Distilling GPT-4 Capabilities to GPT-3.5#

If you want to make GPT-3.5 (a cheaper, smaller, and faster model) more capable, you can use DataDreamer to distill the capabilities of GPT-4 into GPT-3.5. This will allow you to create a more capable model that is cheaper and faster than GPT-4.

We demonstrate an example below on the ELI5 (β€œExplain it like I’m 5”) task.

from datadreamer import DataDreamer
from datadreamer.llms import OpenAI
from datadreamer.steps import ProcessWithPrompt, HFHubDataSource
from datadreamer.trainers import TrainOpenAIFineTune

with DataDreamer("./output"):
    # Load GPT-4
    gpt_4 = OpenAI(model_name="gpt-4")

    # Get ELI5 questions
    eli5_dataset = HFHubDataSource(
        "Get ELI5 Questions",
        "eli5_category",
        split="train",
        trust_remote_code=True,
    ).select_columns(["title"])

    # Keep only 1000 examples as a quick demo
    eli5_dataset = eli5_dataset.take(1000)

    # Ask GPT-4 to ELI5
    questions_and_answers = ProcessWithPrompt(
        "Generate Explanations",
        inputs={"inputs": eli5_dataset.output["title"]},
        args={
            "llm": gpt_4,
            "instruction": (
                'Given the question, give an "Explain it like I\'m 5" answer.'
            ),
            "top_p": 1.0,
        },
        outputs={"inputs": "questions", "generations": "answers"},
    )

    # Create training data splits
    splits = questions_and_answers.splits(train_size=0.90, validation_size=0.10)

    # Train a model to answer questions in ELI5 style
    trainer = TrainOpenAIFineTune(
        "Distill capabilities to GPT-3.5",
        model_name="gpt-3.5-turbo-1106",
    )
    trainer.train(
        train_input=splits["train"].output["questions"],
        train_output=splits["train"].output["answers"],
        validation_input=splits["validation"].output["questions"],
        validation_output=splits["validation"].output["answers"],
        epochs=30,
        batch_size=8,
    )