Optional batch_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. By default, the batch size will be dynamically configured to be ~0.2% of the number of examples in the training set, capped at 256 - in general, we've found that larger batch sizes tend to work better for larger datasets.
CreateFineTuneRequest
Optional classification_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. With a beta of 1 (i.e. the F-1 score), precision and recall are given the same weight. A larger beta score puts more weight on recall and less on precision. A smaller beta score puts more weight on precision and less on recall.
CreateFineTuneRequest
Optional classification_The number of classes in a classification task. This parameter is required for multiclass classification.
CreateFineTuneRequest
Optional classification_The positive class in binary classification. This parameter is needed to generate precision, recall, and F1 metrics when doing binary classification.
CreateFineTuneRequest
Optional compute_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. In
order to compute classification metrics, you must provide a
validation_file. Additionally, you must specify
classification_n_classes for multiclass classification or
classification_positive_class for binary classification.
CreateFineTuneRequest
Optional learning_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. By default, the learning rate multiplier is
the 0.05, 0.1, or 0.2 depending on final batch_size (larger learning
rates tend to perform better with larger batch sizes). We recommend
experimenting with values in the range 0.02 to 0.2 to see what produces
the best results.
CreateFineTuneRequest
Optional modelThe name of the base model to fine-tune. You can select one of "ada", "babbage", "curie", "davinci", or a fine-tuned model created after 2022-04-21. To learn more about these models, see the Models documentation.
CreateFineTuneRequest
Optional n_The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
CreateFineTuneRequest
Optional prompt_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. If prompts are extremely long (relative to completions), it may make sense to reduce this weight so as to avoid over-prioritizing learning the prompt.
CreateFineTuneRequest
Optional suffixA 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
ada:ft-your-org:custom-model-name-2022-02-15-04-21-04.
CreateFineTuneRequest
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, where each training example is
a JSON object with the keys "prompt" and "completion". Additionally,
you must upload your file with the purpose fine-tune. See the
fine-tuning guide for
more details.
CreateFineTuneRequest
Optional validation_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.
Your train and validation data should be mutually exclusive. Your
dataset must be formatted as a JSONL file, where each validation example
is a JSON object with the keys "prompt" and "completion".
Additionally, you must upload your file with the purpose fine-tune.
See the fine-tuning
guide for more details.
CreateFineTuneRequest
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