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

NVIDIA has introduced the discharge of Nemotron-CC, a groundbreaking 6.3-trillion-token English language dataset designed to advance the pretraining of huge language fashions (LLMs). This dataset, derived from Widespread Crawl, goals to raise the accuracy and effectivity of LLMs by way of modern knowledge curation methods, together with using 1.9 trillion tokens of synthetically generated knowledge, based on NVIDIA.

Enhancing LLM Pretraining

NVIDIA’s initiative addresses a vital want in LLM coaching, the place the standard of pretraining datasets performs a pivotal function. Whereas latest fashions like Meta’s Llama sequence have been primarily based on datasets comprising as much as 15 trillion tokens, the precise composition of those datasets stays largely undisclosed. Nemotron-CC seeks to fill this hole by offering the broader neighborhood with a high-quality dataset able to supporting each brief and lengthy token horizon coaching.

Conventional datasets typically sacrifice as much as 90% of information to enhance benchmark accuracies, limiting their utility for in depth coaching. Nemotron-CC, nevertheless, demonstrates easy methods to remodel Widespread Crawl knowledge right into a superior dataset, surpassing even the Llama 3.1 8B mannequin by way of superior strategies corresponding to classifier ensembling and artificial knowledge rephrasing.

Important Outcomes

Nemotron-CC’s efficacy is evidenced by its efficiency in varied benchmarks. When coaching 8B parameter fashions for one trillion tokens, the high-quality subset Nemotron-CC-HQ outperforms main datasets like DCLM, growing MMLU scores by 5.6 factors. Moreover, the entire 6.3-trillion-token dataset matches DCLM on MMLU whereas providing 4 occasions extra distinctive actual tokens. This allows efficient coaching over lengthy token horizons, with Nemotron-CC-trained fashions surpassing Llama 3.1 8B in a number of metrics, together with a 5-point enhance in MMLU and a 3.1-point rise in ARC-Problem scores.

Revolutionary Knowledge Curation Methods

The event of Nemotron-CC concerned a number of key insights. By ensembling completely different model-based classifiers, NVIDIA was in a position to choose a broader array of high-quality tokens. Moreover, rephrasing methods decreased noise and errors, yielding various and precious knowledge variants. The choice to disable conventional heuristic filters additional boosted the dataset’s high quality with out compromising accuracy.

NVIDIA utilized its NeMo Curator software to extract and refine knowledge from Widespread Crawl, making use of filters for language, deduplication, and high quality classification. This course of was complemented by artificial knowledge era, contributing roughly two trillion tokens to the dataset.

Future Prospects

Nemotron-CC is positioned as an important useful resource for pretraining state-of-the-art LLMs over various token horizons. NVIDIA plans to increase its choices by releasing extra specialised datasets, together with these centered on particular domains like arithmetic, to additional improve LLM capabilities.

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