
  "training_file": "file-abc123",
  "hyperparameters": {
      "n_epochs": 4,
      "batch_size": 1,
      "learning_rate_multiplier": 1.0
  },
  "trained_tokens": 5768,
  "integrations": [],
  "seed": 0,
  "estimated_finish": 0,
  "method": {
    "type": "supervised",
    "supervised": {
      "hyperparameters": {
        "n_epochs": 4,
        "batch_size": 1,
        "learning_rate_multiplier": 1.0
      }
    }
  }
}

List fine-tuning events

fine_tuning.jobs.list_events(strfine_tuning_job_id, JobListEventsParams**kwargs) -> SyncCursorPage[FineTuningJobEvent]

get /fine_tuning/jobs/{fine_tuning_job_id}/events

Get status updates for a fine-tuning job.

Parameters

• fine_tuning_job_id: str

• after: Optional[str]

  Identifier for the last event from the previous pagination request.

• limit: Optional[int]

  Number of events to retrieve.

Returns

• class FineTuningJobEvent: …

  Fine-tuning job event object

  • id: str

    The object identifier.

  • created_at: int

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

  • level: Literal["info", "warn", "error"]

    The log level of the event.

    • "info"

    • "warn"

    • "error"

  • message: str

    The message of the event.

  • object: Literal["fine_tuning.job.event"]

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

    • "fine_tuning.job.event"

  • data: Optional[object]

    The data associated with the event.

  • type: Optional[Literal["message", "metrics"]]

    The type of event.

    • "message"

    • "metrics"

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
page = client.fine_tuning.jobs.list_events(
    fine_tuning_job_id="ft-AF1WoRqd3aJAHsqc9NY7iL8F",
)
page = page.data[0]
print(page.id)

Response

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

Example

from openai import OpenAI
client = OpenAI()

client.fine_tuning.jobs.list_events(
  fine_tuning_job_id="ftjob-abc123",
  limit=2
)

Response

{
  "object": "list",
  "data": [
    {
      "object": "fine_tuning.job.event",
      "id": "ft-event-ddTJfwuMVpfLXseO0Am0Gqjm",
      "created_at": 1721764800,
      "level": "info",
      "message": "Fine tuning job successfully completed",
      "data": null,
      "type": "message"
    },
    {
      "object": "fine_tuning.job.event",
      "id": "ft-event-tyiGuB72evQncpH87xe505Sv",
      "created_at": 1721764800,
      "level": "info",
      "message": "New fine-tuned model created: ft:gpt-4o-mini:openai::7p4lURel",
      "data": null,
      "type": "message"
    }
  ],
  "has_more": true
}

Cancel fine-tuning

fine_tuning.jobs.cancel(strfine_tuning_job_id) -> FineTuningJob

post /fine_tuning/jobs/{fine_tuning_job_id}/cancel

Immediately cancel a fine-tune job.

Parameters

• fine_tuning_job_id: str

Returns

• class FineTuningJob: …

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

  • id: str

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

  • created_at: int

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

  • error: Optional[Error]

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

    • code: str

      A machine-readable error code.

    • message: str

      A human-readable error message.

    • param: Optional[str]

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

  • fine_tuned_model: Optional[str]

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

  • finished_at: Optional[int]

    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.

    • batch_size: Optional[Union[Literal["auto"], int, null]]

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

      • Literal["auto"]

        • "auto"

      • int

    • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

      • Literal["auto"]

        • "auto"

      • float

    • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

      • Literal["auto"]

        • "auto"

      • int

  • model: str

    The base model that is being fine-tuned.

  • object: Literal["fine_tuning.job"]

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

    • "fine_tuning.job"

  • organization_id: str

    The organization that owns the fine-tuning job.

  • result_files: List[str]

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

  • seed: int

    The seed used for the fine-tuning job.

  • status: Literal["validating_files", "queued", "running", 3 more]

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

    • "validating_files"

    • "queued"

    • "running"

    • "succeeded"

    • "failed"

    • "cancelled"

  • trained_tokens: Optional[int]

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

  • training_file: str

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

  • validation_file: Optional[str]

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

  • estimated_finish: Optional[int]

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

  • integrations: Optional[List[FineTuningJobWandbIntegrationObject]]

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

    • type: Literal["wandb"]

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

      • "wandb"

    • wandb: FineTuningJobWandbIntegration

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

      • project: str

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

      • entity: Optional[str]

        The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

      • name: Optional[str]

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

      • tags: Optional[List[str]]

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

  • metadata: Optional[Metadata]

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

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

  • method: Optional[Method]

    The method used for fine-tuning.

    • type: Literal["supervised", "dpo", "reinforcement"]

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

      • "supervised"

      • "dpo"

      • "reinforcement"

    • dpo: Optional[DpoMethod]

      Configuration for the DPO fine-tuning method.

      • hyperparameters: Optional[DpoHyperparameters]

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • beta: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

    • reinforcement: Optional[ReinforcementMethod]

      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.

          • input: str

            The input text. This may include template strings.

          • name: str

            The name of the grader.

          • operation: Literal["eq", "ne", "like", "ilike"]

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

            • "eq"

            • "ne"

            • "like"

            • "ilike"

          • reference: str

            The reference text. This may include template strings.

          • type: Literal["string_check"]

            The object type, which is always string_check.

            • "string_check"

        • class TextSimilarityGrader: …

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: Literal["cosine", "fuzzy_match", "bleu", 8 more]

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

            • "cosine"

            • "fuzzy_match"

            • "bleu"

            • "gleu"

            • "meteor"

            • "rouge_1"

            • "rouge_2"

            • "rouge_3"

            • "rouge_4"

            • "rouge_5"

            • "rouge_l"

          • input: str

            The text being graded.

          • name: str

            The name of the grader.

          • reference: str

            The text being graded against.

          • type: Literal["text_similarity"]

            The type of grader.

            • "text_similarity"

        • class PythonGrader: …

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

          • name: str

            The name of the grader.

          • source: str

            The source code of the python script.

          • type: Literal["python"]

            The object type, which is always python.

            • "python"

          • image_tag: Optional[str]

            The image tag to use for the python script.

        • class ScoreModelGrader: …

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

          • input: List[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: InputContent

              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.

              • str

                A text input to the model.

              • class ResponseInputText: …

                A text input to the model.

                • text: str

                  The text input to the model.

                • type: Literal["input_text"]

                  The type of the input item. Always input_text.

                  • "input_text"

                • prompt_cache_breakpoint: Optional[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.

                  • mode: Literal["explicit"]

                    The breakpoint mode. Always explicit.

                    • "explicit"

              • class InputContentOutputText: …

                A text output from the model.

                • text: str

                  The text output from the model.

                • type: Literal["output_text"]

                  The type of the output text. Always output_text.

                  • "output_text"

              • class InputContentInputImage: …

                An image input block used within EvalItem content arrays.

                • image_url: str

                  The URL of the image input.

                • type: Literal["input_image"]

                  The type of the image input. Always input_image.

                  • "input_image"

                • detail: Optional[str]

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

              • class ResponseInputAudio: …

                An audio input to the model.

                • input_audio: InputAudio

                  • data: str

                    Base64-encoded audio data.

                  • format: Literal["mp3", "wav"]

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

                    • "mp3"

                    • "wav"

                • type: Literal["input_audio"]

                  The type of the input item. Always input_audio.

                  • "input_audio"

              • List[GraderInputItem]

                • str

                  A text input to the model.

                • class ResponseInputText: …

                  A text input to the model.

                • class GraderInputItemOutputText: …

                  A text output from the model.

                  • text: str

                    The text output from the model.

                  • type: Literal["output_text"]

                    The type of the output text. Always output_text.

                    • "output_text"

                • class GraderInputItemInputImage: …

                  An image input block used within EvalItem content arrays.

                  • image_url: str

                    The URL of the image input.

                  • type: Literal["input_image"]

                    The type of the image input. Always input_image.

                    • "input_image"

                  • detail: Optional[str]

                    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: Literal["user", "assistant", "system", "developer"]

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

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: Optional[Literal["message"]]

              The type of the message input. Always message.

              • "message"

          • model: str

            The model to use for the evaluation.

          • name: str

            The name of the grader.

          • type: Literal["score_model"]

            The object type, which is always score_model.

            • "score_model"

          • range: Optional[List[float]]

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

          • sampling_params: Optional[SamplingParams]

            The sampling parameters for the model.

            • max_completions_tokens: Optional[int]

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

            • reasoning_effort: Optional[ReasoningEffort]

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

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

              • "max"

            • seed: Optional[int]

              A seed value to initialize the randomness, during sampling.

            • temperature: Optional[float]

              A higher temperature increases randomness in the outputs.

            • top_p: Optional[float]

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

        • class MultiGrader: …

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

          • calculate_output: str

            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.

              • input: List[Input]

                • content: InputContent

                  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.

                  • str

                    A text input to the model.

                  • class ResponseInputText: …

                    A text input to the model.

                  • class InputContentOutputText: …

                    A text output from the model.

                    • text: str

                      The text output from the model.

                    • type: Literal["output_text"]

                      The type of the output text. Always output_text.

                      • "output_text"

                  • class InputContentInputImage: …

                    An image input block used within EvalItem content arrays.

                    • image_url: str

                      The URL of the image input.

                    • type: Literal["input_image"]

                      The type of the image input. Always input_image.

                      • "input_image"

                    • detail: Optional[str]

                      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[GraderInputItem]

                    • str

                      A text input to the model.

                    • class ResponseInputText: …

                      A text input to the model.

                    • class GraderInputItemOutputText: …

                      A text output from the model.

                    • class GraderInputItemInputImage: …

                      An image input block used within EvalItem content arrays.

                    • class ResponseInputAudio: …

                      An audio input to the model.

                • role: Literal["user", "assistant", "system", "developer"]

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

                  • "user"

                  • "assistant"

                  • "system"

                  • "developer"

                • type: Optional[Literal["message"]]

                  The type of the message input. Always message.

                  • "message"

              • labels: List[str]

                The labels to assign to each item in the evaluation.

              • model: str

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

              • name: str

                The name of the grader.

              • passing_labels: List[str]

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

              • type: Literal["label_model"]

                The object type, which is always label_model.

                • "label_model"

          • name: str

            The name of the grader.

          • type: Literal["multi"]

            The object type, which is always multi.

            • "multi"

      • hyperparameters: Optional[ReinforcementHyperparameters]

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • compute_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • eval_interval: Optional[Union[Literal["auto"], int, null]]

          The number of training steps between evaluation runs.

          • Literal["auto"]

            • "auto"

          • int

        • eval_samples: Optional[Union[Literal["auto"], int, null]]

          Number of evaluation samples to generate per training step.

          • Literal["auto"]

            • "auto"

          • int

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • reasoning_effort: Optional[Literal["default", "low", "medium", "high"]]

          Level of reasoning effort.

          • "default"

          • "low"

          • "medium"

          • "high"

    • supervised: Optional[SupervisedMethod]

      Configuration for the supervised fine-tuning method.

      • hyperparameters: Optional[SupervisedHyperparameters]

        The hyperparameters used for the fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
fine_tuning_job = client.fine_tuning.jobs.cancel(
    "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
)
print(fine_tuning_job.id)

Response

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

Example

from openai import OpenAI
client = OpenAI()

client.fine_tuning.jobs.cancel("ftjob-abc123")

Response

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "gpt-4o-mini-2024-07-18",
  "created_at": 1721764800,
  "fine_tuned_model": null,
  "organization_id": "org-123",
  "result_files": [],
  "status": "cancelled",
  "validation_file": "file-abc123",
  "training_file": "file-abc123"
}

Pause fine-tuning

fine_tuning.jobs.pause(strfine_tuning_job_id) -> FineTuningJob

post /fine_tuning/jobs/{fine_tuning_job_id}/pause

Pause a fine-tune job.

Parameters

• fine_tuning_job_id: str

Returns

• class FineTuningJob: …

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

  • id: str

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

  • created_at: int

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

  • error: Optional[Error]

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

    • code: str

      A machine-readable error code.

    • message: str

      A human-readable error message.

    • param: Optional[str]

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

  • fine_tuned_model: Optional[str]

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

  • finished_at: Optional[int]

    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.

    • batch_size: Optional[Union[Literal["auto"], int, null]]

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

      • Literal["auto"]

        • "auto"

      • int

    • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

      • Literal["auto"]

        • "auto"

      • float

    • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

      • Literal["auto"]

        • "auto"

      • int

  • model: str

    The base model that is being fine-tuned.

  • object: Literal["fine_tuning.job"]

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

    • "fine_tuning.job"

  • organization_id: str

    The organization that owns the fine-tuning job.

  • result_files: List[str]

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

  • seed: int

    The seed used for the fine-tuning job.

  • status: Literal["validating_files", "queued", "running", 3 more]

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

    • "validating_files"

    • "queued"

    • "running"

    • "succeeded"

    • "failed"

    • "cancelled"

  • trained_tokens: Optional[int]

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

  • training_file: str

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

  • validation_file: Optional[str]

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

  • estimated_finish: Optional[int]

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

  • integrations: Optional[List[FineTuningJobWandbIntegrationObject]]

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

    • type: Literal["wandb"]

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

      • "wandb"

    • wandb: FineTuningJobWandbIntegration

      The settings for your integration with Weights and Biases. This payload specifies the project that metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags to your run, and set a default entity (team, username, etc) to be associated with your run.

      • project: str

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

      • entity: Optional[str]

        The entity to use for the run. This allows you to set the team or username of the WandB user that you would like associated with the run. If not set, the default entity for the registered WandB API key is used.

      • name: Optional[str]

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

      • tags: Optional[List[str]]

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

  • metadata: Optional[Metadata]

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

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

  • method: Optional[Method]

    The method used for fine-tuning.

    • type: Literal["supervised", "dpo", "reinforcement"]

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

      • "supervised"

      • "dpo"

      • "reinforcement"

    • dpo: Optional[DpoMethod]

      Configuration for the DPO fine-tuning method.

      • hyperparameters: Optional[DpoHyperparameters]

        The hyperparameters used for the DPO fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • beta: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

    • reinforcement: Optional[ReinforcementMethod]

      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.

          • input: str

            The input text. This may include template strings.

          • name: str

            The name of the grader.

          • operation: Literal["eq", "ne", "like", "ilike"]

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

            • "eq"

            • "ne"

            • "like"

            • "ilike"

          • reference: str

            The reference text. This may include template strings.

          • type: Literal["string_check"]

            The object type, which is always string_check.

            • "string_check"

        • class TextSimilarityGrader: …

          A TextSimilarityGrader object which grades text based on similarity metrics.

          • evaluation_metric: Literal["cosine", "fuzzy_match", "bleu", 8 more]

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

            • "cosine"

            • "fuzzy_match"

            • "bleu"

            • "gleu"

            • "meteor"

            • "rouge_1"

            • "rouge_2"

            • "rouge_3"

            • "rouge_4"

            • "rouge_5"

            • "rouge_l"

          • input: str

            The text being graded.

          • name: str

            The name of the grader.

          • reference: str

            The text being graded against.

          • type: Literal["text_similarity"]

            The type of grader.

            • "text_similarity"

        • class PythonGrader: …

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

          • name: str

            The name of the grader.

          • source: str

            The source code of the python script.

          • type: Literal["python"]

            The object type, which is always python.

            • "python"

          • image_tag: Optional[str]

            The image tag to use for the python script.

        • class ScoreModelGrader: …

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

          • input: List[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: InputContent

              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.

              • str

                A text input to the model.

              • class ResponseInputText: …

                A text input to the model.

                • text: str

                  The text input to the model.

                • type: Literal["input_text"]

                  The type of the input item. Always input_text.

                  • "input_text"

                • prompt_cache_breakpoint: Optional[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.

                  • mode: Literal["explicit"]

                    The breakpoint mode. Always explicit.

                    • "explicit"

              • class InputContentOutputText: …

                A text output from the model.

                • text: str

                  The text output from the model.

                • type: Literal["output_text"]

                  The type of the output text. Always output_text.

                  • "output_text"

              • class InputContentInputImage: …

                An image input block used within EvalItem content arrays.

                • image_url: str

                  The URL of the image input.

                • type: Literal["input_image"]

                  The type of the image input. Always input_image.

                  • "input_image"

                • detail: Optional[str]

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

              • class ResponseInputAudio: …

                An audio input to the model.

                • input_audio: InputAudio

                  • data: str

                    Base64-encoded audio data.

                  • format: Literal["mp3", "wav"]

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

                    • "mp3"

                    • "wav"

                • type: Literal["input_audio"]

                  The type of the input item. Always input_audio.

                  • "input_audio"

              • List[GraderInputItem]

                • str

                  A text input to the model.

                • class ResponseInputText: …

                  A text input to the model.

                • class GraderInputItemOutputText: …

                  A text output from the model.

                  • text: str

                    The text output from the model.

                  • type: Literal["output_text"]

                    The type of the output text. Always output_text.

                    • "output_text"

                • class GraderInputItemInputImage: …

                  An image input block used within EvalItem content arrays.

                  • image_url: str

                    The URL of the image input.

                  • type: Literal["input_image"]

                    The type of the image input. Always input_image.

                    • "input_image"

                  • detail: Optional[str]

                    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: Literal["user", "assistant", "system", "developer"]

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

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: Optional[Literal["message"]]

              The type of the message input. Always message.

              • "message"

          • model: str

            The model to use for the evaluation.

          • name: str

            The name of the grader.

          • type: Literal["score_model"]

            The object type, which is always score_model.

            • "score_model"

          • range: Optional[List[float]]

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

          • sampling_params: Optional[SamplingParams]

            The sampling parameters for the model.

            • max_completions_tokens: Optional[int]

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

            • reasoning_effort: Optional[ReasoningEffort]

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

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

              • "max"

            • seed: Optional[int]

              A seed value to initialize the randomness, during sampling.

            • temperature: Optional[float]

              A higher temperature increases randomness in the outputs.

            • top_p: Optional[float]

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

        • class MultiGrader: …

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

          • calculate_output: str

            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.

              • input: List[Input]

                • content: InputContent

                  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.

                  • str

                    A text input to the model.

                  • class ResponseInputText: …

                    A text input to the model.

                  • class InputContentOutputText: …

                    A text output from the model.

                    • text: str

                      The text output from the model.

                    • type: Literal["output_text"]

                      The type of the output text. Always output_text.

                      • "output_text"

                  • class InputContentInputImage: …

                    An image input block used within EvalItem content arrays.

                    • image_url: str

                      The URL of the image input.

                    • type: Literal["input_image"]

                      The type of the image input. Always input_image.

                      • "input_image"

                    • detail: Optional[str]

                      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[GraderInputItem]

                    • str

                      A text input to the model.

                    • class ResponseInputText: …

                      A text input to the model.

                    • class GraderInputItemOutputText: …

                      A text output from the model.

                    • class GraderInputItemInputImage: …

                      An image input block used within EvalItem content arrays.

                    • class ResponseInputAudio: …

                      An audio input to the model.

                • role: Literal["user", "assistant", "system", "developer"]

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

                  • "user"

                  • "assistant"

                  • "system"

                  • "developer"

                • type: Optional[Literal["message"]]

                  The type of the message input. Always message.

                  • "message"

              • labels: List[str]

                The labels to assign to each item in the evaluation.

              • model: str

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

              • name: str

                The name of the grader.

              • passing_labels: List[str]

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

              • type: Literal["label_model"]

                The object type, which is always label_model.

                • "label_model"

          • name: str

            The name of the grader.

          • type: Literal["multi"]

            The object type, which is always multi.

            • "multi"

      • hyperparameters: Optional[ReinforcementHyperparameters]

        The hyperparameters used for the reinforcement fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • compute_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • eval_interval: Optional[Union[Literal["auto"], int, null]]

          The number of training steps between evaluation runs.

          • Literal["auto"]

            • "auto"

          • int

        • eval_samples: Optional[Union[Literal["auto"], int, null]]

          Number of evaluation samples to generate per training step.

          • Literal["auto"]

            • "auto"

          • int

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • reasoning_effort: Optional[Literal["default", "low", "medium", "high"]]

          Level of reasoning effort.

          • "default"

          • "low"

          • "medium"

          • "high"

    • supervised: Optional[SupervisedMethod]

      Configuration for the supervised fine-tuning method.

      • hyperparameters: Optional[SupervisedHyperparameters]

        The hyperparameters used for the fine-tuning job.

        • batch_size: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

        • learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]

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

          • Literal["auto"]

            • "auto"

          • float

        • n_epochs: Optional[Union[Literal["auto"], int, null]]

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

          • Literal["auto"]

            • "auto"

          • int

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
fine_tuning_job = client.fine_tuning.jobs.pause(
    "ft-AF1WoRqd3aJAHsqc9NY7iL8F",
)
print(fine_tuning_job.id)

Response

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

Example

from openai import OpenAI
client = OpenAI()

client.fine_tuning.jobs.pause("ftjob-abc123")

Response

{
  "object": "fine_tuning.job",
  "id": "ftjob-abc123",
  "model": "gpt-4o-mini-2024-07-18",
  "created_at": 1721764800,
  "fine_tuned_model": null,
  "organization_id": "org-123",
  "result_files": [],
  "status": "paused",
  "validation_file": "file-abc123",
  "training_file": "file-abc123"
}