--- title: "List your organization's fine-tuning jobs" method: GET path: "/fine_tuning/jobs" tags: ["Fine-tuning"] --- # List your organization's fine-tuning jobs `GET /fine_tuning/jobs` ## Query parameters - `after` string - `limit` integer - `metadata` object, nullable ## Response `200` OK - ListPaginatedFineTuningJobsResponse - `data` FineTuningJob[], required - `id` string, required — The object identifier, which can be referenced in the API endpoints. - `created_at` integer, required — The Unix timestamp (in seconds) for when the fine-tuning job was created. - `error` object, nullable, required — For fine-tuning jobs that have `failed`, this will contain more information on the cause of the failure. - `code` string, required — A machine-readable error code. - `message` string, required — A human-readable error message. - `param` string, nullable, required — 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, nullable, required — 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` integer, nullable, required — 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, required — The hyperparameters used for the fine-tuning job. This value will only be returned when running `supervised` jobs. - `batch_size` union - 'auto' - integer - `learning_rate_multiplier` union — Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. - 'auto' - number - `n_epochs` union — The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. - 'auto' - integer - `model` string, required — The base model that is being fine-tuned. - `object` 'fine_tuning.job', required — The object type, which is always "fine_tuning.job". - `organization_id` string, required — The organization that owns the fine-tuning job. - `result_files` string[], required — 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). - `status` 'validating_files' | 'queued' | 'running' | 'succeeded' | 'failed' | 'cancelled', required — The current status of the fine-tuning job, which can be either `validating_files`, `queued`, `running`, `succeeded`, `failed`, or `cancelled`. - `trained_tokens` integer, nullable, required — 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, required — 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, nullable, required — The file ID used for validation. You can retrieve the validation results with the [Files API](/docs/api-reference/files/retrieve-contents). - `integrations` FineTuningIntegration[], nullable — A list of integrations to enable for this fine-tuning job. - `type` 'wandb', required — The type of the integration being enabled for the fine-tuning job - `wandb` object, required — 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, required — The name of the project that the new run will be created under. - `name` string, nullable — A display name to set for the run. If not set, we will use the Job ID as the name. - `entity` string, nullable — 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. - `tags` 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}". - `seed` integer, required — The seed used for the fine-tuning job. - `estimated_finish` integer, nullable — 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. - `method` FineTuneMethod — The method used for fine-tuning. - `type` 'supervised' | 'dpo' | 'reinforcement', required — The type of method. Is either `supervised`, `dpo`, or `reinforcement`. - `supervised` FineTuneSupervisedMethod — Configuration for the supervised fine-tuning method. - `hyperparameters` FineTuneSupervisedHyperparameters — The hyperparameters used for the fine-tuning job. - `batch_size` union — Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. - 'auto' - integer - `learning_rate_multiplier` union — Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. - 'auto' - number - `n_epochs` union — The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. - 'auto' - integer - `dpo` FineTuneDPOMethod — Configuration for the DPO fine-tuning method. - `hyperparameters` FineTuneDPOHyperparameters — The hyperparameters used for the DPO fine-tuning job. - `beta` union — 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' - number - `batch_size` union — Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. - 'auto' - integer - `learning_rate_multiplier` union — Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. - 'auto' - number - `n_epochs` union — The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. - 'auto' - integer - `reinforcement` FineTuneReinforcementMethod — Configuration for the reinforcement fine-tuning method. - `grader` union, required — The grader used for the fine-tuning job. - object — A StringCheckGrader object that performs a string comparison between input and reference using a specified operation. - `type` 'string_check', required — The object type, which is always `string_check`. - `name` string, required — The name of the grader. - `input` string, required — The input text. This may include template strings. - `reference` string, required — The reference text. This may include template strings. - `operation` 'eq' | 'ne' | 'like' | 'ilike', required — The string check operation to perform. One of `eq`, `ne`, `like`, or `ilike`. - object — A TextSimilarityGrader object which grades text based on similarity metrics. - `type` 'text_similarity', required — The type of grader. - `name` string, required — The name of the grader. - `input` string, required — The text being graded. - `reference` string, required — The text being graded against. - `evaluation_metric` 'cosine' | 'fuzzy_match' | 'bleu' | 'gleu' | 'meteor' | 'rouge_1' | 'rouge_2' | 'rouge_3' | 'rouge_4' | 'rouge_5' | 'rouge_l', required — 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`. - object — A PythonGrader object that runs a python script on the input. - `type` 'python', required — The object type, which is always `python`. - `name` string, required — The name of the grader. - `source` string, required — The source code of the python script. - `image_tag` string — The image tag to use for the python script. - object — A ScoreModelGrader object that uses a model to assign a score to the input. - `type` 'score_model', required — The object type, which is always `score_model`. - `name` string, required — The name of the grader. - `model` string, required — The model to use for the evaluation. - `sampling_params` object — The sampling parameters for the model. - … - `input` EvalItem[], required — The input messages evaluated by the grader. Supports text, output text, input image, and input audio content blocks, and may include template strings. - … - `range` number[] — The range of the score. Defaults to `[0, 1]`. - object — A MultiGrader object combines the output of multiple graders to produce a single score. - `type` 'multi', required — The object type, which is always `multi`. - `name` string, required — The name of the grader. - `graders` union, required - … - `calculate_output` string, required — A formula to calculate the output based on grader results. - `hyperparameters` FineTuneReinforcementHyperparameters — The hyperparameters used for the reinforcement fine-tuning job. - `batch_size` union — Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. - 'auto' - integer - `learning_rate_multiplier` union — Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. - 'auto' - number - `n_epochs` union — The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. - 'auto' - integer - `reasoning_effort` 'default' | 'low' | 'medium' | 'high' — Level of reasoning effort. - `compute_multiplier` union — Multiplier on amount of compute used for exploring search space during training. - 'auto' - number - `eval_interval` union — The number of training steps between evaluation runs. - 'auto' - integer - `eval_samples` union — Number of evaluation samples to generate per training step. - 'auto' - integer - `metadata` Metadata, nullable — 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. - `has_more` boolean, required - `object` 'list', required --- [API](https://skmtc.net/openai/apis/openai-api-3.md) · [All operations](https://skmtc.net/openai/apis/openai-api-3/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/openai/openai-api-3/versions/74cbcf73838f/schema)