1 citations · 1 across the 6 of their papers we have counts for
6 papers
Yuan3.0 Flash: An Open Multimodal Large Language Model for Enterprise Applications
YuanLab. ai, :, Shawn Wu +24
We introduce Yuan3.0 Flash, an open-source Mixture-of-Experts (MoE) MultiModal Large Language Model featuring 3.7B activated parameters and 40B total parameters, specifically desig…
Yuan3.0 Ultra: A Trillion-Parameter Enterprise-Oriented MoE LLM
YuanLab. ai, :, Shawn Wu +25
We introduce Yuan3.0 Ultra, an open-source Mixture-of-Experts (MoE) large language model featuring 68.8B activated parameters and 1010B total parameters, specially designed to enha…
Yuan-TecSwin: A text conditioned Diffusion model with Swin-transformer blocks
Shaohua Wu, Tong Yu, Shenling Wang +1
Diffusion models have shown remarkable capacity in image synthesis based on their U-shaped architecture and convolutional neural networks (CNN) as basic blocks. The locality of the…
PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs
Tongzhou Yu, Zhuhao Zhang, Guanghui Zhu +3
Parameter Efficient Fine-Tuning (PEFT) methods have emerged as effective and promising approaches for fine-tuning pre-trained language models. Compared with Full parameter Fine-Tun…
Yuan 2.0-M32: Mixture of Experts with Attention Router
Shaohua Wu, Jiangang Luo, Xi Chen +12
Yuan 2.0-M32, with a similar base architecture as Yuan-2.0 2B, uses a mixture-of-experts architecture with 32 experts of which 2 experts are active. A new router network, Attention…
YUAN 2.0: A Large Language Model with Localized Filtering-based Attention
Shaohua Wu, Xudong Zhao, Shenling Wang +9
In this work, we develop and release Yuan 2.0, a series of large language models with parameters ranging from 2.1 billion to 102.6 billion. The Localized Filtering-based Attention…