NeMo, developed by NVIDIA, is a generative AI framework targeting researchers and developers who use PyTorch. Its core purpose is to provide a robust and scalable framework to facilitate the design and implementation of generative AI models. NeMo simplifies access to pre-existing code and pretrained models, helping users from both industry and academia accelerate their development processes. The developer guide offers extensive technical details regarding NeMo’s design, implementation, and optimizations.
The NeMo Framework Launcher enhances the user experience by providing a user-friendly interface for managing and organizing experiments across various frameworks. It simplifies the interaction with the NeMo Framework, making it more accessible and efficient for conducting AI research and development.
The NeMo Aligner is a comprehensive toolkit designed for efficient model alignment. It supports advanced alignment algorithms, including SteerLM, DPO, and Reinforcement Learning from Human Feedback (RLHF), which help in tuning language models to be safer, less harmful, and more beneficial. The toolkit caters to a wide range of model sizes and employs parallelism techniques to optimize performance and resource efficiency in model alignment tasks.
The NeMo Curator is a Python library composed of several scalable data-mining modules, specifically designed for curating Natural Language Processing (NLP) data to train large language models (LLMs). It enables NLP researchers to extract high-quality text from vast, uncurated web corpora efficiently, supporting the development of more accurate and powerful language models.