15 citations · 17 across the 11 of their papers we have counts for
4 papers · 1 filter
Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
NVIDIA, :, Amala Sanjay Deshmukh +204
We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 N…
Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation
Peter Belcak, Greg Heinrich, Jan Kautz +1
Finetuning language models for a new domain inevitably leads to the deterioration of their general performance. This becomes more pronounced the more limited the finetuning data re…
PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation
Mike Ranzinger, Jon Barker, Greg Heinrich +3
Various visual foundation models have distinct strengths and weaknesses, both of which can be improved through heterogeneous multi-teacher knowledge distillation without labels, te…
A deeper look at depth pruning of LLMs
Shoaib Ahmed Siddiqui, Xin Dong, Greg Heinrich +4
Large Language Models (LLMs) are not only resource-intensive to train but even more costly to deploy in production. Therefore, recent work has attempted to prune blocks of LLMs bas…