NVIDIA Nemotron-4 340B: Large Language Model Training with Synthetic Data

NVIDIA has announced a new family of open generative AI models, Nemotron-4 340B. Developers can use Nemotron for synthetic data generation (SDG) for use in other large language models (LLMs). The Nemotron-4 340B family is a game-changer for generating high-quality synthetic data that significantly enhances model performance, even when real-world data is scarce.

The Nemotron-4 open models family includes Base, Instruct, and Reward models. All are available today on Hugging Face, and developers will soon be able to access the models at ai.nvidia.com. There, they can be packaged as an NVIDIA NIM microservice with a standard application programming interface (API) deployed anywhere.

NVIDIA adds to a growing list of tech companies supporting open models

The announcement by NVIDIA adds to the growing number of “big tech” companies like Google and Meta that are releasing highly-performant generative AI open models. An open model license provides developers a free, scalable way to incorporate generative AI technologies into custom applications and workflows.

Commercial applications for synthetic data Nemotron is optimized for include healthcare, finance, manufacturing, and retail, among other industries, according to NVIDIA.

Nemotron 4 utilizes advanced generative AI techniques to create realistic synthetic data that mirrors the characteristics of real data. This means LLMs trained on Nemotron-generated data can achieve better accuracy and generalization capabilities, ultimately leading to more effective and versatile AI applications.

The benefits of using synthetic data for LLM training are diverse:

• Data Augmentation: Nemotron 4 expands training datasets, overcoming limitations of insufficient real-world data.
• Privacy and Security: Synthetic data eliminates privacy concerns associated with using real user data.
• Cost Reduction: Generating synthetic data is often more cost-effective than acquiring and annotating real data.
• Enhanced Performance: LLMs trained on synthetic data exhibit improved accuracy, generalization, and robustness.

Nemotron fine-tuning for inference with NeMo and TensorRT-LLM

NVIDIA Nemotron 4 leverages open-source tools like NVIDIA NeMo and TensorRT-LLM to optimize instruct and reward models for efficient synthetic data generation and response scoring. The 340B models, optimized with TensorRT-LLM for tensor parallelism, enable efficient inference at scale.

These models, trained on a massive 9 trillion tokens, can be customized through NeMo for specific use cases and domains, resulting in more accurate outputs for downstream tasks. The NeMo framework offers a range of customization methods, including supervised fine-tuning and parameter-efficient methods like LoRA.

NVIDIA Nemotron 4 empowers researchers and developers to train LLMs with unprecedented efficiency and effectiveness. This powerful model family paves the way for the next generation of cutting-edge AI applications across diverse industries by generating high-quality synthetic data.

NVIDIA Nemotron-4 340B reference documentation

• NVIDIA Nemotron-4 340B blog announcement
• NVIDIA open model license agreement
• NVIDIA Nemotron-4 340B Base on Hugging Face
• NVIDIA Nemotron-4 340B Instruct on Hugging Face
• NVIDIA Nemotron-4 340B Reward on Hugging Face