The Nemotron-4 340B Instruct model generates diverse synthetic data, while the Nemotron-4 340B Reward model filters for high-quality responses. Developers can customize the Nemotron-4 340B Base model using their proprietary data and the included HelpSteer2 dataset. Using NVIDIA NeMo and NVIDIA TensorRT-LLM, developers can optimize their models to generate synthetic data and score responses. The Nemotron-4 340B Instruct model underwent extensive safety evaluation, including adversarial tests. However, users are advised to evaluate the model’s outputs for their specific use case.
Key takeaways:
- NVIDIA has announced Nemotron-4 340B, a family of open models that can be used to generate synthetic data for training large language models (LLMs) for various commercial applications.
- The Nemotron-4 340B family includes base, instruct and reward models that are optimized to work with NVIDIA NeMo, an open-source framework for end-to-end model training.
- The Nemotron-4 340B models can be customized using the NeMo framework to adapt to specific use cases or domains, and are optimized with TensorRT-LLM to take advantage of tensor parallelism.
- The Nemotron-4 340B Instruct model underwent extensive safety evaluation, including adversarial tests, and performed well across a wide range of risk indicators. However, users are advised to perform careful evaluation of the model’s outputs to ensure the synthetically generated data is suitable, safe and accurate for their use case.