SyGra: The One-Stop Framework for Building Data for LLMs and SLMs

When we think about building a model - be it a Large Language Model (LLM) or a Small Language Model (SLM) - the first thing we need is data. While a vast amount of open data is available, it rarely comes in the exact format required to train or align models. In practice, we often face scenarios where the raw data isn't enough. We need data that is more structured, domain-specific, complex, or aligned with the task at hand. Let's look at some common situations:

You start with a simple dataset, but the model fails on advanced reasoning tasks. How do you generate more complex datasets to strengthen performance?

You already have a knowledge base, but it's not in Q&A format. How can you transform it into a usable question-answering dataset?

Data is the foundation of AI. The quality, diversity, and structure of your data often matter more than model architecture tweaks. By enabling flexible and scalable dataset creation, SyGra helps teams:

You've prepared a supervised fine-tuning (SFT) dataset. But now you want to align your model using Direct Preference Optimization (DPO). How can you generate preference pairs?

You have a Q&A dataset, but the questions are shallow. How can you create in-depth, multi-turn, or reasoning-heavy questions?

You possess a massive corpus but need to filter and curate data for mid-training on a specific domain.