OpenAI Fine Tuning API

openapi: 3.2.0
info:
  title: Openai Fine Tuning API
  contact:
    name: OpenAI Support
    url: https://help.openai.com/
  license:
    name: MIT
    url: https://github.com/openai/openai-openapi/blob/master/LICENSE
  termsOfService: https://openai.com/policies/terms-of-use
  version: '1.0'
  description: 'Operations tagged Fine Tuning across 2 of this provider''s published API definitions: fine-tuning-openapi-original.yml, openai-openapi-master.yml. Each path carries the servers of the definition it was published in.'
servers:
- url: https://api.openai.com/v1
security:
- ApiKeyAuth: []
tags:
- name: Fine Tuning
paths:
  /fine_tuning/jobs:
    post:
      operationId: createFineTuningJob
      tags:
      - Fine Tuning
      summary: 'OpenAI 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/fine-tuning)'
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/CreateFineTuningJobRequest'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/FineTuningJob'
      x-oaiMeta:
        name: Create fine-tuning job
        group: fine-tuning
        returns: A [fine-tuning.job](/docs/api-reference/fine-tuning/object) object.
        examples:
        - title: Default
          request:
            curl: "curl https://api.openai.com/v1/fine_tuning/jobs \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n  -d '{\n    \"training_file\": \"file-BK7bzQj3FfZFXr7DbL6xJwfo\",\n    \"model\": \"gpt-3.5-turbo\"\n  }'\n"
            python: "from openai import OpenAI\nclient = OpenAI()\n\nclient.fine_tuning.jobs.create(\n  training_file=\"file-abc123\",\n  model=\"gpt-3.5-turbo\"\n)\n"
            node.js: "import OpenAI from \"openai\";\n\nconst openai = new OpenAI();\n\nasync function main() {\n  const fineTune = await openai.fineTuning.jobs.create({\n    training_file: \"file-abc123\"\n  });\n\n  console.log(fineTune);\n}\n\nmain();\n"
          response: "{\n  \"object\": \"fine_tuning.job\",\n  \"id\": \"ftjob-abc123\",\n  \"model\": \"gpt-3.5-turbo-0613\",\n  \"created_at\": 1614807352,\n  \"fine_tuned_model\": null,\n  \"organization_id\": \"org-123\",\n  \"result_files\": [],\n  \"status\": \"queued\",\n  \"validation_file\": null,\n  \"training_file\": \"file-abc123\",\n}\n"
        - title: Epochs
          request:
            curl: "curl https://api.openai.com/v1/fine_tuning/jobs \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n  -d '{\n    \"training_file\": \"file-abc123\",\n    \"model\": \"gpt-3.5-turbo\",\n    \"hyperparameters\": {\n      \"n_epochs\": 2\n    }\n  }'\n"
            python: "from openai import OpenAI\nclient = OpenAI()\n\nclient.fine_tuning.jobs.create(\n  training_file=\"file-abc123\",\n  model=\"gpt-3.5-turbo\",\n  hyperparameters={\n    \"n_epochs\":2\n  }\n)\n"
            node.js: "import OpenAI from \"openai\";\n\nconst openai = new OpenAI();\n\nasync function main() {\n  const fineTune = await openai.fineTuning.jobs.create({\n    training_file: \"file-abc123\",\n    model: \"gpt-3.5-turbo\",\n    hyperparameters: { n_epochs: 2 }\n  });\n\n  console.log(fineTune);\n}\n\nmain();\n"
          response: "{\n  \"object\": \"fine_tuning.job\",\n  \"id\": \"ftjob-abc123\",\n  \"model\": \"gpt-3.5-turbo-0613\",\n  \"created_at\": 1614807352,\n  \"fine_tuned_model\": null,\n  \"organization_id\": \"org-123\",\n  \"result_files\": [],\n  \"status\": \"queued\",\n  \"validation_file\": null,\n  \"training_file\": \"file-abc123\",\n  \"hyperparameters\": {\"n_epochs\": 2},\n}\n"
        - title: Validation file
          request:
            curl: "curl https://api.openai.com/v1/fine_tuning/jobs \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n  -d '{\n    \"training_file\": \"file-abc123\",\n    \"validation_file\": \"file-abc123\",\n    \"model\": \"gpt-3.5-turbo\"\n  }'\n"
            python: "from openai import OpenAI\nclient = OpenAI()\n\nclient.fine_tuning.jobs.create(\n  training_file=\"file-abc123\",\n  validation_file=\"file-def456\",\n  model=\"gpt-3.5-turbo\"\n)\n"
            node.js: "import OpenAI from \"openai\";\n\nconst openai = new OpenAI();\n\nasync function main() {\n  const fineTune = await openai.fineTuning.jobs.create({\n    training_file: \"file-abc123\",\n    validation_file: \"file-abc123\"\n  });\n\n  console.log(fineTune);\n}\n\nmain();\n"
          response: "{\n  \"object\": \"fine_tuning.job\",\n  \"id\": \"ftjob-abc123\",\n  \"model\": \"gpt-3.5-turbo-0613\",\n  \"created_at\": 1614807352,\n  \"fine_tuned_model\": null,\n  \"organization_id\": \"org-123\",\n  \"result_files\": [],\n  \"status\": \"queued\",\n  \"validation_file\": \"file-abc123\",\n  \"training_file\": \"file-abc123\",\n}\n"
    get:
      operationId: listPaginatedFineTuningJobs
      tags:
      - Fine Tuning
      summary: OpenAI List your organization's fine-tuning jobs
      parameters:
      - name: after
        in: query
        description: Identifier for the last job from the previous pagination request.
        required: false
        schema:
          type: string
      - name: limit
        in: query
        description: Number of fine-tuning jobs to retrieve.
        required: false
        schema:
          type: integer
          default: 20
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ListPaginatedFineTuningJobsResponse'
      x-oaiMeta:
        name: List fine-tuning jobs
        group: fine-tuning
        returns: A list of paginated [fine-tuning job](/docs/api-reference/fine-tuning/object) objects.
        examples:
          request:
            curl: "curl https://api.openai.com/v1/fine_tuning/jobs?limit=2 \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\"\n"
            python: 'from openai import OpenAI

              client = OpenAI()


              client.fine_tuning.jobs.list()

              '
            node.js: "import OpenAI from \"openai\";\n\nconst openai = new OpenAI();\n\nasync function main() {\n  const list = await openai.fineTuning.jobs.list();\n\n  for await (const fineTune of list) {\n    console.log(fineTune);\n  }\n}\n\nmain();"
          response: "{\n  \"object\": \"list\",\n  \"data\": [\n    {\n      \"object\": \"fine_tuning.job.event\",\n      \"id\": \"ft-event-TjX0lMfOniCZX64t9PUQT5hn\",\n      \"created_at\": 1689813489,\n      \"level\": \"warn\",\n      \"message\": \"Fine tuning process stopping due to job cancellation\",\n      \"data\": null,\n      \"type\": \"message\"\n    },\n    { ... },\n    { ... }\n  ], \"has_more\": true\n}\n"
    servers:
    - url: https://api.openai.com/v1
  /fine_tuning/jobs/{fine_tuning_job_id}:
    get:
      operationId: retrieveFineTuningJob
      tags:
      - Fine Tuning
      summary: 'OpenAI Get info about a fine-tuning job.


        [Learn more about fine-tuning](/docs/guides/fine-tuning)'
      parameters:
      - in: path
        name: fine_tuning_job_id
        required: true
        schema:
          type: string
          example: ft-AF1WoRqd3aJAHsqc9NY7iL8F
        description: 'The ID of the fine-tuning job.

          '
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/FineTuningJob'
      x-oaiMeta:
        name: Retrieve fine-tuning job
        group: fine-tuning
        returns: The [fine-tuning](/docs/api-reference/fine-tuning/object) object with the given ID.
        examples:
          request:
            curl: "curl https://api.openai.com/v1/fine_tuning/jobs/ft-AF1WoRqd3aJAHsqc9NY7iL8F \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\"\n"
            python: 'from openai import OpenAI

              client = OpenAI()


              client.fine_tuning.jobs.retrieve("ftjob-abc123")

              '
            node.js: "import OpenAI from \"openai\";\n\nconst openai = new OpenAI();\n\nasync function main() {\n  const fineTune = await openai.fineTuning.jobs.retrieve(\"ftjob-abc123\");\n\n  console.log(fineTune);\n}\n\nmain();\n"
          response: "{\n  \"object\": \"fine_tuning.job\",\n  \"id\": \"ftjob-abc123\",\n  \"model\": \"davinci-002\",\n  \"created_at\": 1692661014,\n  \"finished_at\": 1692661190,\n  \"fine_tuned_model\": \"ft:davinci-002:my-org:custom_suffix:7q8mpxmy\",\n  \"organization_id\": \"org-123\",\n  \"result_files\": [\n      \"file-abc123\"\n  ],\n  \"status\": \"succeeded\",\n  \"validation_file\": null,\n  \"training_file\": \"file-abc123\",\n  \"hyperparameters\": {\n      \"n_epochs\": 4,\n  },\n  \"trained_tokens\": 5768\n}\n"
    servers:
    - url: https://api.openai.com/v1
  /fine_tuning/jobs/{fine_tuning_job_id}/events:
    get:
      operationId: listFineTuningEvents
      tags:
      - Fine Tuning
      summary: OpenAI Get status updates for a fine-tuning job.
      parameters:
      - in: path
        name: fine_tuning_job_id
        required: true
        schema:
          type: string
          example: ft-AF1WoRqd3aJAHsqc9NY7iL8F
        description: 'The ID of the fine-tuning job to get events for.

          '
      - name: after
        in: query
        description: Identifier for the last event from the previous pagination request.
        required: false
        schema:
          type: string
      - name: limit
        in: query
        description: Number of events to retrieve.
        required: false
        schema:
          type: integer
          default: 20
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ListFineTuningJobEventsResponse'
      x-oaiMeta:
        name: List fine-tuning events
        group: fine-tuning
        returns: A list of fine-tuning event objects.
        examples:
          request:
            curl: "curl https://api.openai.com/v1/fine_tuning/jobs/ftjob-abc123/events \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\"\n"
            python: "from openai import OpenAI\nclient = OpenAI()\n\nclient.fine_tuning.jobs.list_events(\n  fine_tuning_job_id=\"ftjob-abc123\",\n  limit=2\n)\n"
            node.js: "import OpenAI from \"openai\";\n\nconst openai = new OpenAI();\n\nasync function main() {\n  const list = await openai.fineTuning.list_events(id=\"ftjob-abc123\", limit=2);\n\n  for await (const fineTune of list) {\n    console.log(fineTune);\n  }\n}\n\nmain();"
          response: "{\n  \"object\": \"list\",\n  \"data\": [\n    {\n      \"object\": \"fine_tuning.job.event\",\n      \"id\": \"ft-event-ddTJfwuMVpfLXseO0Am0Gqjm\",\n      \"created_at\": 1692407401,\n      \"level\": \"info\",\n      \"message\": \"Fine tuning job successfully completed\",\n      \"data\": null,\n      \"type\": \"message\"\n    },\n    {\n      \"object\": \"fine_tuning.job.event\",\n      \"id\": \"ft-event-tyiGuB72evQncpH87xe505Sv\",\n      \"created_at\": 1692407400,\n      \"level\": \"info\",\n      \"message\": \"New fine-tuned model created: ft:gpt-3.5-turbo:openai::7p4lURel\",\n      \"data\": null,\n      \"type\": \"message\"\n    }\n  ],\n  \"has_more\": true\n}\n"
    servers:
    - url: https://api.openai.com/v1
  /fine_tuning/jobs/{fine_tuning_job_id}/cancel:
    post:
      operationId: cancelFineTuningJob
      tags:
      - Fine Tuning
      summary: OpenAI Immediately cancel a fine-tune job.
      parameters:
      - in: path
        name: fine_tuning_job_id
        required: true
        schema:
          type: string
          example: ft-AF1WoRqd3aJAHsqc9NY7iL8F
        description: 'The ID of the fine-tuning job to cancel.

          '
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/FineTuningJob'
      x-oaiMeta:
        name: Cancel fine-tuning
        group: fine-tuning
        returns: The cancelled [fine-tuning](/docs/api-reference/fine-tuning/object) object.
        examples:
          request:
            curl: "curl -X POST https://api.openai.com/v1/fine_tuning/jobs/ftjob-abc123/cancel \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\"\n"
            python: 'from openai import OpenAI

              client = OpenAI()


              client.fine_tuning.jobs.cancel("ftjob-abc123")

              '
            node.js: "import OpenAI from \"openai\";\n\nconst openai = new OpenAI();\n\nasync function main() {\n  const fineTune = await openai.fineTuning.jobs.cancel(\"ftjob-abc123\");\n\n  console.log(fineTune);\n}\nmain();"
          response: "{\n  \"object\": \"fine_tuning.job\",\n  \"id\": \"ftjob-abc123\",\n  \"model\": \"gpt-3.5-turbo-0613\",\n  \"created_at\": 1689376978,\n  \"fine_tuned_model\": null,\n  \"organization_id\": \"org-123\",\n  \"result_files\": [],\n  \"hyperparameters\": {\n    \"n_epochs\":  \"auto\"\n  },\n  \"status\": \"cancelled\",\n  \"validation_file\": \"file-abc123\",\n  \"training_file\": \"file-abc123\"\n}\n"
    servers:
    - url: https://api.openai.com/v1
  /fine_tuning/alpha/graders/run:
    post:
      operationId: runGrader
      tags:
      - Fine Tuning
      summary: 'Run a grader.

        '
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/RunGraderRequest'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/RunGraderResponse'
      x-oaiMeta:
        name: Run grader
        beta: true
        group: graders
        examples:
        - title: Score text alignment
          request:
            curl: "curl -X POST https://api.openai.com/v1/fine_tuning/alpha/graders/run \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n  -d '{\n    \"grader\": {\n      \"type\": \"score_model\",\n      \"name\": \"Example score model grader\",\n      \"input\": [\n        {\n          \"role\": \"user\",\n          \"content\": [\n            {\n              \"type\": \"input_text\",\n              \"text\": \"Score how close the reference answer is to the model answer on a 0-1 scale. Return only the score.\\n\\nReference answer: {{item.reference_answer}}\\n\\nModel answer: {{sample.output_text}}\"\n            }\n          ]\n        }\n      ],\n      \"model\": \"gpt-5-mini\",\n      \"sampling_params\": {\n        \"temperature\": 1,\n        \"top_p\": 1,\n        \"seed\": 42\n      }\n    },\n    \"item\": {\n      \"reference_answer\": \"fuzzy wuzzy was a bear\"\n    },\n    \"model_sample\": \"fuzzy wuzzy was a bear\"\n  }'\n"
            python: "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n    api_key=os.environ.get(\"OPENAI_API_KEY\"),  # This is the default and can be omitted\n)\nresponse = client.fine_tuning.alpha.graders.run(\n    grader={\n        \"input\": \"input\",\n        \"name\": \"name\",\n        \"operation\": \"eq\",\n        \"reference\": \"reference\",\n        \"type\": \"string_check\",\n    },\n    model_sample=\"model_sample\",\n)\nprint(response.metadata)"
            javascript: "import OpenAI from \"openai\";\n\nconst openai = new OpenAI();\n\nconst result = await openai.fineTuning.alpha.graders.run({\n  grader: {\n    type: \"score_model\",\n    name: \"Example score model grader\",\n    input: [\n      {\n        role: \"user\",\n        content: [\n          {\n            type: \"input_text\",\n            text: \"Score how close the reference answer is to the model answer on a 0-1 scale. Return only the score.\\n\\nReference answer: {{item.reference_answer}}\\n\\nModel answer: {{sample.output_text}}\",\n          },\n        ],\n      },\n    ],\n    model: \"gpt-5-mini\",\n    sampling_params: { temperature: 1, top_p: 1, seed: 42 },\n  },\n  item: { reference_answer: \"fuzzy wuzzy was a bear\" },\n  model_sample: \"fuzzy wuzzy was a bear\",\n});\nconsole.log(result);\n"
            node.js: "import OpenAI from 'openai';\n\nconst client = new OpenAI({\n  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted\n});\n\nconst response = await client.fineTuning.alpha.graders.run({\n  grader: {\n    input: 'input',\n    name: 'name',\n    operation: 'eq',\n    reference: 'reference',\n    type: 'string_check',\n  },\n  model_sample: 'model_sample',\n});\n\nconsole.log(response.metadata);"
            go: "package main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\n\t\"github.com/openai/openai-go\"\n\t\"github.com/openai/openai-go/option\"\n)\n\nfunc main() {\n\tclient := openai.NewClient(\n\t\toption.WithAPIKey(\"My API Key\"),\n\t)\n\tresponse, err := client.FineTuning.Alpha.Graders.Run(context.TODO(), openai.FineTuningAlphaGraderRunParams{\n\t\tGrader: openai.FineTuningAlphaGraderRunParamsGraderUnion{\n\t\t\tOfStringCheck: &openai.StringCheckGraderParam{\n\t\t\t\tInput:     \"input\",\n\t\t\t\tName:      \"name\",\n\t\t\t\tOperation: openai.StringCheckGraderOperationEq,\n\t\t\t\tReference: \"reference\",\n\t\t\t},\n\t\t},\n\t\tModelSample: \"model_sample\",\n\t})\n\tif err != nil {\n\t\tpanic(err.Error())\n\t}\n\tfmt.Printf(\"%+v\\n\", response.Metadata)\n}\n"
            java: "package com.openai.example;\n\nimport com.openai.client.OpenAIClient;\nimport com.openai.client.okhttp.OpenAIOkHttpClient;\nimport com.openai.models.finetuning.alpha.graders.GraderRunParams;\nimport com.openai.models.finetuning.alpha.graders.GraderRunResponse;\nimport com.openai.models.graders.gradermodels.StringCheckGrader;\n\npublic final class Main {\n    private Main() {}\n\n    public static void main(String[] args) {\n        OpenAIClient client = OpenAIOkHttpClient.fromEnv();\n\n        GraderRunParams params = GraderRunParams.builder()\n            .grader(StringCheckGrader.builder()\n                .input(\"input\")\n                .name(\"name\")\n                .operation(StringCheckGrader.Operation.EQ)\n                .reference(\"reference\")\n                .build())\n            .modelSample(\"model_sample\")\n            .build();\n        GraderRunResponse response = client.fineTuning().alpha().graders().run(params);\n    }\n}"
            ruby: "require \"openai\"\n\nopenai = OpenAI::Client.new(api_key: \"My API Key\")\n\nresponse = openai.fine_tuning.alpha.graders.run(\n  grader: {input: \"input\", name: \"name\", operation: :eq, reference: \"reference\", type: :string_check},\n  model_sample: \"model_sample\"\n)\n\nputs(response)"
          response: "{\n  \"reward\": 1.0,\n  \"metadata\": {\n    \"name\": \"Example score model grader\",\n    \"type\": \"score_model\",\n    \"errors\": {\n      \"formula_parse_error\": false,\n      \"sample_parse_error\": false,\n      \"truncated_observation_error\": false,\n      \"unresponsive_reward_error\": false,\n      \"invalid_variable_error\": false,\n      \"other_error\": false,\n      \"python_grader_server_error\": false,\n      \"python_grader_server_error_type\": null,\n      \"python_grader_runtime_error\": false,\n      \"python_grader_runtime_error_details\": null,\n      \"model_grader_server_error\": false,\n      \"model_grader_refusal_error\": false,\n      \"model_grader_parse_error\": false,\n      \"model_grader_server_error_details\": null\n    },\n    \"execution_time\": 4.365238428115845,\n    \"scores\": {},\n    \"token_usage\": {\n      \"prompt_tokens\": 190,\n      \"total_tokens\": 324,\n      \"completion_tokens\": 134,\n      \"cached_tokens\": 0\n    },\n    \"sampled_model_name\": \"gpt-4o-2024-08-06\"\n  },\n  \"sub_rewards\": {},\n  \"model_grader_token_usage_per_model\": {\n    \"gpt-4o-2024-08-06\": {\n      \"prompt_tokens\": 190,\n      \"total_tokens\": 324,\n      \"completion_tokens\": 134,\n      \"cached_tokens\": 0\n    }\n  }\n}\n"
        - title: Score an image caption
          request:
            curl: "curl -X POST https://api.openai.com/v1/fine_tuning/alpha/graders/run \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n  -d '{\n    \"grader\": {\n      \"type\": \"score_model\",\n      \"name\": \"Image caption grader\",\n      \"input\": [\n        {\n          \"role\": \"user\",\n          \"content\": [\n            {\n              \"type\": \"input_text\",\n              \"text\": \"Score how well the provided caption matches the image on a 0-1 scale. Only return the score.\\n\\nCaption: {{sample.output_text}}\"\n            },\n            {\n              \"type\": \"input_image\",\n              \"image_url\": \"https://example.com/dog-catching-ball.png\",\n              \"file_id\": null,\n              \"detail\": \"high\"\n            }\n          ]\n        }\n      ],\n      \"model\": \"gpt-5-mini\",\n      \"sampling_params\": {\n        \"temperature\": 0.2\n      }\n    },\n    \"item\": {\n      \"expected_caption\": \"A golden retriever jumps to catch a tennis ball\"\n    },\n    \"model_sample\": \"A dog leaps to grab a tennis ball mid-air\"\n  }'\n"
            node.js: "import OpenAI from 'openai';\n\nconst client = new OpenAI({\n  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted\n});\n\nconst response = await client.fineTuning.alpha.graders.run({\n  grader: {\n    input: 'input',\n    name: 'name',\n    operation: 'eq',\n    reference: 'reference',\n    type: 'string_check',\n  },\n  model_sample: 'model_sample',\n});\n\nconsole.log(response.metadata);"
            python: "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n    api_key=os.environ.get(\"OPENAI_API_KEY\"),  # This is the default and can be omitted\n)\nresponse = client.fine_tuning.alpha.graders.run(\n    grader={\n        \"input\": \"input\",\n        \"name\": \"name\",\n        \"operation\": \"eq\",\n        \"reference\": \"reference\",\n        \"type\": \"string_check\",\n    },\n    model_sample=\"model_sample\",\n)\nprint(response.metadata)"
            go: "package main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\n\t\"github.com/openai/openai-go\"\n\t\"github.com/openai/openai-go/option\"\n)\n\nfunc main() {\n\tclient := openai.NewClient(\n\t\toption.WithAPIKey(\"My API Key\"),\n\t)\n\tresponse, err := client.FineTuning.Alpha.Graders.Run(context.TODO(), openai.FineTuningAlphaGraderRunParams{\n\t\tGrader: openai.FineTuningAlphaGraderRunParamsGraderUnion{\n\t\t\tOfStringCheck: &openai.StringCheckGraderParam{\n\t\t\t\tInput:     \"input\",\n\t\t\t\tName:      \"name\",\n\t\t\t\tOperation: openai.StringCheckGraderOperationEq,\n\t\t\t\tReference: \"reference\",\n\t\t\t},\n\t\t},\n\t\tModelSample: \"model_sample\",\n\t})\n\tif err != nil {\n\t\tpanic(err.Error())\n\t}\n\tfmt.Printf(\"%+v\\n\", response.Metadata)\n}\n"
            java: "package com.openai.example;\n\nimport com.openai.client.OpenAIClient;\nimport com.openai.client.okhttp.OpenAIOkHttpClient;\nimport com.openai.models.finetuning.alpha.graders.GraderRunParams;\nimport com.openai.models.finetuning.alpha.graders.GraderRunResponse;\nimport com.openai.models.graders.gradermodels.StringCheckGrader;\n\npublic final class Main {\n    private Main() {}\n\n    public static void main(String[] args) {\n        OpenAIClient client = OpenAIOkHttpClient.fromEnv();\n\n        GraderRunParams params = GraderRunParams.builder()\n            .grader(StringCheckGrader.builder()\n                .input(\"input\")\n                .name(\"name\")\n                .operation(StringCheckGrader.Operation.EQ)\n                .reference(\"reference\")\n                .build())\n            .modelSample(\"model_sample\")\n            .build();\n        GraderRunResponse response = client.fineTuning().alpha().graders().run(params);\n    }\n}"
            ruby: "require \"openai\"\n\nopenai = OpenAI::Client.new(api_key: \"My API Key\")\n\nresponse = openai.fine_tuning.alpha.graders.run(\n  grader: {input: \"input\", name: \"name\", operation: :eq, reference: \"reference\", type: :string_check},\n  model_sample: \"model_sample\"\n)\n\nputs(response)"
        - title: Score an audio response
          request:
            curl: "curl -X POST https://api.openai.com/v1/fine_tuning/alpha/graders/run \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n  -d '{\n    \"grader\": {\n      \"type\": \"score_model\",\n      \"name\": \"Audio clarity grader\",\n      \"input\": [\n        {\n          \"role\": \"user\",\n          \"content\": [\n            {\n              \"type\": \"input_text\",\n              \"text\": \"Listen to the clip and return a confidence score from 0 to 1 that the speaker said: {{item.target_phrase}}\"\n            },\n            {\n              \"type\": \"input_audio\",\n              \"input_audio\": {\n                \"data\": \"{{item.audio_clip_b64}}\",\n                \"format\": \"mp3\"\n              }\n            }\n          ]\n        }\n      ],\n      \"model\": \"gpt-audio\",\n      \"sampling_params\": {\n        \"temperature\": 0.2,\n        \"top_p\": 1,\n        \"seed\": 123\n      }\n    },\n    \"item\": {\n      \"target_phrase\": \"Please deliver the package on Tuesday\",\n      \"audio_clip_b64\": \"<base64-encoded mp3>\"\n    },\n    \"model_sample\": \"Please deliver the package on Tuesday\"\n  }'\n"
            node.js: "import OpenAI from 'openai';\n\nconst client = new OpenAI({\n  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted\n});\n\nconst response = await client.fineTuning.alpha.graders.run({\n  grader: {\n    input: 'input',\n    name: 'name',\n    operation: 'eq',\n    reference: 'reference',\n    type: 'string_check',\n  },\n  model_sample: 'model_sample',\n});\n\nconsole.log(response.metadata);"
            python: "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n    api_key=os.environ.get(\"OPENAI_API_KEY\"),  # This is the default and can be omitted\n)\nresponse = client.fine_tuning.alpha.graders.run(\n    grader={\n        \"input\": \"input\",\n        \"name\": \"name\",\n        \"operation\": \"eq\",\n        \"reference\": \"reference\",\n        \"type\": \"string_check\",\n    },\n    model_sample=\"model_sample\",\n)\nprint(response.metadata)"
            go: "package main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\n\t\"github.com/openai/openai-go\"\n\t\"github.com/openai/openai-go/option\"\n)\n\nfunc main() {\n\tclient := openai.NewClient(\n\t\toption.WithAPIKey(\"My API Key\"),\n\t)\n\tresponse, err := client.FineTuning.Alpha.Graders.Run(context.TODO(), openai.FineTuningAlphaGraderRunParams{\n\t\tGrader: openai.FineTuningAlphaGraderRunParamsGraderUnion{\n\t\t\tOfStringCheck: &openai.StringCheckGraderParam{\n\t\t\t\tInput:     \"input\",\n\t\t\t\tName:      \"name\",\n\t\t\t\tOperation: openai.StringCheckGraderOperationEq,\n\t\t\t\tReference: \"reference\",\n\t\t\t},\n\t\t},\n\t\tModelSample: \"model_sample\",\n\t})\n\tif err != nil {\n\t\tpanic(err.Error())\n\t}\n\tfmt.Printf(\"%+v\\n\", response.Metadata)\n}\n"
            java: "package com.openai.example;\n\nimport com.openai.client.OpenAIClient;\nimport com.openai.client.okhttp.OpenAIOkHttpClient;\nimport com.openai.models.finetuning.alpha.graders.GraderRunParams;\nimport com.openai.models.finetuning.alpha.graders.GraderRunResponse;\nimport com.openai.models.graders.gradermodels.StringCheckGrader;\n\npublic final class Main {\n    private Main() {}\n\n    public static void main(String[] args) {\n        OpenAIClient client = OpenAIOkHttpClient.fromEnv();\n\n        GraderRunParams params = GraderRunParams.builder()\n            .grader(StringCheckGrader.builder()\n                .input(\"input\")\n                .name(\"name\")\n                .operation(StringCheckGrader.Operation.EQ)\n                .reference(\"reference\")\n                .build())\n            .modelSample(\"model_sample\")\n            .build();\n        GraderRunResponse response = client.fineTuning().alpha().graders().run(params);\n    }\n}"
            ruby: "require \"openai\"\n\nopenai = OpenAI::Client.new(api_key: \"My API Key\")\n\nresponse = openai.fine_tuning.alpha.graders.run(\n  grader: {input: \"input\", name: \"name\", operation: :eq, reference: \"reference\", type: :string_check},\n  model_sample: \"model_sample\"\n)\n\nputs(response)"
    servers:
    - url: https://api.openai.com/v1
  /fine_tuning/alpha/graders/validate:
    post:
      operationId: validateGrader
      tags:
      - Fine Tuning
      summary: 'Validate a grader.

        '
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/ValidateGraderRequest'
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ValidateGraderResponse'
      x-oaiMeta:
        name: Validate grader
        beta: true
        group: graders
        examples:
          request:
            curl: "curl https://api.openai.com/v1/fine_tuning/alpha/graders/validate \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"grader\": {\n      \"type\": \"string_check\",\n      \"name\": \"Example string check grader\",\n      \"input\": \"{{sample.output_text}}\",\n      \"reference\": \"{{item.label}}\",\n      \"operation\": \"eq\"\n    }\n  }'\n"
            node.js: "import OpenAI from 'openai';\n\nconst client = new OpenAI({\n  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted\n});\n\nconst response = await client.fineTuning.alpha.graders.validate({\n  grader: {\n    input: 'input',\n    name: 'name',\n    operation: 'eq',\n    reference: 'reference',\n    type: 'string_check',\n  },\n});\n\nconsole.log(response.grader);"
            python: "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n    api_key=os.environ.get(\"OPENAI_API_KEY\"),  # This is the default and can be omitted\n)\nresponse = client.fine_tuning.alpha.graders.validate(\n    grader={\n        \"input\": \"input\",\n        \"name\": \"name\",\n        \"operation\": \"eq\",\n        \"reference\": \"reference\",\n        \"type\": \"string_check\",\n    },\n)\nprint(response.grader)"
            go: "package main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\n\t\"github.com/openai/openai-go\"\n\t\"github.com/openai/openai-go/option\"\n)\n\nfunc main() {\n\tclient := openai.NewClient(\n\t\toption.WithAPIKey(\"My API Key\"),\n\t)\n\tresponse, err := client.FineTuning.Alpha.Graders.Validate(context.TODO(), openai.FineTuningAlphaGraderValidateParams{\n\t\tGrader: openai.FineTuningAlphaGraderValidateParamsGraderUnion{\n\t\t\tOfStringCheckGrader: &openai.StringCheckGraderParam{\n\t\t\t\tInput:     \"input\",\n\t\t\t\tName:      \"name\",\n\t\t\t\tOperation: openai.StringCheckGraderOperationEq,\n\t\t\t\tReference: \"reference\",\n\t\t\t},\n\t\t},\n\t})\n\tif err != nil {\n\t\tpanic(err.Error())\n\t}\n\tfmt.Printf(\"%+v\\n\", response.Grader)\n}\n"
            java: "package com.openai.example;\n\nimport com.openai.client.OpenAIClient;\nimport com.openai.client.okhttp.OpenAIOkHttpClient;\nimport com.openai.models.finetuning.alpha.graders.GraderValidateParams;\nimport com.openai.models.finetuning.alpha.graders.GraderValidateResponse;\nimport com.openai.models.graders.gradermodels.StringCheckGrader;\n\npublic final class Main {\n    private Main() {}\n\n    public static void main(String[] args) {\n        OpenAIClient client = OpenAIOkHttpClient.fromEnv();\n\n        GraderValidateParams params = GraderValidateParams.builder()\n            .grader(StringCheckGrader.builder()\n                .input(\"input\")\n                .name(\"name\")\n                .operation(StringCheckGrader.Operation.EQ)\n                .reference(\"reference\")\n                .build())\n            .build();\n        GraderValidateResponse response = client.fineTuning().alpha().graders().validate(params);\n    }\n}"
            ruby: "require \"openai\"\n\nopenai = OpenAI::Client.new(api_key: \"My API Key\")\n\nrespon

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# Full source: https://raw.githubusercontent.com/api-evangelist/openai/refs/heads/main/openapi/openai-fine-tuning-api-openapi.yml