Create Fine-tuning Job

The name of the model to fine-tune. Choose from supported models by OpenAI, Bedrock, or Fireworks.

The ID of an uploaded file that contains training data.

See upload file for how to upload a file.

Your dataset must be formatted as a JSONL file. Additionally, you must upload your file with the purpose fine-tune.

The contents of the file should differ depending on if the model uses the chat or completions format.

See the fine-tuning guide for more details.

The hyperparameters used for the fine-tuning job.

A string of up to 18 characters that will be added to your fine-tuned model name.

For example, a suffix of "custom-model-name" would produce a model name like ft:gpt-3.5-turbo:openai:custom-model-name:7p4lURel.

The ID of an uploaded file that contains validation data.

If you provide this file, the data is used to generate validation metrics periodically during fine-tuning. These metrics can be viewed in the fine-tuning results file. The same data should not be present in both train and validation files.

Your dataset must be formatted as a JSONL file. You must upload your file with the purpose fine-tune.

See the fine-tuning guide for more details.

A list of integrations to enable for your fine-tuning job.

The seed controls the reproducibility of the job. Passing in the same seed and job parameters should produce the same results, but may differ in rare cases. If a seed is not specified, one will be generated for you.

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

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

For fine-tuning jobs that have failed, this will contain more information on the cause of the failure.

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.

The hyperparameters used for the fine-tuning job. See the fine-tuning guide for more details.

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 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 seed used for the fine-tuning job.

A list of integrations to enable for this fine-tuning job.

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.