NVIDIA has released its new large-scale language model Nemotron-4, a 15 billion parameter model that performs well in multiple benchmarks, outperforming competitors of the same size. The model is based on the standard pure decoder Transformer architecture and is trained using a multi-language and encoding dataset containing 8 trillion tokens. Its powerful performance covers common sense reasoning, mathematics and code, multi-language classification and generation, and machine translation.
The NVIDIA team launched a new model of 15 billion parameters, Nemotron-4, which performs well in English, multi-language and coding tasks, and beats models of the same parameter size on multiple evaluation benchmarks. Using a standard pure decoder Transformer architecture, the training data set contains 8 trillion tokens, covering multiple languages and encoded texts. Nemotron-415B has excellent performance in various task areas, including common sense reasoning, mathematics and coding, multi-language classification and generation, machine translation, etc. The author believes that Nemotron-415B is expected to become the best general-purpose large model that can run on a single NVIDIA A100 or H100 GPU.
The emergence of Nemotron-4 demonstrates NVIDIA's continued technological breakthroughs in the field of large-scale language models. Its advantages in single-GPU operation also make it have broad application prospects. It is worth looking forward to its further development and application in the field of artificial intelligence in the future.