8 citations · 11 across the 7 of their papers we have counts for
7 papers
LINA: Linear Autoregressive Image Generative Models with Continuous Tokens
Jiahao Wang, Ting Pan, Haoge Deng +4
Autoregressive models with continuous tokens form a promising paradigm for visual generation, especially for text-to-image (T2I) synthesis, but they suffer from high computational…
LiT: Delving into a Simple Linear Diffusion Transformer for Image Generation
Jiahao Wang, Ning Kang, Lewei Yao +12
In this paper, we investigate how to convert a pre-trained Diffusion Transformer (DiT) into a linear DiT, as its simplicity, parallelism, and efficiency for image generation. Throu…
PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models Quantization
Mengzhao Chen, Yi Liu, Jiahao Wang +3
Existing weight-activation quantization methods for Large Language Models (LLMs) primarily address channel-wise outliers but often neglect token-wise outliers, which limits the acc…
EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
Mengzhao Chen, Wenqi Shao, Peng Xu +4
Large language models (LLMs) are crucial in modern natural language processing and artificial intelligence. However, they face challenges in managing their significant memory requi…
Adapting LLaMA Decoder to Vision Transformer
Jiahao Wang, Wenqi Shao, Mengzhao Chen +7
This work examines whether decoder-only Transformers such as LLaMA, which were originally designed for large language models (LLMs), can be adapted to the computer vision field. We…
Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots
Chengyue Wu, Yixiao Ge, Qiushan Guo +5
The remarkable progress of Multi-modal Large Language Models (MLLMs) has attracted significant attention due to their superior performance in visual contexts. However, their capabi…
LLaMA Pro: Progressive LLaMA with Block Expansion
Chengyue Wu, Yukang Gan, Yixiao Ge +5
Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e.g., from LLaMA to CodeLLaMA. To this end, we propo…