How can I add options and hyperparameters to the fine-tuning command in new Fine-Tuning API?

API
2

Request body

training_file

string

Required

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.

See the fine-tuning guide for more details.

validation_file

string or null

Optional

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.

model

string

Required

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

hyperparameters

object

Optional

The hyperparameters used for the fine-tuning job.

n_epochs

string or integer

Optional

Defaults to auto

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

suffix

string or null

Optional

Defaults to null

A string of up to 40 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.

https://platform.openai.com/docs/api-reference/fine-tuning/create#fine-tuning/create-suffix

Here you go… The parameters are still there…you just need to pass them…

openai.FineTune.create(
    model_engine=model_engine,
    n_epochs=n_epochs,
    batch_size=batch_size,
    learning_rate=learning_rate,
    max_tokens=max_tokens,
    training_file=os.path.abspath(training_file),
    validation_file=os.path.abspath(validation_file),
)
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