We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward. Our models are open access under the NVIDIA Open Model License Agreement, a permissive model license that allows distribution, modification, and use of the models and its outputs. These models perform competitively to open access models on a wide range of evaluation benchmarks, and were sized to fit on a single DGX H100 with 8 GPUs when deployed in FP8 precision. We believe that the community can benefit from these models in various research studies and commercial applications, especially for generating synthetic data to train smaller language models. Notably, over 98% of data used in our model alignment process is synthetically generated, showcasing the effectiveness of these models in generating synthetic data. To further support open research and facilitate model development, we are also open-sourcing the synthetic data generation pipeline used in our model alignment process.

For example, (Hoffmann et al., 2022) shows that given two roughly IsoFLOP GPT models with a similar data distribution, a 65-billion-parameter model on 1.4 trillion tokens and a 280-billion-parameter model on 300 billion tokens, the 65B model has better accuracy on downstream tasks. This trade-off of allocating compute towards training on more data as opposed to increasing model size is particularly appealing from an inference perspective, reducing latency and the amount of compute needed to serve models. As a consequence, a major focus of language modeling training efforts has shifted to collecting high-quality multi-trillion token datasets from public sources such as Common Crawl.

We propose three novel pruning techniques to improve the cost and results of Inference-Aware Differentiable Neural Architecture Search (DNAS). First, we introduce Prunode, a stochastic bi-path building block for DNAS, which can search over inner hidden dimensions with O(1) memory and compute complexity. Second, we present an algorithm for pruning blocks within a stochastic layer of the SuperNet during the search. Third, we describe a novel technique for pruning unnecessary stochastic layers during the search. New concepts in NAS succeed with the evergrowing search space, increasing the dimensionality and complexity of the problem. Balancing the search-cost and quality of the search hence is essential for employing NAS in practice. Traditional NAS methods require evaluating many candidate networks to find optimized ones with respect to the desired metric. This approach can be successfully applied to simple problems like CIFAR-10 Krizhevsky et al. (2010), but for more demanding problems, these methods may turn out to be computationally prohibitive. To minimize this computational cost, recent research has focused on partial training Falkner et al. (2018); Li et al. (2020a); Luo et al. (2018), performing network morphism Cai et al. (2018a); Jin et al. (2019); Molchanov et al. (2021) instead of training from scratch, or training many candidates at the same time by sharing the weights Pham et al. (2018). These approaches can save computational time, but their reliability is questionable Bender et al. In our experiments, we focus on a search space based on a state-of-the-art network to showcase the value of our methodology.