Jobs

Manage fine-tuning jobs to tailor a model to your specific training data.

Create fine-tuning job

client.fineTuning.jobs.create(, ?): FineTuningJob { id, created_at, error, 16 more }

POST/fine_tuning/jobs

List fine-tuning jobs

client.fineTuning.jobs.list(?, ?): CursorPage<FineTuningJob { id, created_at, error, 16 more } >

GET/fine_tuning/jobs

Retrieve fine-tuning job

client.fineTuning.jobs.retrieve(, ?): FineTuningJob { id, created_at, error, 16 more }

GET/fine_tuning/jobs/{fine_tuning_job_id}

List fine-tuning events

client.fineTuning.jobs.listEvents(, ?, ?): CursorPage<FineTuningJobEvent { id, created_at, level, 4 more } >

GET/fine_tuning/jobs/{fine_tuning_job_id}/events

Cancel fine-tuning

client.fineTuning.jobs.cancel(, ?): FineTuningJob { id, created_at, error, 16 more }

POST/fine_tuning/jobs/{fine_tuning_job_id}/cancel

Pause fine-tuning

client.fineTuning.jobs.pause(, ?): FineTuningJob { id, created_at, error, 16 more }

POST/fine_tuning/jobs/{fine_tuning_job_id}/pause

Resume fine-tuning

client.fineTuning.jobs.resume(, ?): FineTuningJob { id, created_at, error, 16 more }

POST/fine_tuning/jobs/{fine_tuning_job_id}/resume

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FineTuningJob { id, created_at, error, 16 more }

The fine_tuning.job object represents a fine-tuning job that has been created through the API.

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: Error | 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 | 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 | 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 | 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: Hyperparameters { 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?: "auto" | number | null

Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

One of the following:

"auto"

number

learning_rate_multiplier?: "auto" | number

Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

One of the following:

"auto"

number

n_epochs?: "auto" | number

The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

One of the following:

"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".

organization_id: string

The organization that owns the fine-tuning job.

result_files: Array<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" | "queued" | "running" | 3 more

The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.

One of the following:

"validating_files"

"queued"

"running"

"succeeded"

"failed"

"cancelled"

trained_tokens: number | 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 | null

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

estimated_finish?: number | 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?: Array<FineTuningJobWandbIntegrationObject { type, wandb } > | 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: FineTuningJobWandbIntegration { project, entity, name, tags }

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.

metadata?: Metadata | 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?: Method { type, dpo, reinforcement, supervised }

The method used for fine-tuning.

type: "supervised" | "dpo" | "reinforcement"

The type of method. Is either supervised, dpo, or reinforcement.

One of the following:

"supervised"

"dpo"

"reinforcement"

dpo?: DpoMethod { hyperparameters }

Configuration for the DPO fine-tuning method.

reinforcement?: ReinforcementMethod { grader, hyperparameters }

Configuration for the reinforcement fine-tuning method.

supervised?: SupervisedMethod { hyperparameters }

Configuration for the supervised fine-tuning method.

FineTuningJobEvent { id, created_at, level, 4 more }

Fine-tuning job event object

id: string

The object identifier.

created_at: number

The Unix timestamp (in seconds) for when the fine-tuning job was created.

level: "info" | "warn" | "error"

The log level of the event.

One of the following:

"info"

"warn"

"error"

message: string

The message of the event.

object: "fine_tuning.job.event"

The object type, which is always "fine_tuning.job.event".

data?: unknown

The data associated with the event.

type?: "message" | "metrics"

The type of event.

One of the following:

"message"

"metrics"

FineTuningJobWandbIntegration { project, entity, name, tags }

project: string

The name of the project that the new run will be created under.

entity?: string | 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?: string | null

A display name to set for the run. If not set, we will use the Job ID as the name.

tags?: Array<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}".

FineTuningJobWandbIntegrationObject { type, wandb }

type: "wandb"

The type of the integration being enabled for the fine-tuning job

wandb: FineTuningJobWandbIntegration { project, entity, name, tags }

JobsCheckpoints

Manage fine-tuning jobs to tailor a model to your specific training data.

List fine-tuning checkpoints

client.fineTuning.jobs.checkpoints.list(, ?, ?): CursorPage<FineTuningJobCheckpoint { id, created_at, fine_tuned_model_checkpoint, 4 more } >

GET/fine_tuning/jobs/{fine_tuning_job_id}/checkpoints

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FineTuningJobCheckpoint { id, created_at, fine_tuned_model_checkpoint, 4 more }

The fine_tuning.job.checkpoint object represents a model checkpoint for a fine-tuning job that is ready to use.

id: string

The checkpoint identifier, which can be referenced in the API endpoints.

created_at: number

The Unix timestamp (in seconds) for when the checkpoint was created.

fine_tuned_model_checkpoint: string

The name of the fine-tuned checkpoint model that is created.

fine_tuning_job_id: string

The name of the fine-tuning job that this checkpoint was created from.

metrics: Metrics { full_valid_loss, full_valid_mean_token_accuracy, step, 4 more }

Metrics at the step number during the fine-tuning job.

full_valid_loss?: number

full_valid_mean_token_accuracy?: number

step?: number

train_loss?: number

train_mean_token_accuracy?: number

valid_loss?: number

valid_mean_token_accuracy?: number

object: "fine_tuning.job.checkpoint"

The object type, which is always "fine_tuning.job.checkpoint".

step_number: number

The step number that the checkpoint was created at.

Suggested

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JobsCheckpoints

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