Jobs
Create fine-tuning job
List fine-tuning jobs
Retrieve fine-tuning job
List fine-tuning events
Cancel fine-tuning
Pause fine-tuning
Resume fine-tuning
ModelsExpand Collapse
class FineTuningJob: …The fine_tuning.job object represents a fine-tuning job that has been created through the API.
The fine_tuning.job object represents a fine-tuning job that has been created through the API.
The object identifier, which can be referenced in the API endpoints.
The Unix timestamp (in seconds) for when the fine-tuning job was created.
error: Optional[Error]For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.
For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.
A machine-readable error code.
A human-readable error message.
The parameter that was invalid, usually training_file or validation_file. This field will be null if the failure was not parameter-specific.
The name of the fine-tuned model that is being created. The value will be null if the fine-tuning job is still running.
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: HyperparametersThe hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.
The hyperparameters used for the fine-tuning job. This value will only be returned when running supervised jobs.
batch_size: Optional[Union[Literal["auto"], int, null]]Number of examples in each batch. A larger batch size means that model parameters
are updated less frequently, but with lower variance.
Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.
learning_rate_multiplier: Optional[Union[Literal["auto"], float, null]]Scaling factor for the learning rate. A smaller learning rate may be useful to avoid
overfitting.
Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.
n_epochs: Optional[Union[Literal["auto"], int, null]]The number of epochs to train the model for. An epoch refers to one full cycle
through the training dataset.
The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
The base model that is being fine-tuned.
The object type, which is always "fine_tuning.job".
The organization that owns the fine-tuning job.
The compiled results file ID(s) for the fine-tuning job. You can retrieve the results with the Files API.
The seed used for the fine-tuning job.
status: Literal["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.
The current status of the fine-tuning job, which can be either validating_files, queued, running, succeeded, failed, or cancelled.
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.
The file ID used for training. You can retrieve the training data with the Files API.
The file ID used for validation. You can retrieve the validation results with the Files API.
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.
A list of integrations to enable for this fine-tuning job.
A list of integrations to enable for this fine-tuning job.
The type of the integration being enabled for the fine-tuning job
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.
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[Method]The method used for fine-tuning.
The method used for fine-tuning.
type: Literal["supervised", "dpo", "reinforcement"]The type of method. Is either supervised, dpo, or reinforcement.
The type of method. Is either supervised, dpo, or reinforcement.
Configuration for the DPO fine-tuning method.
Configuration for the reinforcement fine-tuning method.
Configuration for the supervised fine-tuning method.
class FineTuningJobEvent: …Fine-tuning job event object
Fine-tuning job event object
The object identifier.
The Unix timestamp (in seconds) for when the fine-tuning job was created.
level: Literal["info", "warn", "error"]The log level of the event.
The log level of the event.
The message of the event.
The object type, which is always "fine_tuning.job.event".
The data associated with the event.
type: Optional[Literal["message", "metrics"]]The type of event.
The type of event.
class 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.
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.
The name of the project that the new run will be created under.
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.
A display name to set for the run. If not set, we will use the Job ID as the name.
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}".
class FineTuningJobWandbIntegrationObject: …
The type of the integration being enabled for the fine-tuning job
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.
JobsCheckpoints
List fine-tuning checkpoints
ModelsExpand Collapse
class FineTuningJobCheckpoint: …The fine_tuning.job.checkpoint object represents a model checkpoint for a fine-tuning job that is ready to use.
The fine_tuning.job.checkpoint object represents a model checkpoint for a fine-tuning job that is ready to use.
The checkpoint identifier, which can be referenced in the API endpoints.
The Unix timestamp (in seconds) for when the checkpoint was created.
The name of the fine-tuned checkpoint model that is created.
The name of the fine-tuning job that this checkpoint was created from.
metrics: MetricsMetrics at the step number during the fine-tuning job.
Metrics at the step number during the fine-tuning job.
The object type, which is always "fine_tuning.job.checkpoint".
The step number that the checkpoint was created at.