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.batchSize
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.classificationBetas
The positive class in binary classification.
This parameter is needed to generate precision,
recall, and F1 metrics when doing binary classification.classificationPositiveClass
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.computeClassificationMetrics
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.learningRateMultiplier
The 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. model
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.promptLossWeight
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 ada:ft-your-org:custom-model-name-2022-02-15-04-21-04.
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. trainingFile
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. validationFile
url https://platform.openai.com/docs/guides/fine-tuning/analyzing-your-fine-tuned-model