diff --git a/en/python/resources/fine_tuning/index.md b/en/python/resources/fine_tuning/index.md deleted file mode 100644 index 8009e63..0000000 --- a/en/python/resources/fine_tuning/index.md +++ /dev/null @@ -1,11708 +0,0 @@ -# Fine Tuning - -# 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` - -# 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](https://platform.openai.com/docs/guides/model-optimization) - -### 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](https://platform.openai.com/docs/guides/fine-tuning#which-models-can-be-fine-tuned). - - - `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](https://platform.openai.com/docs/guides/fine-tuning#which-models-can-be-fine-tuned). - - - `"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](https://platform.openai.com/docs/api-reference/files/create) 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](https://platform.openai.com/docs/api-reference/fine-tuning/chat-input), [completions](https://platform.openai.com/docs/api-reference/fine-tuning/completions-input) format, or if the fine-tuning method uses the [preference](https://platform.openai.com/docs/api-reference/fine-tuning/preference-input) format. - - See the [fine-tuning guide](https://platform.openai.com/docs/guides/model-optimization) 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"` - - - `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: 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](https://platform.openai.com/docs/guides/model-optimization) 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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `validation_file: Optional[str]` - - The file ID used for validation. You can retrieve the validation results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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"` - - - `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: 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 - -```python -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 - -```json -{ - "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 - -```python -from openai import OpenAI -client = OpenAI() - -client.fine_tuning.jobs.create( - training_file="file-abc123", - model="gpt-4o-mini" -) -``` - -#### Response - -```json -{ - "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 - -```python -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 - -```json -{ - "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 - -```python -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 - -```json -{ - "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 - -```python -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 - -```json -{ - "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 - -```python -from openai import OpenAI -client = OpenAI() - -client.fine_tuning.jobs.create( - training_file="file-abc123", - validation_file="file-def456", - model="gpt-4o-mini" -) -``` - -#### Response - -```json -{ - "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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `validation_file: Optional[str]` - - The file ID used for validation. You can retrieve the validation results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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"` - - - `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: 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 - -```python -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 - -```json -{ - "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 - -```python -from openai import OpenAI -client = OpenAI() - -client.fine_tuning.jobs.list() -``` - -#### Response - -```json -{ - "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](https://platform.openai.com/docs/guides/model-optimization) - -### 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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `validation_file: Optional[str]` - - The file ID used for validation. You can retrieve the validation results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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"` - - - `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: 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 - -```python -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 - -```json -{ - "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 - -```python -from openai import OpenAI -client = OpenAI() - -client.fine_tuning.jobs.retrieve("ftjob-abc123") -``` - -#### Response - -```json -{ - "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 - -```python -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 - -```json -{ - "data": [ - { - "id": "id", - "created_at": 0, - "level": "info", - "message": "message", - "object": "fine_tuning.job.event", - "data": {}, - "type": "message" - } - ], - "has_more": true, - "object": "list" -} -``` - -### Example - -```python -from openai import OpenAI -client = OpenAI() - -client.fine_tuning.jobs.list_events( - fine_tuning_job_id="ftjob-abc123", - limit=2 -) -``` - -#### Response - -```json -{ - "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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `validation_file: Optional[str]` - - The file ID used for validation. You can retrieve the validation results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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"` - - - `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: 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 - -```python -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 - -```json -{ - "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 - -```python -from openai import OpenAI -client = OpenAI() - -client.fine_tuning.jobs.cancel("ftjob-abc123") -``` - -#### Response - -```json -{ - "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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `validation_file: Optional[str]` - - The file ID used for validation. You can retrieve the validation results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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"` - - - `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: 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 - -```python -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 - -```json -{ - "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 - -```python -from openai import OpenAI -client = OpenAI() - -client.fine_tuning.jobs.pause("ftjob-abc123") -``` - -#### Response - -```json -{ - "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" -} -``` - -## Resume fine-tuning - -`fine_tuning.jobs.resume(strfine_tuning_job_id) -> FineTuningJob` - -**post** `/fine_tuning/jobs/{fine_tuning_job_id}/resume` - -Resume 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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `validation_file: Optional[str]` - - The file ID used for validation. You can retrieve the validation results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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"` - - - `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: 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 - -```python -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.resume( - "ft-AF1WoRqd3aJAHsqc9NY7iL8F", -) -print(fine_tuning_job.id) -``` - -#### Response - -```json -{ - "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 - -```python -from openai import OpenAI -client = OpenAI() - -client.fine_tuning.jobs.resume("ftjob-abc123") -``` - -#### Response - -```json -{ - "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" -} -``` - -## Domain Types - -### Fine Tuning Job - -- `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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `validation_file: Optional[str]` - - The file ID used for validation. You can retrieve the validation results with the [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents). - - - `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"` - - - `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: 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` - -### Fine Tuning Job Event - -- `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"` - -### Fine Tuning Job Wandb Integration - -- `class FineTuningJobWandbIntegration: …` - - The settings for your integration with Weights and Biases. This payload specifies the project that - metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags - to your run, and set a default entity (team, username, etc) to be associated with your run. - - - `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}". - -### Fine Tuning Job Wandb Integration Object - -- `class FineTuningJobWandbIntegrationObject: …` - - - `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}". - -# Checkpoints - -## List fine-tuning checkpoints - -`fine_tuning.jobs.checkpoints.list(strfine_tuning_job_id, CheckpointListParams**kwargs) -> SyncCursorPage[FineTuningJobCheckpoint]` - -**get** `/fine_tuning/jobs/{fine_tuning_job_id}/checkpoints` - -List checkpoints for a fine-tuning job. - -### Parameters - -- `fine_tuning_job_id: str` - -- `after: Optional[str]` - - Identifier for the last checkpoint ID from the previous pagination request. - -- `limit: Optional[int]` - - Number of checkpoints to retrieve. - -### Returns - -- `class FineTuningJobCheckpoint: …` - - The `fine_tuning.job.checkpoint` object represents a model checkpoint for a fine-tuning job that is ready to use. - - - `id: str` - - The checkpoint identifier, which can be referenced in the API endpoints. - - - `created_at: int` - - The Unix timestamp (in seconds) for when the checkpoint was created. - - - `fine_tuned_model_checkpoint: str` - - The name of the fine-tuned checkpoint model that is created. - - - `fine_tuning_job_id: str` - - The name of the fine-tuning job that this checkpoint was created from. - - - `metrics: Metrics` - - Metrics at the step number during the fine-tuning job. - - - `full_valid_loss: Optional[float]` - - - `full_valid_mean_token_accuracy: Optional[float]` - - - `step: Optional[float]` - - - `train_loss: Optional[float]` - - - `train_mean_token_accuracy: Optional[float]` - - - `valid_loss: Optional[float]` - - - `valid_mean_token_accuracy: Optional[float]` - - - `object: Literal["fine_tuning.job.checkpoint"]` - - The object type, which is always "fine_tuning.job.checkpoint". - - - `"fine_tuning.job.checkpoint"` - - - `step_number: int` - - The step number that the checkpoint was created at. - -### Example - -```python -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.checkpoints.list( - fine_tuning_job_id="ft-AF1WoRqd3aJAHsqc9NY7iL8F", -) -page = page.data[0] -print(page.id) -``` - -#### Response - -```json -{ - "data": [ - { - "id": "id", - "created_at": 0, - "fine_tuned_model_checkpoint": "fine_tuned_model_checkpoint", - "fine_tuning_job_id": "fine_tuning_job_id", - "metrics": { - "full_valid_loss": 0, - "full_valid_mean_token_accuracy": 0, - "step": 0, - "train_loss": 0, - "train_mean_token_accuracy": 0, - "valid_loss": 0, - "valid_mean_token_accuracy": 0 - }, - "object": "fine_tuning.job.checkpoint", - "step_number": 0 - } - ], - "has_more": true, - "object": "list", - "first_id": "first_id", - "last_id": "last_id" -} -``` - -## Domain Types - -### Fine Tuning Job Checkpoint - -- `class FineTuningJobCheckpoint: …` - - The `fine_tuning.job.checkpoint` object represents a model checkpoint for a fine-tuning job that is ready to use. - - - `id: str` - - The checkpoint identifier, which can be referenced in the API endpoints. - - - `created_at: int` - - The Unix timestamp (in seconds) for when the checkpoint was created. - - - `fine_tuned_model_checkpoint: str` - - The name of the fine-tuned checkpoint model that is created. - - - `fine_tuning_job_id: str` - - The name of the fine-tuning job that this checkpoint was created from. - - - `metrics: Metrics` - - Metrics at the step number during the fine-tuning job. - - - `full_valid_loss: Optional[float]` - - - `full_valid_mean_token_accuracy: Optional[float]` - - - `step: Optional[float]` - - - `train_loss: Optional[float]` - - - `train_mean_token_accuracy: Optional[float]` - - - `valid_loss: Optional[float]` - - - `valid_mean_token_accuracy: Optional[float]` - - - `object: Literal["fine_tuning.job.checkpoint"]` - - The object type, which is always "fine_tuning.job.checkpoint". - - - `"fine_tuning.job.checkpoint"` - - - `step_number: int` - - The step number that the checkpoint was created at. - -# Checkpoints - -# Permissions - -## List checkpoint permissions - -`fine_tuning.checkpoints.permissions.retrieve(strfine_tuned_model_checkpoint, PermissionRetrieveParams**kwargs) -> PermissionRetrieveResponse` - -**get** `/fine_tuning/checkpoints/{fine_tuned_model_checkpoint}/permissions` - -**NOTE:** This endpoint requires an [admin API key](../admin-api-keys). - -Organization owners can use this endpoint to view all permissions for a fine-tuned model checkpoint. - -### Parameters - -- `fine_tuned_model_checkpoint: str` - -- `after: Optional[str]` - - Identifier for the last permission ID from the previous pagination request. - -- `limit: Optional[int]` - - Number of permissions to retrieve. - -- `order: Optional[Literal["ascending", "descending"]]` - - The order in which to retrieve permissions. - - - `"ascending"` - - - `"descending"` - -- `project_id: Optional[str]` - - The ID of the project to get permissions for. - -### Returns - -- `class PermissionRetrieveResponse: …` - - - `data: List[Data]` - - - `id: str` - - The permission identifier, which can be referenced in the API endpoints. - - - `created_at: int` - - The Unix timestamp (in seconds) for when the permission was created. - - - `object: Literal["checkpoint.permission"]` - - The object type, which is always "checkpoint.permission". - - - `"checkpoint.permission"` - - - `project_id: str` - - The project identifier that the permission is for. - - - `has_more: bool` - - - `object: Literal["list"]` - - - `"list"` - - - `first_id: Optional[str]` - - - `last_id: Optional[str]` - -### Example - -```python -import os -from openai import OpenAI - -client = OpenAI( - api_key=os.environ.get("OPENAI_API_KEY"), # This is the default and can be omitted -) -permission = client.fine_tuning.checkpoints.permissions.retrieve( - fine_tuned_model_checkpoint="ft-AF1WoRqd3aJAHsqc9NY7iL8F", -) -print(permission.first_id) -``` - -#### Response - -```json -{ - "data": [ - { - "id": "id", - "created_at": 0, - "object": "checkpoint.permission", - "project_id": "project_id" - } - ], - "has_more": true, - "object": "list", - "first_id": "first_id", - "last_id": "last_id" -} -``` - -## List checkpoint permissions - -`fine_tuning.checkpoints.permissions.list(strfine_tuned_model_checkpoint, PermissionListParams**kwargs) -> SyncConversationCursorPage[PermissionListResponse]` - -**get** `/fine_tuning/checkpoints/{fine_tuned_model_checkpoint}/permissions` - -**NOTE:** This endpoint requires an [admin API key](../admin-api-keys). - -Organization owners can use this endpoint to view all permissions for a fine-tuned model checkpoint. - -### Parameters - -- `fine_tuned_model_checkpoint: str` - -- `after: Optional[str]` - - Identifier for the last permission ID from the previous pagination request. - -- `limit: Optional[int]` - - Number of permissions to retrieve. - -- `order: Optional[Literal["ascending", "descending"]]` - - The order in which to retrieve permissions. - - - `"ascending"` - - - `"descending"` - -- `project_id: Optional[str]` - - The ID of the project to get permissions for. - -### Returns - -- `class PermissionListResponse: …` - - The `checkpoint.permission` object represents a permission for a fine-tuned model checkpoint. - - - `id: str` - - The permission identifier, which can be referenced in the API endpoints. - - - `created_at: int` - - The Unix timestamp (in seconds) for when the permission was created. - - - `object: Literal["checkpoint.permission"]` - - The object type, which is always "checkpoint.permission". - - - `"checkpoint.permission"` - - - `project_id: str` - - The project identifier that the permission is for. - -### Example - -```python -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.checkpoints.permissions.list( - fine_tuned_model_checkpoint="ft-AF1WoRqd3aJAHsqc9NY7iL8F", -) -page = page.data[0] -print(page.id) -``` - -#### Response - -```json -{ - "data": [ - { - "id": "id", - "created_at": 0, - "object": "checkpoint.permission", - "project_id": "project_id" - } - ], - "has_more": true, - "object": "list", - "first_id": "first_id", - "last_id": "last_id" -} -``` - -## Create checkpoint permissions - -`fine_tuning.checkpoints.permissions.create(strfine_tuned_model_checkpoint, PermissionCreateParams**kwargs) -> SyncPage[PermissionCreateResponse]` - -**post** `/fine_tuning/checkpoints/{fine_tuned_model_checkpoint}/permissions` - -**NOTE:** Calling this endpoint requires an [admin API key](../admin-api-keys). - -This enables organization owners to share fine-tuned models with other projects in their organization. - -### Parameters - -- `fine_tuned_model_checkpoint: str` - -- `project_ids: Sequence[str]` - - The project identifiers to grant access to. - -### Returns - -- `class PermissionCreateResponse: …` - - The `checkpoint.permission` object represents a permission for a fine-tuned model checkpoint. - - - `id: str` - - The permission identifier, which can be referenced in the API endpoints. - - - `created_at: int` - - The Unix timestamp (in seconds) for when the permission was created. - - - `object: Literal["checkpoint.permission"]` - - The object type, which is always "checkpoint.permission". - - - `"checkpoint.permission"` - - - `project_id: str` - - The project identifier that the permission is for. - -### Example - -```python -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.checkpoints.permissions.create( - fine_tuned_model_checkpoint="ft:gpt-4o-mini-2024-07-18:org:weather:B7R9VjQd", - project_ids=["string"], -) -page = page.data[0] -print(page.id) -``` - -#### Response - -```json -{ - "data": [ - { - "id": "id", - "created_at": 0, - "object": "checkpoint.permission", - "project_id": "project_id" - } - ], - "has_more": true, - "object": "list", - "first_id": "first_id", - "last_id": "last_id" -} -``` - -## Delete checkpoint permission - -`fine_tuning.checkpoints.permissions.delete(strpermission_id, PermissionDeleteParams**kwargs) -> PermissionDeleteResponse` - -**delete** `/fine_tuning/checkpoints/{fine_tuned_model_checkpoint}/permissions/{permission_id}` - -**NOTE:** This endpoint requires an [admin API key](../admin-api-keys). - -Organization owners can use this endpoint to delete a permission for a fine-tuned model checkpoint. - -### Parameters - -- `fine_tuned_model_checkpoint: str` - -- `permission_id: str` - -### Returns - -- `class PermissionDeleteResponse: …` - - - `id: str` - - The ID of the fine-tuned model checkpoint permission that was deleted. - - - `deleted: bool` - - Whether the fine-tuned model checkpoint permission was successfully deleted. - - - `object: Literal["checkpoint.permission"]` - - The object type, which is always "checkpoint.permission". - - - `"checkpoint.permission"` - -### Example - -```python -import os -from openai import OpenAI - -client = OpenAI( - api_key=os.environ.get("OPENAI_API_KEY"), # This is the default and can be omitted -) -permission = client.fine_tuning.checkpoints.permissions.delete( - permission_id="cp_zc4Q7MP6XxulcVzj4MZdwsAB", - fine_tuned_model_checkpoint="ft:gpt-4o-mini-2024-07-18:org:weather:B7R9VjQd", -) -print(permission.id) -``` - -#### Response - -```json -{ - "id": "id", - "deleted": true, - "object": "checkpoint.permission" -} -``` - -## Domain Types - -### Permission Retrieve Response - -- `class PermissionRetrieveResponse: …` - - - `data: List[Data]` - - - `id: str` - - The permission identifier, which can be referenced in the API endpoints. - - - `created_at: int` - - The Unix timestamp (in seconds) for when the permission was created. - - - `object: Literal["checkpoint.permission"]` - - The object type, which is always "checkpoint.permission". - - - `"checkpoint.permission"` - - - `project_id: str` - - The project identifier that the permission is for. - - - `has_more: bool` - - - `object: Literal["list"]` - - - `"list"` - - - `first_id: Optional[str]` - - - `last_id: Optional[str]` - -### Permission List Response - -- `class PermissionListResponse: …` - - The `checkpoint.permission` object represents a permission for a fine-tuned model checkpoint. - - - `id: str` - - The permission identifier, which can be referenced in the API endpoints. - - - `created_at: int` - - The Unix timestamp (in seconds) for when the permission was created. - - - `object: Literal["checkpoint.permission"]` - - The object type, which is always "checkpoint.permission". - - - `"checkpoint.permission"` - - - `project_id: str` - - The project identifier that the permission is for. - -### Permission Create Response - -- `class PermissionCreateResponse: …` - - The `checkpoint.permission` object represents a permission for a fine-tuned model checkpoint. - - - `id: str` - - The permission identifier, which can be referenced in the API endpoints. - - - `created_at: int` - - The Unix timestamp (in seconds) for when the permission was created. - - - `object: Literal["checkpoint.permission"]` - - The object type, which is always "checkpoint.permission". - - - `"checkpoint.permission"` - - - `project_id: str` - - The project identifier that the permission is for. - -### Permission Delete Response - -- `class PermissionDeleteResponse: …` - - - `id: str` - - The ID of the fine-tuned model checkpoint permission that was deleted. - - - `deleted: bool` - - Whether the fine-tuned model checkpoint permission was successfully deleted. - - - `object: Literal["checkpoint.permission"]` - - The object type, which is always "checkpoint.permission". - - - `"checkpoint.permission"` - -# Alpha - -# Graders - -## Run grader - -`fine_tuning.alpha.graders.run(GraderRunParams**kwargs) -> GraderRunResponse` - -**post** `/fine_tuning/alpha/graders/run` - -Run a grader. - -### Parameters - -- `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"` - -- `model_sample: str` - - The model sample to be evaluated. This value will be used to populate - the `sample` namespace. See [the guide](https://platform.openai.com/docs/guides/graders) for more details. - The `output_json` variable will be populated if the model sample is a - valid JSON string. - -- `item: Optional[object]` - - The dataset item provided to the grader. This will be used to populate - the `item` namespace. See [the guide](https://platform.openai.com/docs/guides/graders) for more details. - -### Returns - -- `class GraderRunResponse: …` - - - `metadata: Metadata` - - - `errors: MetadataErrors` - - - `formula_parse_error: bool` - - - `invalid_variable_error: bool` - - - `model_grader_parse_error: bool` - - - `model_grader_refusal_error: bool` - - - `model_grader_server_error: bool` - - - `model_grader_server_error_details: Optional[str]` - - - `other_error: bool` - - - `python_grader_runtime_error: bool` - - - `python_grader_runtime_error_details: Optional[str]` - - - `python_grader_server_error: bool` - - - `python_grader_server_error_type: Optional[str]` - - - `sample_parse_error: bool` - - - `truncated_observation_error: bool` - - - `unresponsive_reward_error: bool` - - - `execution_time: float` - - - `name: str` - - - `sampled_model_name: Optional[str]` - - - `scores: Dict[str, object]` - - - `token_usage: Optional[int]` - - - `type: str` - - - `model_grader_token_usage_per_model: Dict[str, object]` - - - `reward: float` - - - `sub_rewards: Dict[str, object]` - -### Example - -```python -import os -from openai import OpenAI - -client = OpenAI( - api_key=os.environ.get("OPENAI_API_KEY"), # This is the default and can be omitted -) -response = client.fine_tuning.alpha.graders.run( - grader={ - "input": "input", - "name": "name", - "operation": "eq", - "reference": "reference", - "type": "string_check", - }, - model_sample="model_sample", -) -print(response.metadata) -``` - -#### Response - -```json -{ - "metadata": { - "errors": { - "formula_parse_error": true, - "invalid_variable_error": true, - "model_grader_parse_error": true, - "model_grader_refusal_error": true, - "model_grader_server_error": true, - "model_grader_server_error_details": "model_grader_server_error_details", - "other_error": true, - "python_grader_runtime_error": true, - "python_grader_runtime_error_details": "python_grader_runtime_error_details", - "python_grader_server_error": true, - "python_grader_server_error_type": "python_grader_server_error_type", - "sample_parse_error": true, - "truncated_observation_error": true, - "unresponsive_reward_error": true - }, - "execution_time": 0, - "name": "name", - "sampled_model_name": "sampled_model_name", - "scores": { - "foo": "bar" - }, - "token_usage": 0, - "type": "type" - }, - "model_grader_token_usage_per_model": { - "foo": "bar" - }, - "reward": 0, - "sub_rewards": { - "foo": "bar" - } -} -``` - -### Score text alignment - -```python -from openai import OpenAI - -client = OpenAI() -result = client.fine_tuning.alpha.graders.run( - grader={ - "type": "score_model", - "name": "Example score model grader", - "input": [ - { - "role": "user", - "content": [ - { - "type": "input_text", - "text": "Score how close the reference answer is to the model answer on a 0-1 scale. Return only the score.\n\nReference answer: {{item.reference_answer}}\n\nModel answer: {{sample.output_text}}", - } - ], - } - ], - "model": "gpt-5-mini", - "sampling_params": {"temperature": 1, "top_p": 1, "seed": 42}, - }, - item={"reference_answer": "fuzzy wuzzy was a bear"}, - model_sample="fuzzy wuzzy was a bear", -) -print(result) -``` - -#### Response - -```json -{ - "reward": 1.0, - "metadata": { - "name": "Example score model grader", - "type": "score_model", - "errors": { - "formula_parse_error": false, - "sample_parse_error": false, - "truncated_observation_error": false, - "unresponsive_reward_error": false, - "invalid_variable_error": false, - "other_error": false, - "python_grader_server_error": false, - "python_grader_server_error_type": null, - "python_grader_runtime_error": false, - "python_grader_runtime_error_details": null, - "model_grader_server_error": false, - "model_grader_refusal_error": false, - "model_grader_parse_error": false, - "model_grader_server_error_details": null - }, - "execution_time": 4.365238428115845, - "scores": {}, - "token_usage": { - "prompt_tokens": 190, - "total_tokens": 324, - "completion_tokens": 134, - "cached_tokens": 0 - }, - "sampled_model_name": "gpt-4o-2024-08-06" - }, - "sub_rewards": {}, - "model_grader_token_usage_per_model": { - "gpt-4o-2024-08-06": { - "prompt_tokens": 190, - "total_tokens": 324, - "completion_tokens": 134, - "cached_tokens": 0 - } - } -} -``` - -## Validate grader - -`fine_tuning.alpha.graders.validate(GraderValidateParams**kwargs) -> GraderValidateResponse` - -**post** `/fine_tuning/alpha/graders/validate` - -Validate a grader. - -### Parameters - -- `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"` - -### Returns - -- `class GraderValidateResponse: …` - - - `grader: Optional[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"` - -### Example - -```python -import os -from openai import OpenAI - -client = OpenAI( - api_key=os.environ.get("OPENAI_API_KEY"), # This is the default and can be omitted -) -response = client.fine_tuning.alpha.graders.validate( - grader={ - "input": "input", - "name": "name", - "operation": "eq", - "reference": "reference", - "type": "string_check", - }, -) -print(response.grader) -``` - -#### Response - -```json -{ - "grader": { - "input": "input", - "name": "name", - "operation": "eq", - "reference": "reference", - "type": "string_check" - } -} -``` - -## Domain Types - -### Grader Run Response - -- `class GraderRunResponse: …` - - - `metadata: Metadata` - - - `errors: MetadataErrors` - - - `formula_parse_error: bool` - - - `invalid_variable_error: bool` - - - `model_grader_parse_error: bool` - - - `model_grader_refusal_error: bool` - - - `model_grader_server_error: bool` - - - `model_grader_server_error_details: Optional[str]` - - - `other_error: bool` - - - `python_grader_runtime_error: bool` - - - `python_grader_runtime_error_details: Optional[str]` - - - `python_grader_server_error: bool` - - - `python_grader_server_error_type: Optional[str]` - - - `sample_parse_error: bool` - - - `truncated_observation_error: bool` - - - `unresponsive_reward_error: bool` - - - `execution_time: float` - - - `name: str` - - - `sampled_model_name: Optional[str]` - - - `scores: Dict[str, object]` - - - `token_usage: Optional[int]` - - - `type: str` - - - `model_grader_token_usage_per_model: Dict[str, object]` - - - `reward: float` - - - `sub_rewards: Dict[str, object]` - -### Grader Validate Response - -- `class GraderValidateResponse: …` - - - `grader: Optional[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"`