NVIDIA has launched Nemotron-4 340B, a series of open models crafted to produce synthetic data for training large language models (LLMs). The initiative targets increasing demands for quality training data in fields such as healthcare, finance, manufacturing, and retail.
NVIDIA Nemotron-4 340B for AI Training Data Generation
The Nemotron-4 340B series includes base, instruct, and reward models. Instruct models are fine-tuned to follow specific human instructions, making them highly effective for tasks that require clear, directive responses. They are trained to understand and execute user commands accurately. Reward models, on the other hand, are designed to evaluate the quality of generated outputs. They assign scores to these outputs based on how well they align with desired outcomes or criteria, helping refine and improve AI systems’ performance by promoting high-quality responses.
The models used in the Nemotron-4 340B series are designed to integrate smoothly with NVIDIA NeMo, an open-source platform for comprehensive model training, and TensorRT-LLM, a library for optimizing inference. Users can download these models from Hugging Face, with additional availability on ai.nvidia.com within the NVIDIA NIM microservice.
As the shortage of high-grade training data for LLMs is becoming increasingly pressing, some experts predict that demand may soon exceed supply. The generation rate of high-quality data can’t keep pace with LLM companies’ “consumption”. Nemotron-4 340B aims to bridge this gap by offering scalable synthetic data production.
The Nemotron-4 340B Reward model has attained top position on the Hugging Face RewardBench leaderboard, which benchmarks reward models. This accolade signifies the model’s proficiency in producing high-quality synthetic data for AI training.
Creating Synthetic Data
In Nemotron-4 340B, the synthetic data generation starts with the Nemotron-4 340B Instruct model, which synthesizes data reflecting real-world contexts. Following this, the Nemotron-4 340B Reward model assesses the generated data on parameters like helpfulness, accuracy, coherence, complexity, and verbosity, ensuring high-quality outputs that improve custom LLM performance in various domains.
Researchers can personalize the Nemotron-4 340B Base model by incorporating proprietary data and the HelpSteer2 dataset. This adaptability allows for tailored instruct or reward models fitting specific needs. Fine-tuning, which involves tweaking a pre-trained model with a smaller dataset devoted to a particular task, enhances task-specific performance.
NVIDIA NeMo and TensorRT-LLM are essential for optimizing model efficiency. TensorRT-LLM uses tensor parallelism, distributing weight matrices over multiple GPUs and servers to support scalable inference. The Nemotron-4 340B Base model, trained on 9 trillion tokens, can be fine-tuned for particular applications with techniques such as low-rank adaptation (LoRA).
Model Quality and Safety
To improve model precision, developers can align their models using NeMo Aligner and datasets annotated by Nemotron-4 340B Reward. This process, which includes reinforcement learning from human feedback (RLHF), ensures the models’ outputs remain safe, accurate, and contextually suitable. Intensive safety testing, including adversarial checks, was conducted on the Nemotron-4 340B Instruct model.
For companies needing robust support and security, NeMo and TensorRT-LLM are accessible via the NVIDIA AI Enterprise software platform. This platform provides advanced runtimes for generative AI foundation models.
Last Updated on November 18, 2024 2:03 pm CET
