NVIDIA Nemotron Nano V2 VL
NVIDIA: Amala Sanjay Deshmukh, Kateryna Chumachenko, Tuomas Rintamaki, Matthieu Le, Tyler Poon, Danial Mohseni Taheri, Ilia Karmanov, Guilin Liu, Jarno Seppanen, Guo Chen, Karan Sapra, Zhiding Yu, Adi Renduchintala, Charles Wang, Peter Jin, Arushi Goel, Mike Ranzinger

TL;DR
Nemotron Nano V2 VL is a new vision-language model optimized for real-world document and video understanding, offering significant improvements over previous models through architecture, dataset, and training enhancements.
Contribution
Introduces Nemotron Nano V2 VL with advanced architecture, token reduction, and training methods for improved long document and video comprehension.
Findings
Enhanced inference throughput in long scenarios
Significant performance improvements over previous models
Open release of datasets, recipes, and training code
Abstract
We introduce Nemotron Nano V2 VL, the latest model of the Nemotron vision-language series designed for strong real-world document understanding, long video comprehension, and reasoning tasks. Nemotron Nano V2 VL delivers significant improvements over our previous model, Llama-3.1-Nemotron-Nano-VL-8B, across all vision and text domains through major enhancements in model architecture, datasets, and training recipes. Nemotron Nano V2 VL builds on Nemotron Nano V2, a hybrid Mamba-Transformer LLM, and innovative token reduction techniques to achieve higher inference throughput in long document and video scenarios. We are releasing model checkpoints in BF16, FP8, and FP4 formats and sharing large parts of our datasets, recipes and training code.
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Taxonomy
TopicsMultimodal Machine Learning Applications · Generative Adversarial Networks and Image Synthesis · Domain Adaptation and Few-Shot Learning
