Fine Tuning Jobs — List

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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] or null

  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 } or null

    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 or null

      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 or null

    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 or null

    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 or null

      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↗.

  • 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 or null

    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↗.

  • validation_file: string or null

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

  • estimated_finish: optional number or null

    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 or null

    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 or null

        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 or null

        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 or null

    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, prompt_cache_breakpoint }

                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"

                • prompt_cache_breakpoint: optional object { mode }

                  Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

                  • mode: "explicit"

                    The breakpoint mode. Always explicit.

                    • "explicit"

              • 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, prompt_cache_breakpoint }

                  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 or null

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

            • reasoning_effort: optional ReasoningEffort or null

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

              • "none"

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

              • "max"

            • seed: optional number or null

              A seed value to initialize the randomness, during sampling.

            • temperature: optional number or null

              A higher temperature increases randomness in the outputs.

            • top_p: optional number or null

              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, prompt_cache_breakpoint }

                    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

curl https://api.openai.com/v1/fine_tuning/jobs \
    -H "Authorization: Bearer $OPENAI_API_KEY"

Response

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

Example

curl https://api.openai.com/v1/fine_tuning/jobs?limit=2&metadata[key]=value \
  -H "Authorization: Bearer $OPENAI_API_KEY"

Response

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