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teleprompt.BootstrapFinetune

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.

Example​

#Assume defined trainset
#Assume defined RAG class
...

#Define teleprompter
teleprompter = BootstrapFinetune(teacher_settings=dict({'lm': teacher}))

# Compile!
compiled_rag = teleprompter.compile(student=RAG(), trainset=trainset, target='google/flan-t5-base')