Nemotron-4 340B

Nemotron-4 340B is a series of open models released by NVIDIA designed for generating synthetic data to train large language models (LLMs). These models are optimized for use with NVIDIA NeMo and NVIDIA TensorRT-LLM to improve training and inference efficiency.

Nemotron-4 340B includes base, instruction, and reward models, forming a pipeline that generates synthetic data for training and refining LLMs. These models are available for download on Hugging Face and will soon be available on ai.nvidia.com as part of the NVIDIA NIM microservices.

Demand group:

The Nemotron-4 340B model is suitable for developers and researchers who need to train large language models, especially when access to large, diverse labeled datasets is limited. It provides commercial applications with a free, scalable way to generate synthetic data, helping to build powerful LLMs.

Example of usage scenario:

In the healthcare industry, synthetic data generated by Nemotron-4 340B is used to train customized LLMs to improve the accuracy and response quality of medical consultations.

The financial industry uses the data generated by Nemotron-4 340B to train risk assessment models and enhance its ability to predict market dynamics.

The retail industry uses the data generated by the Nemotron-4 340B model to optimize the conversational capabilities of customer service robots and improve customer experience.

Product features:

Generate synthetic data to simulate the characteristics of real-world data and improve the data quality and performance of custom LLMs.

High-quality responses were screened using the Nemotron-4 340B reward model, scored based on five attributes: helpfulness, correctness, coherence, complexity, and redundancy.

Researchers can create their own instruction or reward models by customizing the Nemotron-4 340B base model and the HelpSteer2 dataset.

Optimize the efficiency of instruction and reward models, generate synthetic data, and score responses using open source NVIDIA NeMo and NVIDIA TensorRT-LLM.

Leverage tensor parallelism to optimize all Nemotron-4 340B models with TensorRT-LLM for large-scale inference.

Nemotron-4 340B base model is trained with 9 trillion tokens and can be customized through the NeMo framework to suit specific use cases or domains.

Align models with NeMo Aligner and Nemotron-4 340B reward model-annotated datasets to ensure output is safe, accurate, contextually appropriate, and consistent with intended goals.

Usage tutorial:

Download the Nemotron-4 340B model from Hugging Face.

The Nemotron-4 340B base model is customized using the NeMo framework according to the needs of a specific use case or domain.

Utilize the Nemotron-4 340B instruction model to generate synthetic data that simulates the characteristics of real-world data.

AI-generated data is screened and scored for quality using the Nemotron-4 340B reward model.

Align the model with NeMo Aligner and annotated data sets to ensure the safety and accuracy of the output.

Deploy customized models as NVIDIA NIM microservices and deploy them anywhere through standard application programming interfaces.