FineTuneRequest

The batch size to use for training. The batch size is the number of training examples used to train a single forward and backward pass.

If this is provided, we calculate F-beta scores at the specified beta values. The F-beta score is a generalization of F-1 score. This is only used for binary classification.

The number of classes in a classification task.

The positive class in binary classification.

If set, we calculate classification-specific metrics such as accuracy and F-1 score using the validation set at the end of every epoch. These metrics can be viewed in the results file.

The learning rate multiplier to use for training. The fine-tuning learning rate is the original learning rate used for pretraining multiplied by this value.

The name of the base model to fine-tune.

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

The weight to use for loss on the prompt tokens. This controls how much the model tries to learn to generate the prompt (as compared to the completion which always has a weight of 1.0), and can add a stabilizing effect to training when completions are short.

A string of up to 40 characters that will be added to your fine-tuned model name.

The ID of an uploaded file that contains training data.

The ID of an uploaded file that contains validation data.