1.3k citations · 1.8k across the 82 of their papers we have counts for
16 papers · 1 filter
SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing
Xueting Li, Ye Yuan, Shalini De Mello +5
We introduce SimAvatar, a framework designed to generate simulation-ready clothed 3D human avatars from a text prompt. Current text-driven human avatar generation methods either mo…
RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models
Greg Heinrich, Mike Ranzinger, Hongxu +6
Agglomerative models have recently emerged as a powerful approach to training vision foundation models, leveraging multi-teacher distillation from existing models such as CLIP, DIN…
NVILA: Efficient Frontier Visual Language Models
Zhijian Liu, Ligeng Zhu, Baifeng Shi +24
Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a…
Hymba: A Hybrid-head Architecture for Small Language Models
Xin Dong, Yonggan Fu, Shizhe Diao +10
We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) fo…
EoRA: Fine-tuning-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation
Shih-Yang Liu, Maksim Khadkevich, Nai Chit Fung +10
While post-training compression techniques effectively reduce the memory footprint, latency, and power consumption of Large Language Models (LLMs), they often result in noticeable…
MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models
Gongfan Fang, Hongxu Yin, Saurav Muralidharan +5
Large Language Models (LLMs) are distinguished by their massive parameter counts, which typically result in significant redundancy. This work introduces MaskLLM, a learnable prunin…
COIN: Control-Inpainting Diffusion Prior for Human and Camera Motion Estimation
Jiefeng Li, Ye Yuan, Davis Rempe +5
Estimating global human motion from moving cameras is challenging due to the entanglement of human and camera motions. To mitigate the ambiguity, existing methods leverage learned…
LLM Pruning and Distillation in Practice: The Minitron Approach
Sharath Turuvekere Sreenivas, Saurav Muralidharan, Raviraj Joshi +19
We present a comprehensive report on compressing the Llama 3.1 8B and Mistral NeMo 12B models to 4B and 8B parameters, respectively, using pruning and distillation. We explore two…
LongVILA: Scaling Long-Context Visual Language Models for Long Videos
Yukang Chen, Fuzhao Xue, Dacheng Li +15
Long-context capability is critical for multi-modal foundation models, especially for long video understanding. We introduce LongVILA, a full-stack solution for long-context visual…
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…
VILA: VILA Augmented VILA
Yunhao Fang, Ligeng Zhu, Yao Lu +7
While visual language model architectures and training infrastructures advance rapidly, data curation remains under-explored where quantity and quality become a bottleneck. Existin…
Compact Language Models via Pruning and Knowledge Distillation
Saurav Muralidharan, Sharath Turuvekere Sreenivas, Raviraj Joshi +6
Large language models (LLMs) targeting different deployment scales and sizes are currently produced by training each variant from scratch; this is extremely compute-intensive. In t…
An Empirical Study of Mamba-based Language Models
Roger Waleffe, Wonmin Byeon, Duncan Riach +13
Selective state-space models (SSMs) like Mamba overcome some of the shortcomings of Transformers, such as quadratic computational complexity with sequence length and large inferenc…
Flextron: Many-in-One Flexible Large Language Model
Ruisi Cai, Saurav Muralidharan, Greg Heinrich +4
Training modern LLMs is extremely resource intensive, and customizing them for various deployment scenarios characterized by limited compute and memory resources through repeated t…
X-VILA: Cross-Modality Alignment for Large Language Model
Hanrong Ye, De-An Huang, Yao Lu +8
We introduce X-VILA, an omni-modality model designed to extend the capabilities of large language models (LLMs) by incorporating image, video, and audio modalities. By aligning mod…
LITA: Language Instructed Temporal-Localization Assistant
De-An Huang, Shijia Liao, Subhashree Radhakrishnan +4
There has been tremendous progress in multimodal Large Language Models (LLMs). Recent works have extended these models to video input with promising instruction following capabilit…