The development of artificial intelligence models that can handle both human language and code has been a significant focus for researchers. The goal is to create models that break down linguistic barriers and facilitate more intuitive interactions between humans and machines. This challenge encompasses understanding multiple languages and the intricate syntax and semantics of programming languages.
Previous methods of addressing this issue have involved training extensive models on diverse datasets containing numerous languages and code snippets. However, these efforts often required refinement in terms of the breadth of languages covered and the consistency of performance across different tasks. NVIDIA recently introduced the Nemotron-4 15B model as a groundbreaking solution to these challenges. This model, with 15 billion parameters, has been trained on an unprecedented eight trillion tokens, covering English, a wide range of natural languages, and programming languages.
Nemotron-4 15B’s training methodology uses a standard decoder-only Transformer architecture, optimized with Rotary Position embedding and a SentencePiece tokenizer, enhancing its understanding and generating abilities. This methodology, combined with the strategic selection and processing of training data, ensures that Nemotron-4 15B learns from a vast array of sources efficiently. In addition, this method minimizes redundancy and maximizes the coverage of low-resource languages.
Comprehensive evaluations showed that Nemotron-4 15B demonstrated superior proficiency in English, coding tasks, and multilingual benchmarks. The model significantly outperformed the LLaMA-2 34B model in multilingual capabilities, even though the latter has over twice the number of parameters. Specifically, in coding tasks, Nemotron-4 15B displayed better average accuracy than models specialized in code, such as Starcoder. It also surpassed in performance in low-resource programming languages, and set new records in multilingual evaluations.
NVIDIA’s Nemotron-4 15B is a major advancement in the development of AI models, mastering the dual challenges of multilingual text understanding and programming language interpretation. The model’s training methodology and its outstanding performance highlight the potential of large language models to transform our interaction with technology, making it more inclusive and efficient globally. Examples of potential applications include global communication, accessible coding education, and enhanced machine-human interactions across different languages and cultures.
