# Methods ## Domain Types ### Dpo Hyperparameters - `class 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` ### Dpo Method - `class 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 Hyperparameters - `class 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"` ### Reinforcement Method - `class 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"` - `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](https://platform.openai.com/docs/guides/reasoning). Currently supported values are `none`, `minimal`, `low`, `medium`, `high`, and `xhigh`. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. - `gpt-5.1` defaults to `none`, which does not perform reasoning. The supported reasoning values for `gpt-5.1` are `none`, `low`, `medium`, and `high`. Tool calls are supported for all reasoning values in gpt-5.1. - All models before `gpt-5.1` default to `medium` reasoning effort, and do not support `none`. - The `gpt-5-pro` model defaults to (and only supports) `high` reasoning effort. - `xhigh` is supported for all models after `gpt-5.1-codex-max`. - `"none"` - `"minimal"` - `"low"` - `"medium"` - `"high"` - `"xhigh"` - `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 Hyperparameters - `class 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` ### Supervised Method - `class 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`