Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Pang, Ruoming"'
Autor:
Sun, Haotian, Lei, Tao, Zhang, Bowen, Li, Yanghao, Huang, Haoshuo, Pang, Ruoming, Dai, Bo, Du, Nan
Diffusion transformers have been widely adopted for text-to-image synthesis. While scaling these models up to billions of parameters shows promise, the effectiveness of scaling beyond current sizes remains underexplored and challenging. By explicitly
Externí odkaz:
http://arxiv.org/abs/2410.02098
Autor:
Feng, Shengyu, Kong, Xiang, Ma, Shuang, Zhang, Aonan, Yin, Dong, Wang, Chong, Pang, Ruoming, Yang, Yiming
Augmenting the multi-step reasoning abilities of Large Language Models (LLMs) has been a persistent challenge. Recently, verification has shown promise in improving solution consistency by evaluating generated outputs. However, current verification a
Externí odkaz:
http://arxiv.org/abs/2410.01920
Autor:
Lu, Jiarui, Holleis, Thomas, Zhang, Yizhe, Aumayer, Bernhard, Nan, Feng, Bai, Felix, Ma, Shuang, Ma, Shen, Li, Mengyu, Yin, Guoli, Wang, Zirui, Pang, Ruoming
Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation of tool-use capabilities. While previous works focused on either evalua
Externí odkaz:
http://arxiv.org/abs/2408.04682
Autor:
Gunter, Tom, Wang, Zirui, Wang, Chong, Pang, Ruoming, Narayanan, Andy, Zhang, Aonan, Zhang, Bowen, Chen, Chen, Chiu, Chung-Cheng, Qiu, David, Gopinath, Deepak, Yap, Dian Ang, Yin, Dong, Nan, Feng, Weers, Floris, Yin, Guoli, Huang, Haoshuo, Wang, Jianyu, Lu, Jiarui, Peebles, John, Ye, Ke, Lee, Mark, Du, Nan, Chen, Qibin, Keunebroek, Quentin, Wiseman, Sam, Evans, Syd, Lei, Tao, Rathod, Vivek, Kong, Xiang, Du, Xianzhi, Li, Yanghao, Wang, Yongqiang, Gao, Yuan, Ahmed, Zaid, Xu, Zhaoyang, Lu, Zhiyun, Rashid, Al, Jose, Albin Madappally, Doane, Alec, Bencomo, Alfredo, Vanderby, Allison, Hansen, Andrew, Jain, Ankur, Anupama, Anupama Mann, Kamal, Areeba, Wu, Bugu, Brum, Carolina, Maalouf, Charlie, Erdenebileg, Chinguun, Dulhanty, Chris, Moritz, Dominik, Kang, Doug, Jimenez, Eduardo, Ladd, Evan, Shi, Fangping, Bai, Felix, Chu, Frank, Hohman, Fred, Kotek, Hadas, Coleman, Hannah Gillis, Li, Jane, Bigham, Jeffrey, Cao, Jeffery, Lai, Jeff, Cheung, Jessica, Shan, Jiulong, Zhou, Joe, Li, John, Qin, Jun, Singh, Karanjeet, Vega, Karla, Zou, Kelvin, Heckman, Laura, Gardiner, Lauren, Bowler, Margit, Cordell, Maria, Cao, Meng, Hay, Nicole, Shahdadpuri, Nilesh, Godwin, Otto, Dighe, Pranay, Rachapudi, Pushyami, Tantawi, Ramsey, Frigg, Roman, Davarnia, Sam, Shah, Sanskruti, Guha, Saptarshi, Sirovica, Sasha, Ma, Shen, Ma, Shuang, Wang, Simon, Kim, Sulgi, Jayaram, Suma, Shankar, Vaishaal, Paidi, Varsha, Kumar, Vivek, Wang, Xin, Zheng, Xin, Cheng, Walker, Shrager, Yael, Ye, Yang, Tanaka, Yasu, Guo, Yihao, Meng, Yunsong, Luo, Zhao Tang, Ouyang, Zhi, Aygar, Alp, Wan, Alvin, Walkingshaw, Andrew, Lin, Antonie, Farooq, Arsalan, Ramerth, Brent, Reed, Colorado, Bartels, Chris, Chaney, Chris, Riazati, David, Yang, Eric Liang, Feldman, Erin, Hochstrasser, Gabriel, Seguin, Guillaume, Belousova, Irina, Pelemans, Joris, Yang, Karen, Vahid, Keivan Alizadeh, Cao, Liangliang, Najibi, Mahyar, Zuliani, Marco, Horton, Max, Cho, Minsik, Bhendawade, Nikhil, Dong, Patrick, Maj, Piotr, Agrawal, Pulkit, Shan, Qi, Fu, Qichen, Poston, Regan, Xu, Sam, Liu, Shuangning, Rao, Sushma, Heeramun, Tashweena, Merth, Thomas, Rayala, Uday, Cui, Victor, Sridhar, Vivek Rangarajan, Zhang, Wencong, Zhang, Wenqi, Wu, Wentao, Zhou, Xingyu, Liu, Xinwen, Zhao, Yang, Xia, Yin, Ren, Zhile, Ren, Zhongzheng
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These mode
Externí odkaz:
http://arxiv.org/abs/2407.21075
Autor:
Yin, Guoli, Bai, Haoping, Ma, Shuang, Nan, Feng, Sun, Yanchao, Xu, Zhaoyang, Ma, Shen, Lu, Jiarui, Kong, Xiang, Zhang, Aonan, Yap, Dian Ang, zhang, Yizhe, Ahnert, Karsten, Kamath, Vik, Berglund, Mathias, Walsh, Dominic, Gindele, Tobias, Wiest, Juergen, Lai, Zhengfeng, Wang, Xiaoming, Shan, Jiulong, Cao, Meng, Pang, Ruoming, Wang, Zirui
Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, while useful, often focus on specific application scenarios, emphasizing
Externí odkaz:
http://arxiv.org/abs/2407.18961
Large Language Model (LLM) pre-training exhausts an ever growing compute budget, yet recent research has demonstrated that careful document selection enables comparable model quality with only a fraction of the FLOPs. Inspired by efforts suggesting t
Externí odkaz:
http://arxiv.org/abs/2406.04638
Autor:
Du, Xianzhi, Gunter, Tom, Kong, Xiang, Lee, Mark, Wang, Zirui, Zhang, Aonan, Du, Nan, Pang, Ruoming
Mixture-of-Experts (MoE) enjoys performance gain by increasing model capacity while keeping computation cost constant. When comparing MoE to dense models, prior work typically adopt the following setting: 1) use FLOPs or activated parameters as a mea
Externí odkaz:
http://arxiv.org/abs/2405.15052
Autor:
McKinzie, Brandon, Gan, Zhe, Fauconnier, Jean-Philippe, Dodge, Sam, Zhang, Bowen, Dufter, Philipp, Shah, Dhruti, Du, Xianzhi, Peng, Futang, Weers, Floris, Belyi, Anton, Zhang, Haotian, Singh, Karanjeet, Kang, Doug, Jain, Ankur, Hè, Hongyu, Schwarzer, Max, Gunter, Tom, Kong, Xiang, Zhang, Aonan, Wang, Jianyu, Wang, Chong, Du, Nan, Lei, Tao, Wiseman, Sam, Yin, Guoli, Lee, Mark, Wang, Zirui, Pang, Ruoming, Grasch, Peter, Toshev, Alexander, Yang, Yinfei
In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the v
Externí odkaz:
http://arxiv.org/abs/2403.09611
Conventional end-to-end Automatic Speech Recognition (ASR) models primarily focus on exact transcription tasks, lacking flexibility for nuanced user interactions. With the advent of Large Language Models (LLMs) in speech processing, more organic, tex
Externí odkaz:
http://arxiv.org/abs/2309.09843
Autor:
Daxberger, Erik, Weers, Floris, Zhang, Bowen, Gunter, Tom, Pang, Ruoming, Eichner, Marcin, Emmersberger, Michael, Yang, Yinfei, Toshev, Alexander, Du, Xianzhi
Sparse Mixture-of-Experts models (MoEs) have recently gained popularity due to their ability to decouple model size from inference efficiency by only activating a small subset of the model parameters for any given input token. As such, sparse MoEs ha
Externí odkaz:
http://arxiv.org/abs/2309.04354