Training LLMs with Synthetic Data

Author(s): Louis-François Bouchard

Originally published on Towards AI.

How Nvidia trained Nemotron 340B

Have you ever wondered why training large language models is such a massive challenge? The secret is the enormous amount of high-quality data these models need. But getting that data is incredibly tough. While many people have tried to solve this problem in various ways, one of the most promising approaches is using synthetic data. It’s less expensive than other methods, but it does have a major drawback: the lack of diversity. Recently, Nvidia’s new LLMs from their Nemotron family of models have addressed this issue. They’ve shared a pipeline for generating synthetic data that’s used for training and refining large language models (LLMs).

This is Louis-François, co-founder of Towards AI, where we build and share educational content like our recent book or free videos like this one. In today’s video, we dive into Nvidia’s key learnings and insights for training an LLM using synthetic data.

The first step in creating a synthetic dataset is to generate synthetic prompts, and for that, they built a model generator. One of the big challenges with synthetic data is its lack of diversity from these prompts generating new content. To tackle this, Nvidia controlled the prompts distribution to cover a wide range of scenarios thanks… Read the full blog for free on Medium.

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Published via Towards AI