NVIDIA Introduces Nemotron-CC: A Massive Dataset for LLM Pretraining

NVIDIA has announced the release of Nemotron-CC, a groundbreaking 6.3-trillion-token English language dataset designed to advance the pretraining of large language models (LLMs). This dataset, derived from Common Crawl, aims to elevate the accuracy and efficiency of LLMs through innovative data curation techniques, including the use of 1.9 trillion tokens of synthetically generated data, according to NVIDIA.

Enhancing LLM Pretraining

NVIDIA’s initiative addresses a critical need in LLM training, where the quality of pretraining datasets plays a pivotal role. While recent models like Meta’s Llama series have been based on datasets comprising up to 15 trillion tokens, the exact composition of these datasets remains largely undisclosed. Nemotron-CC seeks to fill this gap by providing the wider community with a high-quality dataset capable of supporting both short and long token horizon training.

Traditional datasets often sacrifice up to 90% of data to improve benchmark accuracies, limiting their utility for extensive training. Nemotron-CC, however, demonstrates how to transform Common Crawl data into a superior dataset, surpassing even the Llama 3.1 8B model through advanced methods such as classifier ensembling and synthetic data rephrasing.

Significant Results

Nemotron-CC’s efficacy is evidenced by its performance in various benchmarks. When training 8B parameter models for one trillion tokens, the high-quality subset Nemotron-CC-HQ outperforms leading datasets like DCLM, increasing MMLU scores by 5.6 points. Furthermore, the complete 6.3-trillion-token dataset matches DCLM on MMLU while offering four times more unique real tokens. This enables effective training over long token horizons, with Nemotron-CC-trained models surpassing Llama 3.1 8B in multiple metrics, including a 5-point increase in MMLU and a 3.1-point rise in ARC-Challenge scores.

Innovative Data Curation Techniques

The development of Nemotron-CC involved several key insights. By ensembling different model-based classifiers, NVIDIA was able to select a broader array of high-quality tokens. Additionally, rephrasing techniques reduced noise and errors, yielding diverse and valuable data variants. The decision to disable traditional heuristic filters further boosted the dataset’s quality without compromising accuracy.

NVIDIA utilized its NeMo Curator tool to extract and refine data from Common Crawl, applying filters for language, deduplication, and quality classification. This process was complemented by synthetic data generation, contributing approximately two trillion tokens to the dataset.

Future Prospects

Nemotron-CC is positioned as a vital resource for pretraining state-of-the-art LLMs over varying token horizons. NVIDIA plans to expand its offerings by releasing more specialized datasets, including those focused on specific domains like mathematics, to further enhance LLM capabilities.

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