Manage fine-tuning jobs to tailor a model to your specific training data. 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.
openapi: 3.0.0
info:
title: 'OpenAI fine tuning'
description: Needs description.
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
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 \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"training_file": "file-BK7bzQj3FfZFXr7DbL6xJwfo",
"model": "gpt-3.5-turbo"
}'
python: |
from openai import OpenAI
client = OpenAI()
client.fine_tuning.jobs.create(
training_file="file-abc123",
model="gpt-3.5-turbo"
)
node.js: |
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const fineTune = await openai.fineTuning.jobs.create({
training_file: "file-abc123"
});
console.log(fineTune);
}
main();
response: |
{
"object": "fine_tuning.job",
"id": "ftjob-abc123",
"model": "gpt-3.5-turbo-0613",
"created_at": 1614807352,
"fine_tuned_model": null,
"organization_id": "org-123",
"result_files": [],
"status": "queued",
"validation_file": null,
"training_file": "file-abc123",
}
- title: Epochs
request:
curl: |
curl https://api.openai.com/v1/fine_tuning/jobs \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"training_file": "file-abc123",
"model": "gpt-3.5-turbo",
"hyperparameters": {
"n_epochs": 2
}
}'
python: |
from openai import OpenAI
client = OpenAI()
client.fine_tuning.jobs.create(
training_file="file-abc123",
model="gpt-3.5-turbo",
hyperparameters={
"n_epochs":2
}
)
node.js: |
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const fineTune = await openai.fineTuning.jobs.create({
training_file: "file-abc123",
model: "gpt-3.5-turbo",
hyperparameters: { n_epochs: 2 }
});
console.log(fineTune);
}
main();
response: |
{
"object": "fine_tuning.job",
"id": "ftjob-abc123",
"model": "gpt-3.5-turbo-0613",
"created_at": 1614807352,
"fine_tuned_model": null,
"organization_id": "org-123",
"result_files": [],
"status": "queued",
"validation_file": null,
"training_file": "file-abc123",
"hyperparameters": {"n_epochs": 2},
}
- title: Validation file
request:
curl: |
curl https://api.openai.com/v1/fine_tuning/jobs \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"training_file": "file-abc123",
"validation_file": "file-abc123",
"model": "gpt-3.5-turbo"
}'
python: |
from openai import OpenAI
client = OpenAI()
client.fine_tuning.jobs.create(
training_file="file-abc123",
validation_file="file-def456",
model="gpt-3.5-turbo"
)
node.js: |
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const fineTune = await openai.fineTuning.jobs.create({
training_file: "file-abc123",
validation_file: "file-abc123"
});
console.log(fineTune);
}
main();
response: |
{
"object": "fine_tuning.job",
"id": "ftjob-abc123",
"model": "gpt-3.5-turbo-0613",
"created_at": 1614807352,
"fine_tuned_model": null,
"organization_id": "org-123",
"result_files": [],
"status": "queued",
"validation_file": "file-abc123",
"training_file": "file-abc123",
}
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 \
-H "Authorization: Bearer $OPENAI_API_KEY"
python: |
from openai import OpenAI
client = OpenAI()
client.fine_tuning.jobs.list()
node.js: |-
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const list = await openai.fineTuning.jobs.list();
for await (const fineTune of list) {
console.log(fineTune);
}
}
main();
response: |
{
"object": "list",
"data": [
{
"object": "fine_tuning.job.event",
"id": "ft-event-TjX0lMfOniCZX64t9PUQT5hn",
"created_at": 1689813489,
"level": "warn",
"message": "Fine tuning process stopping due to job cancellation",
"data": null,
"type": "message"
},
{ ... },
{ ... }
], "has_more": true
}
/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
\
-H "Authorization: Bearer $OPENAI_API_KEY"
python: |
from openai import OpenAI
client = OpenAI()
client.fine_tuning.jobs.retrieve("ftjob-abc123")
node.js: |
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const fineTune = await openai.fineTuning.jobs.retrieve("ftjob-abc123");
console.log(fineTune);
}
main();
response: |
{
"object": "fine_tuning.job",
"id": "ftjob-abc123",
"model": "davinci-002",
"created_at": 1692661014,
"finished_at": 1692661190,
"fine_tuned_model": "ft:davinci-002:my-org:custom_suffix:7q8mpxmy",
"organization_id": "org-123",
"result_files": [
"file-abc123"
],
"status": "succeeded",
"validation_file": null,
"training_file": "file-abc123",
"hyperparameters": {
"n_epochs": 4,
},
"trained_tokens": 5768
}
/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 \
-H "Authorization: Bearer $OPENAI_API_KEY"
python: |
from openai import OpenAI
client = OpenAI()
client.fine_tuning.jobs.list_events(
fine_tuning_job_id="ftjob-abc123",
limit=2
)
node.js: |-
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const list = await openai.fineTuning.list_events(id="ftjob-abc123", limit=2);
for await (const fineTune of list) {
console.log(fineTune);
}
}
main();
response: |
{
"object": "list",
"data": [
{
"object": "fine_tuning.job.event",
"id": "ft-event-ddTJfwuMVpfLXseO0Am0Gqjm",
"created_at": 1692407401,
"level": "info",
"message": "Fine tuning job successfully completed",
"data": null,
"type": "message"
},
{
"object": "fine_tuning.job.event",
"id": "ft-event-tyiGuB72evQncpH87xe505Sv",
"created_at": 1692407400,
"level": "info",
"message": "New fine-tuned model created: ft:gpt-3.5-turbo:openai::7p4lURel",
"data": null,
"type": "message"
}
],
"has_more": true
}
/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 \
-H "Authorization: Bearer $OPENAI_API_KEY"
python: |
from openai import OpenAI
client = OpenAI()
client.fine_tuning.jobs.cancel("ftjob-abc123")
node.js: |-
import OpenAI from "openai";
const openai = new OpenAI();
async function main() {
const fineTune = await openai.fineTuning.jobs.cancel("ftjob-abc123");
console.log(fineTune);
}
main();
response: |
{
"object": "fine_tuning.job",
"id": "ftjob-abc123",
"model": "gpt-3.5-turbo-0613",
"created_at": 1689376978,
"fine_tuned_model": null,
"organization_id": "org-123",
"result_files": [],
"hyperparameters": {
"n_epochs": "auto"
},
"status": "cancelled",
"validation_file": "file-abc123",
"training_file": "file-abc123"
}
components:
securitySchemes:
ApiKeyAuth:
type: http
scheme: bearer
schemas:
FineTuningJob:
type: object
title: FineTuningJob
description: >
The `fine_tuning.job` object represents a fine-tuning job that has been
created through the API.
properties:
id:
type: string
description: The object identifier, which can be referenced in the API endpoints.
created_at:
type: integer
description: >-
The Unix timestamp (in seconds) for when the fine-tuning job was
created.
error:
type: object
nullable: true
description: >-
For fine-tuning jobs that have `failed`, this will contain more
information on the cause of the failure.
properties:
code:
type: string
description: A machine-readable error code.
message:
type: string
description: A human-readable error message.
param:
type: string
description: >-
The parameter that was invalid, usually `training_file` or
`validation_file`. This field will be null if the failure was
not parameter-specific.
nullable: true
required:
- code
- message
- param
fine_tuned_model:
type: string
nullable: true
description: >-
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:
type: integer
nullable: true
description: >-
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:
type: object
description: >-
The hyperparameters used for the fine-tuning job. See the
[fine-tuning guide](/docs/guides/fine-tuning) for more details.
properties:
n_epochs:
oneOf:
- type: string
enum:
- auto
- type: integer
minimum: 1
maximum: 50
default: auto
description: >-
The number of epochs to train the model for. An epoch refers to
one full cycle through the training dataset.
"auto" decides the optimal number of epochs based on the size of
the dataset. If setting the number manually, we support any
number between 1 and 50 epochs.
required:
- n_epochs
model:
type: string
description: The base model that is being fine-tuned.
object:
type: string
description: The object type, which is always "fine_tuning.job".
enum:
- fine_tuning.job
organization_id:
type: string
description: The organization that owns the fine-tuning job.
result_files:
type: array
description: >-
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).
items:
type: string
example: file-abc123
status:
type: string
description: >-
The current status of the fine-tuning job, which can be either
`validating_files`, `queued`, `running`, `succeeded`, `failed`, or
`cancelled`.
enum:
- validating_files
- queued
- running
- succeeded
- failed
- cancelled
trained_tokens:
type: integer
nullable: true
description: >-
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:
type: string
description: >-
The file ID used for training. You can retrieve the training data
with the [Files API](/docs/api-reference/files/retrieve-contents).
validation_file:
type: string
nullable: true
description: >-
The file ID used for validation. You can retrieve the validation
results with the [Files
API](/docs/api-reference/files/retrieve-contents).
required:
- created_at
- error
- finished_at
- fine_tuned_model
- hyperparameters
- id
- model
- object
- organization_id
- result_files
- status
- trained_tokens
- training_file
- validation_file
x-oaiMeta:
name: The fine-tuning job object
example: |
{
"object": "fine_tuning.job",
"id": "ftjob-abc123",
"model": "davinci-002",
"created_at": 1692661014,
"finished_at": 1692661190,
"fine_tuned_model": "ft:davinci-002:my-org:custom_suffix:7q8mpxmy",
"organization_id": "org-123",
"result_files": [
"file-abc123"
],
"status": "succeeded",
"validation_file": null,
"training_file": "file-abc123",
"hyperparameters": {
"n_epochs": 4,
},
"trained_tokens": 5768
}
ListPaginatedFineTuningJobsResponse:
type: object
properties:
data:
type: array
items:
$ref: '#/components/schemas/FineTuningJob'
has_more:
type: boolean
object:
type: string
enum:
- list
required:
- object
- data
- has_more
ListFineTuningJobEventsResponse:
type: object
properties:
data:
type: array
items:
$ref: '#/components/schemas/FineTuningJobEvent'
object:
type: string
enum:
- list
required:
- object
- data
security:
- ApiKeyAuth: []
x-oaiMeta:
groups:
- id: audio
title: Audio
description: |
Learn how to turn audio into text or text into audio.
Related guide: [Speech to text](/docs/guides/speech-to-text)
sections:
- type: endpoint
key: createSpeech
path: createSpeech
- type: endpoint
key: createTranscription
path: createTranscription
- type: endpoint
key: createTranslation
path: createTranslation
- id: chat
title: Chat
description: >
Given a list of messages comprising a conversation, the model will
return a response.
Related guide: [Chat Completions](/docs/guides/text-generation)
sections:
- type: endpoint
key: createChatCompletion
path: create
- type: object
key: CreateChatCompletionResponse
path: object
- type: object
key: CreateChatCompletionStreamResponse
path: streaming
- id: embeddings
title: Embeddings
description: >
Get a vector representation of a given input that can be easily consumed
by machine learning models and algorithms.
Related guide: [Embeddings](/docs/guides/embeddings)
sections:
- type: endpoint
key: createEmbedding
path: create
- type: object
key: Embedding
path: object
- id: fine-tuning
title: Fine-tuning
description: >
Manage fine-tuning jobs to tailor a model to your specific training
data.
Related guide: [Fine-tune models](/docs/guides/fine-tuning)
sections:
- type: endpoint
key: createFineTuningJob
path: create
- type: endpoint
key: listPaginatedFineTuningJobs
path: list
- type: endpoint
key: listFineTuningEvents
path: list-events
- type: endpoint
key: retrieveFineTuningJob
path: retrieve
- type: endpoint
key: cancelFineTuningJob
path: cancel
- type: object
key: FineTuningJob
path: object
- type: object
key: FineTuningJobEvent
path: event-object
- id: files
title: Files
description: >
Files are used to upload documents that can be used with features like
[Assistants](/docs/api-reference/assistants) and
[Fine-tuning](/docs/api-reference/fine-tuning).
sections:
- type: endpoint
key: createFile
path: create
- type: endpoint
key: listFiles
path: list
- type: endpoint
key: retrieveFile
path: retrieve
- type: endpoint
key: deleteFile
path: delete
- type: endpoint
key: downloadFile
path: retrieve-contents
- type: object
key: OpenAIFile
path: object
- id: images
title: Images
description: >
Given a prompt and/or an input image, the model will generate a new
image.
Related guide: [Image generation](/docs/guides/images)
sections:
- type: endpoint
key: createImage
path: create
- type: endpoint
key: createImageEdit
path: createEdit
- type: endpoint
key: createImageVariation
path: createVariation
- type: object
key: Image
path: object
- id: models
title: Models
description: >
List and describe the various models available in the API. You can refer
to the [Models](/docs/models) documentation to understand what models
are available and the differences between them.
sections:
- type: endpoint
key: listModels
path: list
- type: endpoint
key: retrieveModel
path: retrieve
- type: endpoint
key: deleteModel
path: delete
- type: object
key: Model
path: object
- id: moderations
title: Moderations
description: >
Given a input text, outputs if the model classifies it as violating
OpenAI's content policy.
Related guide: [Moderations](/docs/guides/moderation)
sections:
- type: endpoint
key: createModeration
path: create
- type: object
key: CreateModerationResponse
path: object
- id: assistants
title: Assistants
beta: true
description: |
Build assistants that can call models and use tools to perform tasks.
[Get started with the Assistants API](/docs/assistants)
sections:
- type: endpoint
key: createAssistant
path: createAssistant
- type: endpoint
key: createAssistantFile
path: createAssistantFile
- type: endpoint
key: listAssistants
path: listAssistants
- type: endpoint
key: listAssistantFiles
path: listAssistantFiles
- type: endpoint
key: getAssistant
path: getAssistant
- type: endpoint
key: getAssistantFile
path: getAssistantFile
- type: endpoint
key: modifyAssistant
path: modifyAssistant
- type: endpoint
key: deleteAssistant
path: deleteAssistant
- type: endpoint
key: deleteAssistantFile
path: deleteAssistantFile
- type: object
key: AssistantObject
path: object
- type: object
key: AssistantFileObject
path: file-object
- id: threads
title: Threads
beta: true
description: |
Create threads that assistants can interact with.
Related guide: [Assistants](/docs/assistants/overview)
sections:
- type: endpoint
key: createThread
path: createThread
- type: endpoint
key: getThread
path: getThread
- type: endpoint
key: modifyThread
path: modifyThread
- type: endpoint
key: deleteThread
path: deleteThread
- type: object
key: ThreadObject
path: object
- id: messages
title: Messages
beta: true
description: |
Create messages within threads
Related guide: [Assistants](/docs/assistants/overview)
sections:
- type: endpoint
key: createMessage
path: createMessage
- type: endpoint
key: listMessages
path: listMessages
- type: endpoint
key: listMessageFiles
path: listMessageFiles
- type: endpoint
key: getMessage
path: getMessage
- type: endpoint
key: getMessageFile
path: getMessageFile
- type: endpoint
key: modifyMessage
path: modifyMessage
- type: object
key: MessageObject
path: object
- type: object
key: MessageFileObject
path: file-object
- id: runs
title: Runs
beta: true
description: |
Represents an execution run on a thread.
Related guide: [Assistants](/docs/assistants/overview)
sections:
- type: endpoint
key: createRun
path: createRun
- type: endpoint
key: createThreadAndRun
path: createThreadAndRun
- type: endpoint
key: listRuns
path: listRuns
- type: endpoint
key: listRunSteps
path: listRunSteps
- type: endpoint
key: getRun
path: getRun
- type: endpoint
key: getRunStep
path: getRunStep
- type: endpoint
key: modifyRun
path: modifyRun
- type: endpoint
key: submitToolOuputsToRun
path: submitToolOutputs
- type: endpoint
key: cancelRun
path: cancelRun
- type: object
key: RunObject
path: object
- type: object
key: RunStepObject
path: step-object
- id: completions
title: Completions
legacy: true
description: >
Given a prompt, the model will return one or more predicted completions
along with the probabilities of alternative tokens at each position.
Most developer should use our [Chat Completions
API](/docs/guides/text-generation/text-generation-models) to leverage
our best and newest models. Most models that support the legacy
Completions endpoint [will be shut off on January 4th,
2024](/docs/deprecations/2023-07-06-gpt-and-embeddings).
sections:
- type: endpoint
key: createCompletion
path: create
- type: object
key: CreateCompletionResponse
path: object