diff --git a/en/resources/fine_tuning/subresources/jobs/index.md b/en/resources/fine_tuning/subresources/jobs/index.md new file mode 100644 index 0000000..250109c --- /dev/null +++ b/en/resources/fine_tuning/subresources/jobs/index.md @@ -0,0 +1,8121 @@ +# Jobs + +## Create fine-tuning job + +**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](/docs/guides/model-optimization) + +### Body Parameters + +- `model: string or "babbage-002" or "davinci-002" or "gpt-3.5-turbo" or "gpt-4o-mini"` + + The name of the model to fine-tune. You can select one of the + [supported models](/docs/guides/fine-tuning#which-models-can-be-fine-tuned). + + - `string` + + - `"babbage-002" or "davinci-002" or "gpt-3.5-turbo" or "gpt-4o-mini"` + + The name of the model to fine-tune. You can select one of the + [supported models](/docs/guides/fine-tuning#which-models-can-be-fine-tuned). + + - `"babbage-002"` + + - `"davinci-002"` + + - `"gpt-3.5-turbo"` + + - `"gpt-4o-mini"` + +- `training_file: string` + + The ID of an uploaded file that contains training data. + + See [upload file](/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](/docs/api-reference/fine-tuning/chat-input), [completions](/docs/api-reference/fine-tuning/completions-input) format, or if the fine-tuning method uses the [preference](/docs/api-reference/fine-tuning/preference-input) format. + + See the [fine-tuning guide](/docs/guides/model-optimization) for more details. + +- `hyperparameters: optional object { batch_size, learning_rate_multiplier, n_epochs }` + + 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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters + are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid + overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle + through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + +- `integrations: optional array of object { type, wandb }` + + A list of integrations to enable for your fine-tuning job. + + - `type: "wandb"` + + The type of integration to enable. Currently, only "wandb" (Weights and Biases) is supported. + + - `"wandb"` + + - `wandb: object { project, entity, name, tags }` + + The settings for your integration with Weights and Biases. This payload specifies the project that + metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags + to your run, and set a default entity (team, username, etc) to be associated with your run. + + - `project: string` + + The name of the project that the new run will be created under. + + - `entity: optional string` + + The entity to use for the run. This allows you to set the team or username of the WandB user that you would + like associated with the run. If not set, the default entity for the registered WandB API key is used. + + - `name: optional string` + + A display name to set for the run. If not set, we will use the Job ID as the name. + + - `tags: optional array of string` + + A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some + default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". + +- `metadata: 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 object { type, dpo, reinforcement, supervised }` + + The method used for fine-tuning. + + - `type: "supervised" or "dpo" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `beta: optional "auto" or number` + + The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reinforcement: optional ReinforcementMethod` + + Configuration for the reinforcement fine-tuning method. + + - `grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + The grader used for the fine-tuning job. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `input: string` + + The input text. This may include template strings. + + - `name: string` + + The name of the grader. + + - `operation: "eq" or "ne" or "like" or "ilike"` + + The string check operation to perform. One of `eq`, `ne`, `like`, or `ilike`. + + - `"eq"` + + - `"ne"` + + - `"like"` + + - `"ilike"` + + - `reference: string` + + The reference text. This may include template strings. + + - `type: "string_check"` + + The object type, which is always `string_check`. + + - `"string_check"` + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 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: string` + + The text being graded. + + - `name: string` + + The name of the grader. + + - `reference: string` + + The text being graded against. + + - `type: "text_similarity"` + + The type of grader. + + - `"text_similarity"` + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `name: string` + + The name of the grader. + + - `source: string` + + The source code of the python script. + + - `type: "python"` + + The object type, which is always `python`. + + - `"python"` + + - `image_tag: optional string` + + The image tag to use for the python script. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `input: array of object { content, role, type }` + + The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings. + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `text: string` + + The text input to the model. + + - `type: "input_text"` + + The type of the input item. Always `input_text`. + + - `"input_text"` + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `input_audio: object { data, format }` + + - `data: string` + + Base64-encoded audio data. + + - `format: "mp3" or "wav"` + + The format of the audio data. Currently supported formats are `mp3` and + `wav`. + + - `"mp3"` + + - `"wav"` + + - `type: "input_audio"` + + The type of the input item. Always `input_audio`. + + - `"input_audio"` + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `model: string` + + The model to use for the evaluation. + + - `name: string` + + The name of the grader. + + - `type: "score_model"` + + The object type, which is always `score_model`. + + - `"score_model"` + + - `range: optional array of number` + + The range of the score. Defaults to `[0, 1]`. + + - `sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }` + + The sampling parameters for the model. + + - `max_completions_tokens: optional number` + + 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 number` + + A seed value to initialize the randomness, during sampling. + + - `temperature: optional number` + + A higher temperature increases randomness in the outputs. + + - `top_p: optional number` + + An alternative to temperature for nucleus sampling; 1.0 includes all tokens. + + - `MultiGrader object { calculate_output, graders, name, type }` + + A MultiGrader object combines the output of multiple graders to produce a single score. + + - `calculate_output: string` + + A formula to calculate the output based on grader results. + + - `graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `LabelModelGrader object { input, labels, model, 3 more }` + + A LabelModelGrader object which uses a model to assign labels to each item + in the evaluation. + + - `input: array of object { content, role, type }` + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `labels: array of string` + + The labels to assign to each item in the evaluation. + + - `model: string` + + The model to use for the evaluation. Must support structured outputs. + + - `name: string` + + The name of the grader. + + - `passing_labels: array of string` + + The labels that indicate a passing result. Must be a subset of labels. + + - `type: "label_model"` + + The object type, which is always `label_model`. + + - `"label_model"` + + - `name: string` + + The name of the grader. + + - `type: "multi"` + + The object type, which is always `multi`. + + - `"multi"` + + - `hyperparameters: optional ReinforcementHyperparameters` + + The hyperparameters used for the reinforcement fine-tuning job. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `compute_multiplier: optional "auto" or number` + + Multiplier on amount of compute used for exploring search space during training. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_interval: optional "auto" or number` + + The number of training steps between evaluation runs. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_samples: optional "auto" or number` + + Number of evaluation samples to generate per training step. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reasoning_effort: optional "default" or "low" or "medium" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + +- `seed: optional number` + + 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 string` + + A string of up to 64 characters that will be added to your fine-tuned model name. + + For example, a `suffix` of "custom-model-name" would produce a model name like `ft:gpt-4o-mini:openai:custom-model-name:7p4lURel`. + +- `validation_file: optional string` + + The ID of an uploaded file that contains validation data. + + If you provide this file, the data is used to generate validation + metrics periodically during fine-tuning. These metrics can be viewed in + the fine-tuning results file. + The same data should not be present in both train and validation files. + + Your dataset must be formatted as a JSONL file. You must upload your file with the purpose `fine-tune`. + + See the [fine-tuning guide](/docs/guides/model-optimization) for more details. + +### Returns + +- `FineTuningJob object { id, created_at, error, 16 more }` + + The `fine_tuning.job` object represents a fine-tuning job that has been created through the API. + + - `id: string` + + The object identifier, which can be referenced in the API endpoints. + + - `created_at: number` + + The Unix timestamp (in seconds) for when the fine-tuning job was created. + + - `error: object { code, message, param }` + + For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure. + + - `code: string` + + A machine-readable error code. + + - `message: string` + + A human-readable error message. + + - `param: string` + + The parameter that was invalid, usually `training_file` or `validation_file`. This field will be null if the failure was not parameter-specific. + + - `fine_tuned_model: string` + + The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running. + + - `finished_at: number` + + 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: object { batch_size, learning_rate_multiplier, n_epochs }` + + The hyperparameters used for the fine-tuning job. This value will only be returned when running `supervised` jobs. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters + are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid + overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle + through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `model: string` + + The base model that is being fine-tuned. + + - `object: "fine_tuning.job"` + + The object type, which is always "fine_tuning.job". + + - `"fine_tuning.job"` + + - `organization_id: string` + + The organization that owns the fine-tuning job. + + - `result_files: array of string` + + The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `seed: number` + + The seed used for the fine-tuning job. + + - `status: "validating_files" or "queued" or "running" or 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: number` + + 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: string` + + The file ID used for training. You can retrieve the training data with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `validation_file: string` + + The file ID used for validation. You can retrieve the validation results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `estimated_finish: optional number` + + 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 array of FineTuningJobWandbIntegrationObject` + + A list of integrations to enable for this fine-tuning job. + + - `type: "wandb"` + + The type of the integration being enabled for the fine-tuning job + + - `"wandb"` + + - `wandb: FineTuningJobWandbIntegration` + + The settings for your integration with Weights and Biases. This payload specifies the project that + metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags + to your run, and set a default entity (team, username, etc) to be associated with your run. + + - `project: string` + + The name of the project that the new run will be created under. + + - `entity: optional string` + + The entity to use for the run. This allows you to set the team or username of the WandB user that you would + like associated with the run. If not set, the default entity for the registered WandB API key is used. + + - `name: optional string` + + A display name to set for the run. If not set, we will use the Job ID as the name. + + - `tags: optional array of string` + + A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some + default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". + + - `metadata: 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 object { type, dpo, reinforcement, supervised }` + + The method used for fine-tuning. + + - `type: "supervised" or "dpo" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `beta: optional "auto" or number` + + The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reinforcement: optional ReinforcementMethod` + + Configuration for the reinforcement fine-tuning method. + + - `grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + The grader used for the fine-tuning job. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `input: string` + + The input text. This may include template strings. + + - `name: string` + + The name of the grader. + + - `operation: "eq" or "ne" or "like" or "ilike"` + + The string check operation to perform. One of `eq`, `ne`, `like`, or `ilike`. + + - `"eq"` + + - `"ne"` + + - `"like"` + + - `"ilike"` + + - `reference: string` + + The reference text. This may include template strings. + + - `type: "string_check"` + + The object type, which is always `string_check`. + + - `"string_check"` + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 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: string` + + The text being graded. + + - `name: string` + + The name of the grader. + + - `reference: string` + + The text being graded against. + + - `type: "text_similarity"` + + The type of grader. + + - `"text_similarity"` + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `name: string` + + The name of the grader. + + - `source: string` + + The source code of the python script. + + - `type: "python"` + + The object type, which is always `python`. + + - `"python"` + + - `image_tag: optional string` + + The image tag to use for the python script. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `input: array of object { content, role, type }` + + The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings. + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `text: string` + + The text input to the model. + + - `type: "input_text"` + + The type of the input item. Always `input_text`. + + - `"input_text"` + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `input_audio: object { data, format }` + + - `data: string` + + Base64-encoded audio data. + + - `format: "mp3" or "wav"` + + The format of the audio data. Currently supported formats are `mp3` and + `wav`. + + - `"mp3"` + + - `"wav"` + + - `type: "input_audio"` + + The type of the input item. Always `input_audio`. + + - `"input_audio"` + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `model: string` + + The model to use for the evaluation. + + - `name: string` + + The name of the grader. + + - `type: "score_model"` + + The object type, which is always `score_model`. + + - `"score_model"` + + - `range: optional array of number` + + The range of the score. Defaults to `[0, 1]`. + + - `sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }` + + The sampling parameters for the model. + + - `max_completions_tokens: optional number` + + 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 number` + + A seed value to initialize the randomness, during sampling. + + - `temperature: optional number` + + A higher temperature increases randomness in the outputs. + + - `top_p: optional number` + + An alternative to temperature for nucleus sampling; 1.0 includes all tokens. + + - `MultiGrader object { calculate_output, graders, name, type }` + + A MultiGrader object combines the output of multiple graders to produce a single score. + + - `calculate_output: string` + + A formula to calculate the output based on grader results. + + - `graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `LabelModelGrader object { input, labels, model, 3 more }` + + A LabelModelGrader object which uses a model to assign labels to each item + in the evaluation. + + - `input: array of object { content, role, type }` + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `labels: array of string` + + The labels to assign to each item in the evaluation. + + - `model: string` + + The model to use for the evaluation. Must support structured outputs. + + - `name: string` + + The name of the grader. + + - `passing_labels: array of string` + + The labels that indicate a passing result. Must be a subset of labels. + + - `type: "label_model"` + + The object type, which is always `label_model`. + + - `"label_model"` + + - `name: string` + + The name of the grader. + + - `type: "multi"` + + The object type, which is always `multi`. + + - `"multi"` + + - `hyperparameters: optional ReinforcementHyperparameters` + + The hyperparameters used for the reinforcement fine-tuning job. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `compute_multiplier: optional "auto" or number` + + Multiplier on amount of compute used for exploring search space during training. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_interval: optional "auto" or number` + + The number of training steps between evaluation runs. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_samples: optional "auto" or number` + + Number of evaluation samples to generate per training step. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reasoning_effort: optional "default" or "low" or "medium" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + +### Example + +```http +curl https://api.openai.com/v1/fine_tuning/jobs \ + -H 'Content-Type: application/json' \ + -H "Authorization: Bearer $OPENAI_API_KEY" \ + -d '{ + "model": "gpt-4o-mini", + "training_file": "file-abc123", + "seed": 42, + "validation_file": "file-abc123" + }' +``` + +#### 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 + +```http +curl https://api.openai.com/v1/fine_tuning/jobs \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $OPENAI_API_KEY" \ + -d '{ + "training_file": "file-BK7bzQj3FfZFXr7DbL6xJwfo", + "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 + +```http +curl https://api.openai.com/v1/fine_tuning/jobs \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $OPENAI_API_KEY" \ + -d '{ + "training_file": "file-abc123", + "model": "gpt-4o-mini", + "method": { + "type": "supervised", + "supervised": { + "hyperparameters": { + "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 + +```http +curl https://api.openai.com/v1/fine_tuning/jobs \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $OPENAI_API_KEY" \ + -d '{ + "training_file": "file-abc123", + "validation_file": "file-abc123", + "model": "gpt-4o-mini", + "method": { + "type": "dpo", + "dpo": { + "hyperparameters": { + "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 + +```http +curl https://api.openai.com/v1/fine_tuning/jobs \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $OPENAI_API_KEY" \ + -d '{ + "training_file": "file-abc", + "validation_file": "file-123", + "model": "o4-mini", + "method": { + "type": "reinforcement", + "reinforcement": { + "grader": { + "type": "string_check", + "name": "Example string check grader", + "input": "{{sample.output_text}}", + "reference": "{{item.label}}", + "operation": "eq" + }, + "hyperparameters": { + "reasoning_effort": "medium" + } + } + } + }' +``` + +#### 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 + +```http +curl https://api.openai.com/v1/fine_tuning/jobs \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $OPENAI_API_KEY" \ + -d '{ + "training_file": "file-abc123", + "validation_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": "file-abc123", + "training_file": "file-abc123", + "method": { + "type": "supervised", + "supervised": { + "hyperparameters": { + "batch_size": "auto", + "learning_rate_multiplier": "auto", + "n_epochs": "auto", + } + } + }, + "metadata": null +} +``` + +### W&B Integration + +```http +curl https://api.openai.com/v1/fine_tuning/jobs \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $OPENAI_API_KEY" \ + -d '{ + "training_file": "file-abc123", + "validation_file": "file-abc123", + "model": "gpt-4o-mini", + "integrations": [ + { + "type": "wandb", + "wandb": { + "project": "my-wandb-project", + "name": "ft-run-display-name" + "tags": [ + "first-experiment", "v2" + ] + } + } + ] + }' +``` + +#### 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", + "integrations": [ + { + "type": "wandb", + "wandb": { + "project": "my-wandb-project", + "entity": None, + "run_id": "ftjob-abc123" + } + } + ], + "method": { + "type": "supervised", + "supervised": { + "hyperparameters": { + "batch_size": "auto", + "learning_rate_multiplier": "auto", + "n_epochs": "auto", + } + } + }, + "metadata": null +} +``` + +## List fine-tuning jobs + +**get** `/fine_tuning/jobs` + +List your organization's fine-tuning jobs + +### Query Parameters + +- `after: optional string` + + Identifier for the last job from the previous pagination request. + +- `limit: optional number` + + Number of fine-tuning jobs to retrieve. + +- `metadata: optional map[string]` + + Optional metadata filter. To filter, use the syntax `metadata[k]=v`. Alternatively, set `metadata=null` to indicate no metadata. + +### Returns + +- `data: array of FineTuningJob` + + - `id: string` + + The object identifier, which can be referenced in the API endpoints. + + - `created_at: number` + + The Unix timestamp (in seconds) for when the fine-tuning job was created. + + - `error: object { code, message, param }` + + For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure. + + - `code: string` + + A machine-readable error code. + + - `message: string` + + A human-readable error message. + + - `param: string` + + The parameter that was invalid, usually `training_file` or `validation_file`. This field will be null if the failure was not parameter-specific. + + - `fine_tuned_model: string` + + The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running. + + - `finished_at: number` + + 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: object { batch_size, learning_rate_multiplier, n_epochs }` + + The hyperparameters used for the fine-tuning job. This value will only be returned when running `supervised` jobs. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters + are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid + overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle + through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `model: string` + + The base model that is being fine-tuned. + + - `object: "fine_tuning.job"` + + The object type, which is always "fine_tuning.job". + + - `"fine_tuning.job"` + + - `organization_id: string` + + The organization that owns the fine-tuning job. + + - `result_files: array of string` + + The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `seed: number` + + The seed used for the fine-tuning job. + + - `status: "validating_files" or "queued" or "running" or 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: number` + + 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: string` + + The file ID used for training. You can retrieve the training data with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `validation_file: string` + + The file ID used for validation. You can retrieve the validation results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `estimated_finish: optional number` + + 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 array of FineTuningJobWandbIntegrationObject` + + A list of integrations to enable for this fine-tuning job. + + - `type: "wandb"` + + The type of the integration being enabled for the fine-tuning job + + - `"wandb"` + + - `wandb: FineTuningJobWandbIntegration` + + The settings for your integration with Weights and Biases. This payload specifies the project that + metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags + to your run, and set a default entity (team, username, etc) to be associated with your run. + + - `project: string` + + The name of the project that the new run will be created under. + + - `entity: optional string` + + The entity to use for the run. This allows you to set the team or username of the WandB user that you would + like associated with the run. If not set, the default entity for the registered WandB API key is used. + + - `name: optional string` + + A display name to set for the run. If not set, we will use the Job ID as the name. + + - `tags: optional array of string` + + A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some + default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". + + - `metadata: 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 object { type, dpo, reinforcement, supervised }` + + The method used for fine-tuning. + + - `type: "supervised" or "dpo" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `beta: optional "auto" or number` + + The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reinforcement: optional ReinforcementMethod` + + Configuration for the reinforcement fine-tuning method. + + - `grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + The grader used for the fine-tuning job. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `input: string` + + The input text. This may include template strings. + + - `name: string` + + The name of the grader. + + - `operation: "eq" or "ne" or "like" or "ilike"` + + The string check operation to perform. One of `eq`, `ne`, `like`, or `ilike`. + + - `"eq"` + + - `"ne"` + + - `"like"` + + - `"ilike"` + + - `reference: string` + + The reference text. This may include template strings. + + - `type: "string_check"` + + The object type, which is always `string_check`. + + - `"string_check"` + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 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: string` + + The text being graded. + + - `name: string` + + The name of the grader. + + - `reference: string` + + The text being graded against. + + - `type: "text_similarity"` + + The type of grader. + + - `"text_similarity"` + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `name: string` + + The name of the grader. + + - `source: string` + + The source code of the python script. + + - `type: "python"` + + The object type, which is always `python`. + + - `"python"` + + - `image_tag: optional string` + + The image tag to use for the python script. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `input: array of object { content, role, type }` + + The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings. + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `text: string` + + The text input to the model. + + - `type: "input_text"` + + The type of the input item. Always `input_text`. + + - `"input_text"` + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `input_audio: object { data, format }` + + - `data: string` + + Base64-encoded audio data. + + - `format: "mp3" or "wav"` + + The format of the audio data. Currently supported formats are `mp3` and + `wav`. + + - `"mp3"` + + - `"wav"` + + - `type: "input_audio"` + + The type of the input item. Always `input_audio`. + + - `"input_audio"` + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `model: string` + + The model to use for the evaluation. + + - `name: string` + + The name of the grader. + + - `type: "score_model"` + + The object type, which is always `score_model`. + + - `"score_model"` + + - `range: optional array of number` + + The range of the score. Defaults to `[0, 1]`. + + - `sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }` + + The sampling parameters for the model. + + - `max_completions_tokens: optional number` + + 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 number` + + A seed value to initialize the randomness, during sampling. + + - `temperature: optional number` + + A higher temperature increases randomness in the outputs. + + - `top_p: optional number` + + An alternative to temperature for nucleus sampling; 1.0 includes all tokens. + + - `MultiGrader object { calculate_output, graders, name, type }` + + A MultiGrader object combines the output of multiple graders to produce a single score. + + - `calculate_output: string` + + A formula to calculate the output based on grader results. + + - `graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `LabelModelGrader object { input, labels, model, 3 more }` + + A LabelModelGrader object which uses a model to assign labels to each item + in the evaluation. + + - `input: array of object { content, role, type }` + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `labels: array of string` + + The labels to assign to each item in the evaluation. + + - `model: string` + + The model to use for the evaluation. Must support structured outputs. + + - `name: string` + + The name of the grader. + + - `passing_labels: array of string` + + The labels that indicate a passing result. Must be a subset of labels. + + - `type: "label_model"` + + The object type, which is always `label_model`. + + - `"label_model"` + + - `name: string` + + The name of the grader. + + - `type: "multi"` + + The object type, which is always `multi`. + + - `"multi"` + + - `hyperparameters: optional ReinforcementHyperparameters` + + The hyperparameters used for the reinforcement fine-tuning job. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `compute_multiplier: optional "auto" or number` + + Multiplier on amount of compute used for exploring search space during training. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_interval: optional "auto" or number` + + The number of training steps between evaluation runs. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_samples: optional "auto" or number` + + Number of evaluation samples to generate per training step. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reasoning_effort: optional "default" or "low" or "medium" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + +- `has_more: boolean` + +- `object: "list"` + + - `"list"` + +### Example + +```http +curl https://api.openai.com/v1/fine_tuning/jobs \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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 + +```http +curl https://api.openai.com/v1/fine_tuning/jobs?limit=2&metadata[key]=value \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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 + +**get** `/fine_tuning/jobs/{fine_tuning_job_id}` + +Get info about a fine-tuning job. + +[Learn more about fine-tuning](/docs/guides/model-optimization) + +### Path Parameters + +- `fine_tuning_job_id: string` + +### Returns + +- `FineTuningJob object { id, created_at, error, 16 more }` + + The `fine_tuning.job` object represents a fine-tuning job that has been created through the API. + + - `id: string` + + The object identifier, which can be referenced in the API endpoints. + + - `created_at: number` + + The Unix timestamp (in seconds) for when the fine-tuning job was created. + + - `error: object { code, message, param }` + + For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure. + + - `code: string` + + A machine-readable error code. + + - `message: string` + + A human-readable error message. + + - `param: string` + + The parameter that was invalid, usually `training_file` or `validation_file`. This field will be null if the failure was not parameter-specific. + + - `fine_tuned_model: string` + + The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running. + + - `finished_at: number` + + 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: object { batch_size, learning_rate_multiplier, n_epochs }` + + The hyperparameters used for the fine-tuning job. This value will only be returned when running `supervised` jobs. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters + are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid + overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle + through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `model: string` + + The base model that is being fine-tuned. + + - `object: "fine_tuning.job"` + + The object type, which is always "fine_tuning.job". + + - `"fine_tuning.job"` + + - `organization_id: string` + + The organization that owns the fine-tuning job. + + - `result_files: array of string` + + The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `seed: number` + + The seed used for the fine-tuning job. + + - `status: "validating_files" or "queued" or "running" or 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: number` + + 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: string` + + The file ID used for training. You can retrieve the training data with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `validation_file: string` + + The file ID used for validation. You can retrieve the validation results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `estimated_finish: optional number` + + 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 array of FineTuningJobWandbIntegrationObject` + + A list of integrations to enable for this fine-tuning job. + + - `type: "wandb"` + + The type of the integration being enabled for the fine-tuning job + + - `"wandb"` + + - `wandb: FineTuningJobWandbIntegration` + + The settings for your integration with Weights and Biases. This payload specifies the project that + metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags + to your run, and set a default entity (team, username, etc) to be associated with your run. + + - `project: string` + + The name of the project that the new run will be created under. + + - `entity: optional string` + + The entity to use for the run. This allows you to set the team or username of the WandB user that you would + like associated with the run. If not set, the default entity for the registered WandB API key is used. + + - `name: optional string` + + A display name to set for the run. If not set, we will use the Job ID as the name. + + - `tags: optional array of string` + + A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some + default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". + + - `metadata: 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 object { type, dpo, reinforcement, supervised }` + + The method used for fine-tuning. + + - `type: "supervised" or "dpo" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `beta: optional "auto" or number` + + The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reinforcement: optional ReinforcementMethod` + + Configuration for the reinforcement fine-tuning method. + + - `grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + The grader used for the fine-tuning job. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `input: string` + + The input text. This may include template strings. + + - `name: string` + + The name of the grader. + + - `operation: "eq" or "ne" or "like" or "ilike"` + + The string check operation to perform. One of `eq`, `ne`, `like`, or `ilike`. + + - `"eq"` + + - `"ne"` + + - `"like"` + + - `"ilike"` + + - `reference: string` + + The reference text. This may include template strings. + + - `type: "string_check"` + + The object type, which is always `string_check`. + + - `"string_check"` + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 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: string` + + The text being graded. + + - `name: string` + + The name of the grader. + + - `reference: string` + + The text being graded against. + + - `type: "text_similarity"` + + The type of grader. + + - `"text_similarity"` + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `name: string` + + The name of the grader. + + - `source: string` + + The source code of the python script. + + - `type: "python"` + + The object type, which is always `python`. + + - `"python"` + + - `image_tag: optional string` + + The image tag to use for the python script. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `input: array of object { content, role, type }` + + The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings. + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `text: string` + + The text input to the model. + + - `type: "input_text"` + + The type of the input item. Always `input_text`. + + - `"input_text"` + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `input_audio: object { data, format }` + + - `data: string` + + Base64-encoded audio data. + + - `format: "mp3" or "wav"` + + The format of the audio data. Currently supported formats are `mp3` and + `wav`. + + - `"mp3"` + + - `"wav"` + + - `type: "input_audio"` + + The type of the input item. Always `input_audio`. + + - `"input_audio"` + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `model: string` + + The model to use for the evaluation. + + - `name: string` + + The name of the grader. + + - `type: "score_model"` + + The object type, which is always `score_model`. + + - `"score_model"` + + - `range: optional array of number` + + The range of the score. Defaults to `[0, 1]`. + + - `sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }` + + The sampling parameters for the model. + + - `max_completions_tokens: optional number` + + 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 number` + + A seed value to initialize the randomness, during sampling. + + - `temperature: optional number` + + A higher temperature increases randomness in the outputs. + + - `top_p: optional number` + + An alternative to temperature for nucleus sampling; 1.0 includes all tokens. + + - `MultiGrader object { calculate_output, graders, name, type }` + + A MultiGrader object combines the output of multiple graders to produce a single score. + + - `calculate_output: string` + + A formula to calculate the output based on grader results. + + - `graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `LabelModelGrader object { input, labels, model, 3 more }` + + A LabelModelGrader object which uses a model to assign labels to each item + in the evaluation. + + - `input: array of object { content, role, type }` + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `labels: array of string` + + The labels to assign to each item in the evaluation. + + - `model: string` + + The model to use for the evaluation. Must support structured outputs. + + - `name: string` + + The name of the grader. + + - `passing_labels: array of string` + + The labels that indicate a passing result. Must be a subset of labels. + + - `type: "label_model"` + + The object type, which is always `label_model`. + + - `"label_model"` + + - `name: string` + + The name of the grader. + + - `type: "multi"` + + The object type, which is always `multi`. + + - `"multi"` + + - `hyperparameters: optional ReinforcementHyperparameters` + + The hyperparameters used for the reinforcement fine-tuning job. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `compute_multiplier: optional "auto" or number` + + Multiplier on amount of compute used for exploring search space during training. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_interval: optional "auto" or number` + + The number of training steps between evaluation runs. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_samples: optional "auto" or number` + + Number of evaluation samples to generate per training step. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reasoning_effort: optional "default" or "low" or "medium" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + +### Example + +```http +curl https://api.openai.com/v1/fine_tuning/jobs/$FINE_TUNING_JOB_ID \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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 + +```http +curl https://api.openai.com/v1/fine_tuning/jobs/ft-AF1WoRqd3aJAHsqc9NY7iL8F \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### Response + +```json +{ + "object": "fine_tuning.job", + "id": "ftjob-abc123", + "model": "davinci-002", + "created_at": 1692661014, + "finished_at": 1692661190, + "fine_tuned_model": "ft:davinci-002:my-org:custom_suffix:7q8mpxmy", + "organization_id": "org-123", + "result_files": [ + "file-abc123" + ], + "status": "succeeded", + "validation_file": null, + "training_file": "file-abc123", + "hyperparameters": { + "n_epochs": 4, + "batch_size": 1, + "learning_rate_multiplier": 1.0 + }, + "trained_tokens": 5768, + "integrations": [], + "seed": 0, + "estimated_finish": 0, + "method": { + "type": "supervised", + "supervised": { + "hyperparameters": { + "n_epochs": 4, + "batch_size": 1, + "learning_rate_multiplier": 1.0 + } + } + } +} +``` + +## List fine-tuning events + +**get** `/fine_tuning/jobs/{fine_tuning_job_id}/events` + +Get status updates for a fine-tuning job. + +### Path Parameters + +- `fine_tuning_job_id: string` + +### Query Parameters + +- `after: optional string` + + Identifier for the last event from the previous pagination request. + +- `limit: optional number` + + Number of events to retrieve. + +### Returns + +- `data: array of FineTuningJobEvent` + + - `id: string` + + The object identifier. + + - `created_at: number` + + The Unix timestamp (in seconds) for when the fine-tuning job was created. + + - `level: "info" or "warn" or "error"` + + The log level of the event. + + - `"info"` + + - `"warn"` + + - `"error"` + + - `message: string` + + The message of the event. + + - `object: "fine_tuning.job.event"` + + The object type, which is always "fine_tuning.job.event". + + - `"fine_tuning.job.event"` + + - `data: optional unknown` + + The data associated with the event. + + - `type: optional "message" or "metrics"` + + The type of event. + + - `"message"` + + - `"metrics"` + +- `has_more: boolean` + +- `object: "list"` + + - `"list"` + +### Example + +```http +curl https://api.openai.com/v1/fine_tuning/jobs/$FINE_TUNING_JOB_ID/events \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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 + +```http +curl https://api.openai.com/v1/fine_tuning/jobs/ftjob-abc123/events \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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 + +**post** `/fine_tuning/jobs/{fine_tuning_job_id}/cancel` + +Immediately cancel a fine-tune job. + +### Path Parameters + +- `fine_tuning_job_id: string` + +### Returns + +- `FineTuningJob object { id, created_at, error, 16 more }` + + The `fine_tuning.job` object represents a fine-tuning job that has been created through the API. + + - `id: string` + + The object identifier, which can be referenced in the API endpoints. + + - `created_at: number` + + The Unix timestamp (in seconds) for when the fine-tuning job was created. + + - `error: object { code, message, param }` + + For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure. + + - `code: string` + + A machine-readable error code. + + - `message: string` + + A human-readable error message. + + - `param: string` + + The parameter that was invalid, usually `training_file` or `validation_file`. This field will be null if the failure was not parameter-specific. + + - `fine_tuned_model: string` + + The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running. + + - `finished_at: number` + + 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: object { batch_size, learning_rate_multiplier, n_epochs }` + + The hyperparameters used for the fine-tuning job. This value will only be returned when running `supervised` jobs. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters + are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid + overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle + through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `model: string` + + The base model that is being fine-tuned. + + - `object: "fine_tuning.job"` + + The object type, which is always "fine_tuning.job". + + - `"fine_tuning.job"` + + - `organization_id: string` + + The organization that owns the fine-tuning job. + + - `result_files: array of string` + + The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `seed: number` + + The seed used for the fine-tuning job. + + - `status: "validating_files" or "queued" or "running" or 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: number` + + 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: string` + + The file ID used for training. You can retrieve the training data with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `validation_file: string` + + The file ID used for validation. You can retrieve the validation results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `estimated_finish: optional number` + + 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 array of FineTuningJobWandbIntegrationObject` + + A list of integrations to enable for this fine-tuning job. + + - `type: "wandb"` + + The type of the integration being enabled for the fine-tuning job + + - `"wandb"` + + - `wandb: FineTuningJobWandbIntegration` + + The settings for your integration with Weights and Biases. This payload specifies the project that + metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags + to your run, and set a default entity (team, username, etc) to be associated with your run. + + - `project: string` + + The name of the project that the new run will be created under. + + - `entity: optional string` + + The entity to use for the run. This allows you to set the team or username of the WandB user that you would + like associated with the run. If not set, the default entity for the registered WandB API key is used. + + - `name: optional string` + + A display name to set for the run. If not set, we will use the Job ID as the name. + + - `tags: optional array of string` + + A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some + default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". + + - `metadata: 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 object { type, dpo, reinforcement, supervised }` + + The method used for fine-tuning. + + - `type: "supervised" or "dpo" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `beta: optional "auto" or number` + + The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reinforcement: optional ReinforcementMethod` + + Configuration for the reinforcement fine-tuning method. + + - `grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + The grader used for the fine-tuning job. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `input: string` + + The input text. This may include template strings. + + - `name: string` + + The name of the grader. + + - `operation: "eq" or "ne" or "like" or "ilike"` + + The string check operation to perform. One of `eq`, `ne`, `like`, or `ilike`. + + - `"eq"` + + - `"ne"` + + - `"like"` + + - `"ilike"` + + - `reference: string` + + The reference text. This may include template strings. + + - `type: "string_check"` + + The object type, which is always `string_check`. + + - `"string_check"` + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 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: string` + + The text being graded. + + - `name: string` + + The name of the grader. + + - `reference: string` + + The text being graded against. + + - `type: "text_similarity"` + + The type of grader. + + - `"text_similarity"` + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `name: string` + + The name of the grader. + + - `source: string` + + The source code of the python script. + + - `type: "python"` + + The object type, which is always `python`. + + - `"python"` + + - `image_tag: optional string` + + The image tag to use for the python script. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `input: array of object { content, role, type }` + + The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings. + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `text: string` + + The text input to the model. + + - `type: "input_text"` + + The type of the input item. Always `input_text`. + + - `"input_text"` + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `input_audio: object { data, format }` + + - `data: string` + + Base64-encoded audio data. + + - `format: "mp3" or "wav"` + + The format of the audio data. Currently supported formats are `mp3` and + `wav`. + + - `"mp3"` + + - `"wav"` + + - `type: "input_audio"` + + The type of the input item. Always `input_audio`. + + - `"input_audio"` + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `model: string` + + The model to use for the evaluation. + + - `name: string` + + The name of the grader. + + - `type: "score_model"` + + The object type, which is always `score_model`. + + - `"score_model"` + + - `range: optional array of number` + + The range of the score. Defaults to `[0, 1]`. + + - `sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }` + + The sampling parameters for the model. + + - `max_completions_tokens: optional number` + + 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 number` + + A seed value to initialize the randomness, during sampling. + + - `temperature: optional number` + + A higher temperature increases randomness in the outputs. + + - `top_p: optional number` + + An alternative to temperature for nucleus sampling; 1.0 includes all tokens. + + - `MultiGrader object { calculate_output, graders, name, type }` + + A MultiGrader object combines the output of multiple graders to produce a single score. + + - `calculate_output: string` + + A formula to calculate the output based on grader results. + + - `graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `LabelModelGrader object { input, labels, model, 3 more }` + + A LabelModelGrader object which uses a model to assign labels to each item + in the evaluation. + + - `input: array of object { content, role, type }` + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `labels: array of string` + + The labels to assign to each item in the evaluation. + + - `model: string` + + The model to use for the evaluation. Must support structured outputs. + + - `name: string` + + The name of the grader. + + - `passing_labels: array of string` + + The labels that indicate a passing result. Must be a subset of labels. + + - `type: "label_model"` + + The object type, which is always `label_model`. + + - `"label_model"` + + - `name: string` + + The name of the grader. + + - `type: "multi"` + + The object type, which is always `multi`. + + - `"multi"` + + - `hyperparameters: optional ReinforcementHyperparameters` + + The hyperparameters used for the reinforcement fine-tuning job. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `compute_multiplier: optional "auto" or number` + + Multiplier on amount of compute used for exploring search space during training. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_interval: optional "auto" or number` + + The number of training steps between evaluation runs. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_samples: optional "auto" or number` + + Number of evaluation samples to generate per training step. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reasoning_effort: optional "default" or "low" or "medium" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + +### Example + +```http +curl https://api.openai.com/v1/fine_tuning/jobs/$FINE_TUNING_JOB_ID/cancel \ + -X POST \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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 + +```http +curl -X POST https://api.openai.com/v1/fine_tuning/jobs/ftjob-abc123/cancel \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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 + +**post** `/fine_tuning/jobs/{fine_tuning_job_id}/pause` + +Pause a fine-tune job. + +### Path Parameters + +- `fine_tuning_job_id: string` + +### Returns + +- `FineTuningJob object { id, created_at, error, 16 more }` + + The `fine_tuning.job` object represents a fine-tuning job that has been created through the API. + + - `id: string` + + The object identifier, which can be referenced in the API endpoints. + + - `created_at: number` + + The Unix timestamp (in seconds) for when the fine-tuning job was created. + + - `error: object { code, message, param }` + + For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure. + + - `code: string` + + A machine-readable error code. + + - `message: string` + + A human-readable error message. + + - `param: string` + + The parameter that was invalid, usually `training_file` or `validation_file`. This field will be null if the failure was not parameter-specific. + + - `fine_tuned_model: string` + + The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running. + + - `finished_at: number` + + 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: object { batch_size, learning_rate_multiplier, n_epochs }` + + The hyperparameters used for the fine-tuning job. This value will only be returned when running `supervised` jobs. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters + are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid + overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle + through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `model: string` + + The base model that is being fine-tuned. + + - `object: "fine_tuning.job"` + + The object type, which is always "fine_tuning.job". + + - `"fine_tuning.job"` + + - `organization_id: string` + + The organization that owns the fine-tuning job. + + - `result_files: array of string` + + The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `seed: number` + + The seed used for the fine-tuning job. + + - `status: "validating_files" or "queued" or "running" or 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: number` + + 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: string` + + The file ID used for training. You can retrieve the training data with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `validation_file: string` + + The file ID used for validation. You can retrieve the validation results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `estimated_finish: optional number` + + 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 array of FineTuningJobWandbIntegrationObject` + + A list of integrations to enable for this fine-tuning job. + + - `type: "wandb"` + + The type of the integration being enabled for the fine-tuning job + + - `"wandb"` + + - `wandb: FineTuningJobWandbIntegration` + + The settings for your integration with Weights and Biases. This payload specifies the project that + metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags + to your run, and set a default entity (team, username, etc) to be associated with your run. + + - `project: string` + + The name of the project that the new run will be created under. + + - `entity: optional string` + + The entity to use for the run. This allows you to set the team or username of the WandB user that you would + like associated with the run. If not set, the default entity for the registered WandB API key is used. + + - `name: optional string` + + A display name to set for the run. If not set, we will use the Job ID as the name. + + - `tags: optional array of string` + + A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some + default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". + + - `metadata: 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 object { type, dpo, reinforcement, supervised }` + + The method used for fine-tuning. + + - `type: "supervised" or "dpo" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `beta: optional "auto" or number` + + The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reinforcement: optional ReinforcementMethod` + + Configuration for the reinforcement fine-tuning method. + + - `grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + The grader used for the fine-tuning job. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `input: string` + + The input text. This may include template strings. + + - `name: string` + + The name of the grader. + + - `operation: "eq" or "ne" or "like" or "ilike"` + + The string check operation to perform. One of `eq`, `ne`, `like`, or `ilike`. + + - `"eq"` + + - `"ne"` + + - `"like"` + + - `"ilike"` + + - `reference: string` + + The reference text. This may include template strings. + + - `type: "string_check"` + + The object type, which is always `string_check`. + + - `"string_check"` + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 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: string` + + The text being graded. + + - `name: string` + + The name of the grader. + + - `reference: string` + + The text being graded against. + + - `type: "text_similarity"` + + The type of grader. + + - `"text_similarity"` + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `name: string` + + The name of the grader. + + - `source: string` + + The source code of the python script. + + - `type: "python"` + + The object type, which is always `python`. + + - `"python"` + + - `image_tag: optional string` + + The image tag to use for the python script. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `input: array of object { content, role, type }` + + The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings. + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `text: string` + + The text input to the model. + + - `type: "input_text"` + + The type of the input item. Always `input_text`. + + - `"input_text"` + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `input_audio: object { data, format }` + + - `data: string` + + Base64-encoded audio data. + + - `format: "mp3" or "wav"` + + The format of the audio data. Currently supported formats are `mp3` and + `wav`. + + - `"mp3"` + + - `"wav"` + + - `type: "input_audio"` + + The type of the input item. Always `input_audio`. + + - `"input_audio"` + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `model: string` + + The model to use for the evaluation. + + - `name: string` + + The name of the grader. + + - `type: "score_model"` + + The object type, which is always `score_model`. + + - `"score_model"` + + - `range: optional array of number` + + The range of the score. Defaults to `[0, 1]`. + + - `sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }` + + The sampling parameters for the model. + + - `max_completions_tokens: optional number` + + 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 number` + + A seed value to initialize the randomness, during sampling. + + - `temperature: optional number` + + A higher temperature increases randomness in the outputs. + + - `top_p: optional number` + + An alternative to temperature for nucleus sampling; 1.0 includes all tokens. + + - `MultiGrader object { calculate_output, graders, name, type }` + + A MultiGrader object combines the output of multiple graders to produce a single score. + + - `calculate_output: string` + + A formula to calculate the output based on grader results. + + - `graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `LabelModelGrader object { input, labels, model, 3 more }` + + A LabelModelGrader object which uses a model to assign labels to each item + in the evaluation. + + - `input: array of object { content, role, type }` + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `labels: array of string` + + The labels to assign to each item in the evaluation. + + - `model: string` + + The model to use for the evaluation. Must support structured outputs. + + - `name: string` + + The name of the grader. + + - `passing_labels: array of string` + + The labels that indicate a passing result. Must be a subset of labels. + + - `type: "label_model"` + + The object type, which is always `label_model`. + + - `"label_model"` + + - `name: string` + + The name of the grader. + + - `type: "multi"` + + The object type, which is always `multi`. + + - `"multi"` + + - `hyperparameters: optional ReinforcementHyperparameters` + + The hyperparameters used for the reinforcement fine-tuning job. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `compute_multiplier: optional "auto" or number` + + Multiplier on amount of compute used for exploring search space during training. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_interval: optional "auto" or number` + + The number of training steps between evaluation runs. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_samples: optional "auto" or number` + + Number of evaluation samples to generate per training step. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reasoning_effort: optional "default" or "low" or "medium" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + +### Example + +```http +curl https://api.openai.com/v1/fine_tuning/jobs/$FINE_TUNING_JOB_ID/pause \ + -X POST \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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 + +```http +curl -X POST https://api.openai.com/v1/fine_tuning/jobs/ftjob-abc123/pause \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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 + +**post** `/fine_tuning/jobs/{fine_tuning_job_id}/resume` + +Resume a fine-tune job. + +### Path Parameters + +- `fine_tuning_job_id: string` + +### Returns + +- `FineTuningJob object { id, created_at, error, 16 more }` + + The `fine_tuning.job` object represents a fine-tuning job that has been created through the API. + + - `id: string` + + The object identifier, which can be referenced in the API endpoints. + + - `created_at: number` + + The Unix timestamp (in seconds) for when the fine-tuning job was created. + + - `error: object { code, message, param }` + + For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure. + + - `code: string` + + A machine-readable error code. + + - `message: string` + + A human-readable error message. + + - `param: string` + + The parameter that was invalid, usually `training_file` or `validation_file`. This field will be null if the failure was not parameter-specific. + + - `fine_tuned_model: string` + + The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running. + + - `finished_at: number` + + 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: object { batch_size, learning_rate_multiplier, n_epochs }` + + The hyperparameters used for the fine-tuning job. This value will only be returned when running `supervised` jobs. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters + are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid + overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle + through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `model: string` + + The base model that is being fine-tuned. + + - `object: "fine_tuning.job"` + + The object type, which is always "fine_tuning.job". + + - `"fine_tuning.job"` + + - `organization_id: string` + + The organization that owns the fine-tuning job. + + - `result_files: array of string` + + The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `seed: number` + + The seed used for the fine-tuning job. + + - `status: "validating_files" or "queued" or "running" or 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: number` + + 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: string` + + The file ID used for training. You can retrieve the training data with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `validation_file: string` + + The file ID used for validation. You can retrieve the validation results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `estimated_finish: optional number` + + 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 array of FineTuningJobWandbIntegrationObject` + + A list of integrations to enable for this fine-tuning job. + + - `type: "wandb"` + + The type of the integration being enabled for the fine-tuning job + + - `"wandb"` + + - `wandb: FineTuningJobWandbIntegration` + + The settings for your integration with Weights and Biases. This payload specifies the project that + metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags + to your run, and set a default entity (team, username, etc) to be associated with your run. + + - `project: string` + + The name of the project that the new run will be created under. + + - `entity: optional string` + + The entity to use for the run. This allows you to set the team or username of the WandB user that you would + like associated with the run. If not set, the default entity for the registered WandB API key is used. + + - `name: optional string` + + A display name to set for the run. If not set, we will use the Job ID as the name. + + - `tags: optional array of string` + + A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some + default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". + + - `metadata: 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 object { type, dpo, reinforcement, supervised }` + + The method used for fine-tuning. + + - `type: "supervised" or "dpo" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `beta: optional "auto" or number` + + The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reinforcement: optional ReinforcementMethod` + + Configuration for the reinforcement fine-tuning method. + + - `grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + The grader used for the fine-tuning job. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `input: string` + + The input text. This may include template strings. + + - `name: string` + + The name of the grader. + + - `operation: "eq" or "ne" or "like" or "ilike"` + + The string check operation to perform. One of `eq`, `ne`, `like`, or `ilike`. + + - `"eq"` + + - `"ne"` + + - `"like"` + + - `"ilike"` + + - `reference: string` + + The reference text. This may include template strings. + + - `type: "string_check"` + + The object type, which is always `string_check`. + + - `"string_check"` + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 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: string` + + The text being graded. + + - `name: string` + + The name of the grader. + + - `reference: string` + + The text being graded against. + + - `type: "text_similarity"` + + The type of grader. + + - `"text_similarity"` + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `name: string` + + The name of the grader. + + - `source: string` + + The source code of the python script. + + - `type: "python"` + + The object type, which is always `python`. + + - `"python"` + + - `image_tag: optional string` + + The image tag to use for the python script. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `input: array of object { content, role, type }` + + The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings. + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `text: string` + + The text input to the model. + + - `type: "input_text"` + + The type of the input item. Always `input_text`. + + - `"input_text"` + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `input_audio: object { data, format }` + + - `data: string` + + Base64-encoded audio data. + + - `format: "mp3" or "wav"` + + The format of the audio data. Currently supported formats are `mp3` and + `wav`. + + - `"mp3"` + + - `"wav"` + + - `type: "input_audio"` + + The type of the input item. Always `input_audio`. + + - `"input_audio"` + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `model: string` + + The model to use for the evaluation. + + - `name: string` + + The name of the grader. + + - `type: "score_model"` + + The object type, which is always `score_model`. + + - `"score_model"` + + - `range: optional array of number` + + The range of the score. Defaults to `[0, 1]`. + + - `sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }` + + The sampling parameters for the model. + + - `max_completions_tokens: optional number` + + 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 number` + + A seed value to initialize the randomness, during sampling. + + - `temperature: optional number` + + A higher temperature increases randomness in the outputs. + + - `top_p: optional number` + + An alternative to temperature for nucleus sampling; 1.0 includes all tokens. + + - `MultiGrader object { calculate_output, graders, name, type }` + + A MultiGrader object combines the output of multiple graders to produce a single score. + + - `calculate_output: string` + + A formula to calculate the output based on grader results. + + - `graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `LabelModelGrader object { input, labels, model, 3 more }` + + A LabelModelGrader object which uses a model to assign labels to each item + in the evaluation. + + - `input: array of object { content, role, type }` + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `labels: array of string` + + The labels to assign to each item in the evaluation. + + - `model: string` + + The model to use for the evaluation. Must support structured outputs. + + - `name: string` + + The name of the grader. + + - `passing_labels: array of string` + + The labels that indicate a passing result. Must be a subset of labels. + + - `type: "label_model"` + + The object type, which is always `label_model`. + + - `"label_model"` + + - `name: string` + + The name of the grader. + + - `type: "multi"` + + The object type, which is always `multi`. + + - `"multi"` + + - `hyperparameters: optional ReinforcementHyperparameters` + + The hyperparameters used for the reinforcement fine-tuning job. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `compute_multiplier: optional "auto" or number` + + Multiplier on amount of compute used for exploring search space during training. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_interval: optional "auto" or number` + + The number of training steps between evaluation runs. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_samples: optional "auto" or number` + + Number of evaluation samples to generate per training step. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reasoning_effort: optional "default" or "low" or "medium" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + +### Example + +```http +curl https://api.openai.com/v1/fine_tuning/jobs/$FINE_TUNING_JOB_ID/resume \ + -X POST \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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 + +```http +curl -X POST https://api.openai.com/v1/fine_tuning/jobs/ftjob-abc123/resume \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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 + +- `FineTuningJob object { id, created_at, error, 16 more }` + + The `fine_tuning.job` object represents a fine-tuning job that has been created through the API. + + - `id: string` + + The object identifier, which can be referenced in the API endpoints. + + - `created_at: number` + + The Unix timestamp (in seconds) for when the fine-tuning job was created. + + - `error: object { code, message, param }` + + For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure. + + - `code: string` + + A machine-readable error code. + + - `message: string` + + A human-readable error message. + + - `param: string` + + The parameter that was invalid, usually `training_file` or `validation_file`. This field will be null if the failure was not parameter-specific. + + - `fine_tuned_model: string` + + The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running. + + - `finished_at: number` + + 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: object { batch_size, learning_rate_multiplier, n_epochs }` + + The hyperparameters used for the fine-tuning job. This value will only be returned when running `supervised` jobs. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters + are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid + overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle + through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `model: string` + + The base model that is being fine-tuned. + + - `object: "fine_tuning.job"` + + The object type, which is always "fine_tuning.job". + + - `"fine_tuning.job"` + + - `organization_id: string` + + The organization that owns the fine-tuning job. + + - `result_files: array of string` + + The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `seed: number` + + The seed used for the fine-tuning job. + + - `status: "validating_files" or "queued" or "running" or 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: number` + + 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: string` + + The file ID used for training. You can retrieve the training data with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `validation_file: string` + + The file ID used for validation. You can retrieve the validation results with the [Files API](/docs/api-reference/files/retrieve-contents). + + - `estimated_finish: optional number` + + 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 array of FineTuningJobWandbIntegrationObject` + + A list of integrations to enable for this fine-tuning job. + + - `type: "wandb"` + + The type of the integration being enabled for the fine-tuning job + + - `"wandb"` + + - `wandb: FineTuningJobWandbIntegration` + + The settings for your integration with Weights and Biases. This payload specifies the project that + metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags + to your run, and set a default entity (team, username, etc) to be associated with your run. + + - `project: string` + + The name of the project that the new run will be created under. + + - `entity: optional string` + + The entity to use for the run. This allows you to set the team or username of the WandB user that you would + like associated with the run. If not set, the default entity for the registered WandB API key is used. + + - `name: optional string` + + A display name to set for the run. If not set, we will use the Job ID as the name. + + - `tags: optional array of string` + + A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some + default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". + + - `metadata: 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 object { type, dpo, reinforcement, supervised }` + + The method used for fine-tuning. + + - `type: "supervised" or "dpo" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `beta: optional "auto" or number` + + The beta value for the DPO method. A higher beta value will increase the weight of the penalty between the policy and reference model. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reinforcement: optional ReinforcementMethod` + + Configuration for the reinforcement fine-tuning method. + + - `grader: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + The grader used for the fine-tuning job. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `input: string` + + The input text. This may include template strings. + + - `name: string` + + The name of the grader. + + - `operation: "eq" or "ne" or "like" or "ilike"` + + The string check operation to perform. One of `eq`, `ne`, `like`, or `ilike`. + + - `"eq"` + + - `"ne"` + + - `"like"` + + - `"ilike"` + + - `reference: string` + + The reference text. This may include template strings. + + - `type: "string_check"` + + The object type, which is always `string_check`. + + - `"string_check"` + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `evaluation_metric: "cosine" or "fuzzy_match" or "bleu" or 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: string` + + The text being graded. + + - `name: string` + + The name of the grader. + + - `reference: string` + + The text being graded against. + + - `type: "text_similarity"` + + The type of grader. + + - `"text_similarity"` + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `name: string` + + The name of the grader. + + - `source: string` + + The source code of the python script. + + - `type: "python"` + + The object type, which is always `python`. + + - `"python"` + + - `image_tag: optional string` + + The image tag to use for the python script. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `input: array of object { content, role, type }` + + The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings. + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `text: string` + + The text input to the model. + + - `type: "input_text"` + + The type of the input item. Always `input_text`. + + - `"input_text"` + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `input_audio: object { data, format }` + + - `data: string` + + Base64-encoded audio data. + + - `format: "mp3" or "wav"` + + The format of the audio data. Currently supported formats are `mp3` and + `wav`. + + - `"mp3"` + + - `"wav"` + + - `type: "input_audio"` + + The type of the input item. Always `input_audio`. + + - `"input_audio"` + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `model: string` + + The model to use for the evaluation. + + - `name: string` + + The name of the grader. + + - `type: "score_model"` + + The object type, which is always `score_model`. + + - `"score_model"` + + - `range: optional array of number` + + The range of the score. Defaults to `[0, 1]`. + + - `sampling_params: optional object { max_completions_tokens, reasoning_effort, seed, 2 more }` + + The sampling parameters for the model. + + - `max_completions_tokens: optional number` + + 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 number` + + A seed value to initialize the randomness, during sampling. + + - `temperature: optional number` + + A higher temperature increases randomness in the outputs. + + - `top_p: optional number` + + An alternative to temperature for nucleus sampling; 1.0 includes all tokens. + + - `MultiGrader object { calculate_output, graders, name, type }` + + A MultiGrader object combines the output of multiple graders to produce a single score. + + - `calculate_output: string` + + A formula to calculate the output based on grader results. + + - `graders: StringCheckGrader or TextSimilarityGrader or PythonGrader or 2 more` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `StringCheckGrader object { input, name, operation, 2 more }` + + A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. + + - `TextSimilarityGrader object { evaluation_metric, input, name, 2 more }` + + A TextSimilarityGrader object which grades text based on similarity metrics. + + - `PythonGrader object { name, source, type, image_tag }` + + A PythonGrader object that runs a python script on the input. + + - `ScoreModelGrader object { input, model, name, 3 more }` + + A ScoreModelGrader object that uses a model to assign a score to the input. + + - `LabelModelGrader object { input, labels, model, 3 more }` + + A LabelModelGrader object which uses a model to assign labels to each item + in the evaluation. + + - `input: array of object { content, role, type }` + + - `content: string or ResponseInputText or object { text, type } or 3 more` + + 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. + + - `TextInput = string` + + A text input to the model. + + - `ResponseInputText object { text, type }` + + A text input to the model. + + - `OutputText object { text, type }` + + A text output from the model. + + - `text: string` + + The text output from the model. + + - `type: "output_text"` + + The type of the output text. Always `output_text`. + + - `"output_text"` + + - `InputImage object { image_url, type, detail }` + + An image input block used within EvalItem content arrays. + + - `image_url: string` + + The URL of the image input. + + - `type: "input_image"` + + The type of the image input. Always `input_image`. + + - `"input_image"` + + - `detail: optional string` + + The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`. + + - `ResponseInputAudio object { input_audio, type }` + + An audio input to the model. + + - `GraderInputs = array of string or ResponseInputText or object { text, type } or 2 more` + + A list of inputs, each of which may be either an input text, output text, input + image, or input audio object. + + - `role: "user" or "assistant" or "system" or "developer"` + + The role of the message input. One of `user`, `assistant`, `system`, or + `developer`. + + - `"user"` + + - `"assistant"` + + - `"system"` + + - `"developer"` + + - `type: optional "message"` + + The type of the message input. Always `message`. + + - `"message"` + + - `labels: array of string` + + The labels to assign to each item in the evaluation. + + - `model: string` + + The model to use for the evaluation. Must support structured outputs. + + - `name: string` + + The name of the grader. + + - `passing_labels: array of string` + + The labels that indicate a passing result. Must be a subset of labels. + + - `type: "label_model"` + + The object type, which is always `label_model`. + + - `"label_model"` + + - `name: string` + + The name of the grader. + + - `type: "multi"` + + The object type, which is always `multi`. + + - `"multi"` + + - `hyperparameters: optional ReinforcementHyperparameters` + + The hyperparameters used for the reinforcement fine-tuning job. + + - `batch_size: optional "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `compute_multiplier: optional "auto" or number` + + Multiplier on amount of compute used for exploring search space during training. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_interval: optional "auto" or number` + + The number of training steps between evaluation runs. + + - `"auto"` + + - `"auto"` + + - `number` + + - `eval_samples: optional "auto" or number` + + Number of evaluation samples to generate per training step. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + + - `reasoning_effort: optional "default" or "low" or "medium" or "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 "auto" or number` + + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + + - `"auto"` + + - `"auto"` + + - `number` + + - `learning_rate_multiplier: optional "auto" or number` + + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + - `"auto"` + + - `"auto"` + + - `number` + + - `n_epochs: optional "auto" or number` + + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + + - `"auto"` + + - `"auto"` + + - `number` + +### Fine Tuning Job Event + +- `FineTuningJobEvent object { id, created_at, level, 4 more }` + + Fine-tuning job event object + + - `id: string` + + The object identifier. + + - `created_at: number` + + The Unix timestamp (in seconds) for when the fine-tuning job was created. + + - `level: "info" or "warn" or "error"` + + The log level of the event. + + - `"info"` + + - `"warn"` + + - `"error"` + + - `message: string` + + The message of the event. + + - `object: "fine_tuning.job.event"` + + The object type, which is always "fine_tuning.job.event". + + - `"fine_tuning.job.event"` + + - `data: optional unknown` + + The data associated with the event. + + - `type: optional "message" or "metrics"` + + The type of event. + + - `"message"` + + - `"metrics"` + +### Fine Tuning Job Wandb Integration + +- `FineTuningJobWandbIntegration object { project, entity, name, tags }` + + The settings for your integration with Weights and Biases. This payload specifies the project that + metrics will be sent to. Optionally, you can set an explicit display name for your run, add tags + to your run, and set a default entity (team, username, etc) to be associated with your run. + + - `project: string` + + The name of the project that the new run will be created under. + + - `entity: optional string` + + The entity to use for the run. This allows you to set the team or username of the WandB user that you would + like associated with the run. If not set, the default entity for the registered WandB API key is used. + + - `name: optional string` + + A display name to set for the run. If not set, we will use the Job ID as the name. + + - `tags: optional array of string` + + A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some + default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". + +### Fine Tuning Job Wandb Integration Object + +- `FineTuningJobWandbIntegrationObject object { type, wandb }` + + - `type: "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: string` + + The name of the project that the new run will be created under. + + - `entity: optional string` + + The entity to use for the run. This allows you to set the team or username of the WandB user that you would + like associated with the run. If not set, the default entity for the registered WandB API key is used. + + - `name: optional string` + + A display name to set for the run. If not set, we will use the Job ID as the name. + + - `tags: optional array of string` + + A list of tags to be attached to the newly created run. These tags are passed through directly to WandB. Some + default tags are generated by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}". + +# Checkpoints + +## List fine-tuning checkpoints + +**get** `/fine_tuning/jobs/{fine_tuning_job_id}/checkpoints` + +List checkpoints for a fine-tuning job. + +### Path Parameters + +- `fine_tuning_job_id: string` + +### Query Parameters + +- `after: optional string` + + Identifier for the last checkpoint ID from the previous pagination request. + +- `limit: optional number` + + Number of checkpoints to retrieve. + +### Returns + +- `data: array of FineTuningJobCheckpoint` + + - `id: string` + + The checkpoint identifier, which can be referenced in the API endpoints. + + - `created_at: number` + + The Unix timestamp (in seconds) for when the checkpoint was created. + + - `fine_tuned_model_checkpoint: string` + + The name of the fine-tuned checkpoint model that is created. + + - `fine_tuning_job_id: string` + + The name of the fine-tuning job that this checkpoint was created from. + + - `metrics: object { full_valid_loss, full_valid_mean_token_accuracy, step, 4 more }` + + Metrics at the step number during the fine-tuning job. + + - `full_valid_loss: optional number` + + - `full_valid_mean_token_accuracy: optional number` + + - `step: optional number` + + - `train_loss: optional number` + + - `train_mean_token_accuracy: optional number` + + - `valid_loss: optional number` + + - `valid_mean_token_accuracy: optional number` + + - `object: "fine_tuning.job.checkpoint"` + + The object type, which is always "fine_tuning.job.checkpoint". + + - `"fine_tuning.job.checkpoint"` + + - `step_number: number` + + The step number that the checkpoint was created at. + +- `has_more: boolean` + +- `object: "list"` + + - `"list"` + +- `first_id: optional string` + +- `last_id: optional string` + +### Example + +```http +curl https://api.openai.com/v1/fine_tuning/jobs/$FINE_TUNING_JOB_ID/checkpoints \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### 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" +} +``` + +### Example + +```http +curl https://api.openai.com/v1/fine_tuning/jobs/ftjob-abc123/checkpoints \ + -H "Authorization: Bearer $OPENAI_API_KEY" +``` + +#### Response + +```json +{ + "object": "list", + "data": [ + { + "object": "fine_tuning.job.checkpoint", + "id": "ftckpt_zc4Q7MP6XxulcVzj4MZdwsAB", + "created_at": 1721764867, + "fine_tuned_model_checkpoint": "ft:gpt-4o-mini-2024-07-18:my-org:custom-suffix:96olL566:ckpt-step-2000", + "metrics": { + "full_valid_loss": 0.134, + "full_valid_mean_token_accuracy": 0.874 + }, + "fine_tuning_job_id": "ftjob-abc123", + "step_number": 2000 + }, + { + "object": "fine_tuning.job.checkpoint", + "id": "ftckpt_enQCFmOTGj3syEpYVhBRLTSy", + "created_at": 1721764800, + "fine_tuned_model_checkpoint": "ft:gpt-4o-mini-2024-07-18:my-org:custom-suffix:7q8mpxmy:ckpt-step-1000", + "metrics": { + "full_valid_loss": 0.167, + "full_valid_mean_token_accuracy": 0.781 + }, + "fine_tuning_job_id": "ftjob-abc123", + "step_number": 1000 + } + ], + "first_id": "ftckpt_zc4Q7MP6XxulcVzj4MZdwsAB", + "last_id": "ftckpt_enQCFmOTGj3syEpYVhBRLTSy", + "has_more": true +} +``` + +## Domain Types + +### Fine Tuning Job Checkpoint + +- `FineTuningJobCheckpoint object { id, created_at, fine_tuned_model_checkpoint, 4 more }` + + The `fine_tuning.job.checkpoint` object represents a model checkpoint for a fine-tuning job that is ready to use. + + - `id: string` + + The checkpoint identifier, which can be referenced in the API endpoints. + + - `created_at: number` + + The Unix timestamp (in seconds) for when the checkpoint was created. + + - `fine_tuned_model_checkpoint: string` + + The name of the fine-tuned checkpoint model that is created. + + - `fine_tuning_job_id: string` + + The name of the fine-tuning job that this checkpoint was created from. + + - `metrics: object { full_valid_loss, full_valid_mean_token_accuracy, step, 4 more }` + + Metrics at the step number during the fine-tuning job. + + - `full_valid_loss: optional number` + + - `full_valid_mean_token_accuracy: optional number` + + - `step: optional number` + + - `train_loss: optional number` + + - `train_mean_token_accuracy: optional number` + + - `valid_loss: optional number` + + - `valid_mean_token_accuracy: optional number` + + - `object: "fine_tuning.job.checkpoint"` + + The object type, which is always "fine_tuning.job.checkpoint". + + - `"fine_tuning.job.checkpoint"` + + - `step_number: number` + + The step number that the checkpoint was created at.