appmixer.openai.core.createFineTuningJob

The name of the model to fine-tune. You can select one of the supported models. Example: gpt-3.5-turbo

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. Example: file-abc123

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. Example: file-abc123

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

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

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