teleprompt.BootstrapFinetune
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Constructor
__init__(self, metric=None, teacher_settings={}, multitask=True)
The constructor initializes a BootstrapFinetune instance and sets up its attributes. It defines the teleprompter as a BootstrapFewShot instance for the finetuning compilation.
class BootstrapFinetune(Teleprompter):
def __init__(self, metric=None, teacher_settings={}, multitask=True):
Parameters:
- metric (callable, optional): Metric function to evaluate examples during bootstrapping. Defaults to None.
- teacher_settings (dict, optional): Settings for teacher predictor. Defaults to empty dictionary.
- multitask (bool, optional): Enable multitask fine-tuning. Defaults to True.
Method
compile(self, student, *, teacher=None, trainset, valset=None, target='t5-large', bsize=12, accumsteps=1, lr=5e-5, epochs=1, bf16=False)
This method first compiles for bootstrapping with the BootstrapFewShot teleprompter. It then prepares fine-tuning data by generating prompt-completion pairs for training and performs finetuning. After compilation, the LMs are set to the finetuned models and the method returns a compiled and fine-tuned predictor.
Parameters:
- student (Predict): Student predictor to be fine-tuned.
- teacher (Predict, optional): Teacher predictor to help with fine-tuning. Defaults to None.
- trainset (list): Training dataset for fine-tuning.
- valset (list, optional): Validation dataset for fine-tuning. Defaults to None.
- target (str, optional): Target model for fine-tuning. Defaults to 't5-large'.
- bsize (int, optional): Batch size for training. Defaults to 12.
- accumsteps (int, optional): Gradient accumulation steps. Defaults to 1.
- lr (float, optional): Learning rate for fine-tuning. Defaults to 5e-5.
- epochs (int, optional): Number of training epochs. Defaults to 1.
- bf16 (bool, optional): Enable mixed-precision training with BF16. Defaults to False.
Returns:
- compiled2 (Predict): A compiled and fine-tuned Predict instance.