We all know: the more data, the better the training. But what if we lack enough data to actually be able to train our own model? The answer: synthetic datasets.

Generate High-Quality Synthetic Datasets at Home with Self-Synthesis

Did you know you can generate high-quality synthetic datasets at home that rival those produced by GPT-4? Self-synthesis is a revolutionary method using an "empty" user message to create large-scale instruction datasets, leveraging the power of Llama 3 70B. This approach democratizes dataset generation, making it accessible and cost-effective for a wide range of applications.

Implementation Steps

1. Select a Fine-Tuned LLM:
   • Start by selecting a fine-tuned or trained large language model (LLM) like Llama 3 70B. This model serves as the backbone for generating the synthetic data.
2. Create Templates:
   • Use left-side truncated templates before the user messages start (e.g., user\n). This setup primes the model to generate realistic interactions.
3. Generate Synthetic Turns:
   • Prompt the LLM to generate a synthetic user turn and an assistant turn. This step involves running the model to produce large volumes of instructional pairs.
4. Filter Generated Samples:
   • Use the LLM to filter the generated samples, ensuring they meet high standards of quality and diversity. This process helps in curating a dataset that is both useful and varied.

This innovative approach opens new doors for researchers and developers, making high-quality synthetic dataset generation more accessible than ever before. Self-synthesis with Llama 3 70B offers a powerful, efficient, and affordable solution.