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NVIDIA Launches Nemotron-4 340B for Synthetic Data Generation in AI Training
NVIDIA has introduced the Nemotron-4 340B, a new family of models designed to generate synthetic data for training large language models (LLMs) in various industries, including healthcare, finance, manufacturing and retail, according to the agency. NVIDIA Blog.
Navigating the Nemotron to generate synthetic data
High-quality training data is crucial to the performance and accuracy of customized LLMs. However, obtaining reliable datasets can be expensive and challenging. Nemotron-4 340B aims to solve this problem by providing developers with a free and scalable way to generate synthetic data through a permissive open model license.
The Nemotron-4 340B family includes basic, educational, and reward models optimized to work with NVIDIA NeMo and NVIDIA TensorRT-LLM. These models form a pipeline to generate synthetic data used for training and refining LLMs. Developers can download Nemotron-4 340B from Hugging face and models will soon be available on ai.nvidia.com.
Optimization with NeMo, optimization for inference with TensorRT-LLM
Using open source frameworks like NVIDIA NeMo and NVIDIA TensorRT-LLM, developers can optimize the efficiency of their instruction and reward models to generate synthetic data and score responses. All Nemotron-4 340B models are optimized with TensorRT-LLM to take advantage of tensor parallelism, enabling efficient inference at scale.
Nemotron-4 340B Base, trained on 9 trillion tokens, can be customized using the NeMo framework to fit specific use cases or domains. This refinement process takes advantage of extensive pre-training data, producing more accurate results for specific downstream tasks.
Customization methods available through the NeMo framework include supervised fine-tuning methods and efficient parameters such as low-rank adaptation (LoRA). Developers can also align their models with NeMo Aligner and annotated datasets from Nemotron-4 340B Reward to ensure accurate and contextually appropriate results.
Model security assessment and getting started guide
The Nemotron-4 340B Instruct model has undergone a thorough safety evaluation, including contradictory testing, and has performed well against various risk indicators. However, users should still carefully evaluate model results to ensure that synthetically generated data is suitable, safe, and accurate for their specific use case.
For more detailed information on model security and security evaluation, users can refer to the model sheet. Nemotron-4 340B models can be downloaded via Hugging Face. Researchers and developers interested in the underlying technology can also review research papers on the model and dataset.
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