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NVIDIA Unveils Open Synthetic Data Generation Pipeline for Training Large Language Models

NVIDIA's new initiative sets a benchmark in AI training with synthetic data
June 17, 2024

In a significant step forward for AI development, NVIDIA has introduced the Nemotron-4 340B, a family of open models designed to generate synthetic data for training large language models (LLMs) across various industries including healthcare, finance, manufacturing, and retail.

Game-Changer for AI Training

High-quality training data is essential for the performance and accuracy of custom LLMs, yet acquiring such data can be costly and challenging. Nemotron-4 340B addresses this by providing developers with a scalable and cost-effective solution for generating synthetic data. This family of models includes base, instruct, and reward variants that form a comprehensive pipeline to enhance LLM training and refinement.

Empowering Developers with Advanced Tools

The Nemotron-4 340B models are optimized to work seamlessly with NVIDIA NeMo, an open-source framework that supports end-to-end model training, including data curation, customization, and evaluation. Additionally, these models are tailored for efficient inference using the open-source NVIDIA TensorRT-LLM library, ensuring high performance at scale.

Available for download on Hugging Face, Nemotron-4 340B will soon be accessible via The models will be offered as an NVIDIA NIM microservice, equipped with a standard API for easy deployment.

Synthetic Data Generation: Enhancing AI Performance

In scenarios where access to large, diverse datasets is limited, LLMs can generate synthetic training data to bridge the gap. The Nemotron-4 340B Instruct model creates synthetic data that mirrors real-world data characteristics, significantly enhancing data quality and improving the performance and robustness of custom LLMs across various domains.

To further refine AI-generated data, developers can utilize the Nemotron-4 340B Reward model, which evaluates responses based on attributes such as helpfulness, correctness, coherence, complexity, and verbosity. This model currently leads the Hugging Face RewardBench leaderboard, underscoring its capability to ensure high-quality outputs.

Researchers have the flexibility to create bespoke instruct or reward models by customizing the Nemotron-4 340B Base model with proprietary data, leveraging the included HelpSteer2 dataset.

Fine-Tuning and Optimization with NeMo and TensorRT-LLM

Using NVIDIA NeMo and TensorRT-LLM, developers can enhance the efficiency of their instruct and reward models. These tools support tensor parallelism, allowing individual weight matrices to be split across multiple GPUs and servers, facilitating efficient inference at scale.

The Nemotron-4 340B Base model, pre-trained on an extensive dataset of 9 trillion tokens, can be fine-tuned using the NeMo framework to suit specific use cases. This customization can be achieved through methods like supervised fine-tuning and parameter-efficient techniques such as low-rank adaptation (LoRA).

To ensure model alignment, developers can utilize NeMo Aligner alongside datasets annotated by the Nemotron-4 340B Reward model. Alignment is crucial for fine-tuning model behavior using algorithms like reinforcement learning from human feedback (RLHF), ensuring outputs are safe, accurate, and contextually appropriate.

For enterprises seeking robust support and security, NeMo and TensorRT-LLM are available through the cloud-native NVIDIA AI Enterprise software platform, offering accelerated runtimes for generative AI models.

Ensuring Safety and Reliability

The Nemotron-4 340B Instruct model has undergone rigorous safety evaluations, including adversarial testing, and has demonstrated strong performance across various risk indicators. Nonetheless, users are encouraged to conduct thorough evaluations of the model's outputs to ensure the synthetic data meets their specific safety and accuracy requirements.

NVIDIA's introduction of Nemotron-4 340B marks a significant advancement in the realm of AI, offering developers powerful tools to generate high-quality synthetic data, optimize LLM training, and drive innovation across multiple industries.

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