openapi: 3.0.0
info:
title: OpenAI Assistants Fine Tuning API
description: The Assistants API allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. The Assistants API currently supports three types of tools - Code Interpreter, Retrieval, and Function calling. In the future, we plan to release more OpenAI-built tools, and allow you to provide your own tools on our platform.
version: 2.0.0
termsOfService: https://openai.com/policies/terms-of-use
contact:
name: OpenAI Support
url: https://help.openai.com/
license:
name: MIT
url: https://github.com/openai/openai-openapi/blob/master/LICENSE
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"
/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"
/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"
/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"
/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)"
/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\nresponse = openai.fine_tuning.alpha.graders.vali
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# Full source: https://raw.githubusercontent.com/api-evangelist/openai/refs/heads/main/openapi/openai-fine-tuning-api-openapi.yml