class

OpenAI::CreateFineTuningJobRequest

Inherits JSON::Serializable < Reference < Object

Constructors

new(model : CreateFineTuningJobRequestModel = "", training_file : String = "", hyperparameters : CreateFineTuningJobRequestHyperparameters | Nil = nil, suffix : String | Nil = nil, validation_file : String | Nil = nil, integrations : Array(CreateFineTuningJobRequestIntegrationsItem) | Nil = nil, seed : Int64 | Nil = nil, method : FineTuneMethod | Nil = nil, metadata : Metadata | Nil = nil)
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new(*, __pull_for_json_serializable pull : JSON::PullParser)
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Instance methods

hyperparameters

The hyperparameters used for the fine-tuning job. This value is now deprecated in favor of method, and should be passed in under the method parameter.

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hyperparameters=(hyperparameters : CreateFineTuningJobRequestHyperparameters | Nil)

The hyperparameters used for the fine-tuning job. This value is now deprecated in favor of method, and should be passed in under the method parameter.

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integrations

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

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integrations=(integrations : Array(CreateFineTuningJobRequestIntegrationsItem) | Nil)

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

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metadata
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metadata=(metadata : Metadata | Nil)
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method
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method=(method : FineTuneMethod | Nil)
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model

The name of the model to fine-tune. You can select one of the supported models.

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model=(model : CreateFineTuningJobRequestModel)

The name of the model to fine-tune. You can select one of the supported models.

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seed

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.

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seed=(seed : Int64 | Nil)

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.

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suffix

A string of up to 64 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-4o-mini:openai:custom-model-name:7p4lURel.

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suffix=(suffix : String | Nil)

A string of up to 64 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-4o-mini:openai:custom-model-name:7p4lURel.

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training_file

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, completions format, or if the fine-tuning method uses the preference format.

See the fine-tuning guide for more details.

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training_file=(training_file : String)

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, completions format, or if the fine-tuning method uses the preference format.

See the fine-tuning guide for more details.

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validation_file

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.

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validation_file=(validation_file : String | Nil)

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.

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