4 code implementations • 8 Apr 2024 • Bo Peng, Daniel Goldstein, Quentin Anthony, Alon Albalak, Eric Alcaide, Stella Biderman, Eugene Cheah, Xingjian Du, Teddy Ferdinan, Haowen Hou, Przemysław Kazienko, Kranthi Kiran GV, Jan Kocoń, Bartłomiej Koptyra, Satyapriya Krishna, Ronald McClelland Jr., Niklas Muennighoff, Fares Obeid, Atsushi Saito, Guangyu Song, Haoqin Tu, Stanisław Woźniak, Ruichong Zhang, Bingchen Zhao, Qihang Zhao, Peng Zhou, Jian Zhu, Rui-Jie Zhu
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture.
no code implementations • 18 Dec 2023 • Bingchen Zhao, Haoqin Tu, Chen Wei, Jieru Mei, Cihang Xie
This paper introduces an efficient strategy to transform Large Language Models (LLMs) into Multi-Modal Large Language Models (MLLMs).
1 code implementation • 27 Nov 2023 • Haoqin Tu, Chenhang Cui, Zijun Wang, Yiyang Zhou, Bingchen Zhao, Junlin Han, Wangchunshu Zhou, Huaxiu Yao, Cihang Xie
Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness.
1 code implementation • 13 Sep 2023 • Haoqin Tu, Bingchen Zhao, Chen Wei, Cihang Xie
Multi-modal large language models (MLLMs) are trained based on large language models (LLM), with an enhanced capability to comprehend multi-modal inputs and generate textual responses.
1 code implementation • 29 Jun 2023 • Haoqin Tu, Bowen Yang, Xianfeng Zhao
Automatically generating textual content with desired attributes is an ambitious task that people have pursued long.
1 code implementation • 23 May 2023 • Haoqin Tu, Yitong Li, Fei Mi, Zhongliang Yang
To demonstrate the superiority and universality of the provided visual knowledge, we propose a simple but effective framework ReSee to add visual representation into vanilla dialogue models by modality concatenations.
1 code implementation • 15 Nov 2022 • Haoqin Tu, Yitong Li
Recent advances in neural-based generative modeling have reignited the hopes of having computer systems capable of conversing with humans and able to understand natural language.
1 code implementation • 7 Oct 2022 • Haoqin Tu, Zhongliang Yang, Jinshuai Yang, Siyu Zhang, Yongfeng Huang
Visualization of the local latent prior well confirms the primary devotion in hidden space of the proposed model.
1 code implementation • 12 May 2022 • Haoqin Tu, Zhongliang Yang, Jinshuai Yang, Yongfeng Huang
Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in achieving representation learning and generation for natural language at the same time.