Don’t have enough data for fine-tuning? Predibase offers synthetic data generation at no extra cost in both the UI and SDK, requiring as few as 10 examples to get started.

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Overview

Synthetic data generation creates new training examples that mimic the patterns and style of your original data. This helps:

• Improve model accuracy through exposure to more scenarios
• Reduce overfitting with diverse training examples
• Balance class distributions by generating examples of sparse classes
• Address model weaknesses with targeted additional data

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How It Works

Our data augmentation process uses OpenAI to generate high-quality synthetic data based on your original examples. The augmented dataset combines both original and synthetic examples and returns a new dataset with three columns:

• prompt: The input text used to generate a response
• completion: The generated response or continuation
• source: Origin of the example (original or synthetic)

Our current implementation generates new, synthetic prompts and their corresponding completions based on your original examples.

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Generation Strategies

Predibase supports two approaches:

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1. Single Pass

A straightforward method that creates data directly reflecting provided examples:

• Uses 1 LLM call per synthetic example
• Direct reflection of seed examples
• Faster and more cost-effective

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2. Mixture of Agents

A sophisticated chain of LLM calls for higher-quality examples:

1. Generate k prompt-completion candidates from each seed
2. Critique candidates based on inferred context and criteria
3. Synthesize final examples from candidates and critiques

Uses 6 LLM calls per synthetic example for higher quality output.

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Implementation Guide

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Prerequisites

• Seed dataset (minimum 10 rows) formatted for supervised fine-tuning in prompt-completion format.
• OpenAI API Key

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Supported Generation Models

The following OpenAI models are supported for synthetic data generation:

• gpt-4o-mini
• gpt-4o-2024-08-06
• gpt-4-turbo
• gpt-4-1106-preview
• gpt-4-0125-preview

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Quick Start

import os
import pandas as pd
from predibase import Predibase, AugmentationConfig

pb = Predibase(api_token="<YOUR_PREDIBASE_API_KEY>")

seed_dataset = pb.datasets.get("file_uploads/seed_dataset_20_rows")

dataset = pb.datasets.augment(
    AugmentationConfig(
        # Model options: gpt-4-turbo, gpt-4-1106-preview, gpt-4-0125-preview,
        # gpt-4o, gpt-4o-2024-08-06, gpt-4o-mini
        base_model="gpt-4o-mini",
        # Strategy: 'mixture_of_agents' or 'single_pass'
        # augmentation_strategy="mixture_of_agents",
        # Recommended values: 25, 50, 100, 200, 500, 1000
        num_samples_to_generate=50,
        # Optional: number of seed samples to use
        # num_seed_samples="all",
        # Optional: task context (inferred if not provided)
        # task_context="",
    ),
    dataset=seed_dataset,
    # Optional: custom name
    # name="my_synthetic_data",
    openai_api_key="<YOUR_OPENAI_API_KEY>",
)

pb.datasets.download(
    dataset_ref=dataset, # can be a string or a Dataset object
    dest="augmented_seed_dataset_20_rows.jsonl"
)

augmented_df = pd.read_json(
    "augmented_seed_dataset_20_rows.jsonl",
    lines=True
)

modified_dataset = pb.datasets.from_pandas_dataframe(
    augmented_df,
    "augmented_seed_dataset_20_rows"
)

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Next Steps

• Get detailed guidance on creating synthetic data
• Prepare your dataset for fine-tuning
• Start fine-tuning with your augmented dataset
• Evaluate results to measure effectiveness