Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Zebaze, Armel"'
Large Language Models (LLMs) have demonstrated remarkable performance across multiple tasks through in-context learning. For complex reasoning tasks that require step-by-step thinking, Chain-of-Thought (CoT) prompting has given impressive results, es
Externí odkaz:
http://arxiv.org/abs/2410.06634
The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural language processing tasks. In this paper, we focus on machine translat
Externí odkaz:
http://arxiv.org/abs/2408.00397
Autor:
Zhuo, Terry Yue, Vu, Minh Chien, Chim, Jenny, Hu, Han, Yu, Wenhao, Widyasari, Ratnadira, Yusuf, Imam Nur Bani, Zhan, Haolan, He, Junda, Paul, Indraneil, Brunner, Simon, Gong, Chen, Hoang, Thong, Zebaze, Armel Randy, Hong, Xiaoheng, Li, Wen-Ding, Kaddour, Jean, Xu, Ming, Zhang, Zhihan, Yadav, Prateek, Jain, Naman, Gu, Alex, Cheng, Zhoujun, Liu, Jiawei, Liu, Qian, Wang, Zijian, Lo, David, Hui, Binyuan, Muennighoff, Niklas, Fried, Daniel, Du, Xiaoning, de Vries, Harm, Von Werra, Leandro
Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to general-purpose reasoning. While current benchmarks have shown that LL
Externí odkaz:
http://arxiv.org/abs/2406.15877
Autor:
Futeral, Matthieu, Zebaze, Armel, Suarez, Pedro Ortiz, Abadji, Julien, Lacroix, Rémi, Schmid, Cordelia, Bawden, Rachel, Sagot, Benoît
Multimodal Large Language Models (mLLMs) are trained on a large amount of text-image data. While most mLLMs are trained on caption-like data only, Alayrac et al. [2022] showed that additionally training them on interleaved sequences of text and image
Externí odkaz:
http://arxiv.org/abs/2406.08707
Autor:
Lozhkov, Anton, Li, Raymond, Allal, Loubna Ben, Cassano, Federico, Lamy-Poirier, Joel, Tazi, Nouamane, Tang, Ao, Pykhtar, Dmytro, Liu, Jiawei, Wei, Yuxiang, Liu, Tianyang, Tian, Max, Kocetkov, Denis, Zucker, Arthur, Belkada, Younes, Wang, Zijian, Liu, Qian, Abulkhanov, Dmitry, Paul, Indraneil, Li, Zhuang, Li, Wen-Ding, Risdal, Megan, Li, Jia, Zhu, Jian, Zhuo, Terry Yue, Zheltonozhskii, Evgenii, Dade, Nii Osae Osae, Yu, Wenhao, Krauß, Lucas, Jain, Naman, Su, Yixuan, He, Xuanli, Dey, Manan, Abati, Edoardo, Chai, Yekun, Muennighoff, Niklas, Tang, Xiangru, Oblokulov, Muhtasham, Akiki, Christopher, Marone, Marc, Mou, Chenghao, Mishra, Mayank, Gu, Alex, Hui, Binyuan, Dao, Tri, Zebaze, Armel, Dehaene, Olivier, Patry, Nicolas, Xu, Canwen, McAuley, Julian, Hu, Han, Scholak, Torsten, Paquet, Sebastien, Robinson, Jennifer, Anderson, Carolyn Jane, Chapados, Nicolas, Patwary, Mostofa, Tajbakhsh, Nima, Jernite, Yacine, Ferrandis, Carlos Muñoz, Zhang, Lingming, Hughes, Sean, Wolf, Thomas, Guha, Arjun, von Werra, Leandro, de Vries, Harm
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digita
Externí odkaz:
http://arxiv.org/abs/2402.19173
Autor:
Zhuo, Terry Yue, Zebaze, Armel, Suppattarachai, Nitchakarn, von Werra, Leandro, de Vries, Harm, Liu, Qian, Muennighoff, Niklas
The high cost of full-parameter fine-tuning (FFT) of Large Language Models (LLMs) has led to a series of parameter-efficient fine-tuning (PEFT) methods. However, it remains unclear which methods provide the best cost-performance trade-off at differen
Externí odkaz:
http://arxiv.org/abs/2401.00788
Autor:
Muennighoff, Niklas, Liu, Qian, Zebaze, Armel, Zheng, Qinkai, Hui, Binyuan, Zhuo, Terry Yue, Singh, Swayam, Tang, Xiangru, von Werra, Leandro, Longpre, Shayne
Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human inst
Externí odkaz:
http://arxiv.org/abs/2308.07124
Autor:
Li, Raymond, Allal, Loubna Ben, Zi, Yangtian, Muennighoff, Niklas, Kocetkov, Denis, Mou, Chenghao, Marone, Marc, Akiki, Christopher, Li, Jia, Chim, Jenny, Liu, Qian, Zheltonozhskii, Evgenii, Zhuo, Terry Yue, Wang, Thomas, Dehaene, Olivier, Davaadorj, Mishig, Lamy-Poirier, Joel, Monteiro, João, Shliazhko, Oleh, Gontier, Nicolas, Meade, Nicholas, Zebaze, Armel, Yee, Ming-Ho, Umapathi, Logesh Kumar, Zhu, Jian, Lipkin, Benjamin, Oblokulov, Muhtasham, Wang, Zhiruo, Murthy, Rudra, Stillerman, Jason, Patel, Siva Sankalp, Abulkhanov, Dmitry, Zocca, Marco, Dey, Manan, Zhang, Zhihan, Fahmy, Nour, Bhattacharyya, Urvashi, Yu, Wenhao, Singh, Swayam, Luccioni, Sasha, Villegas, Paulo, Kunakov, Maxim, Zhdanov, Fedor, Romero, Manuel, Lee, Tony, Timor, Nadav, Ding, Jennifer, Schlesinger, Claire, Schoelkopf, Hailey, Ebert, Jan, Dao, Tri, Mishra, Mayank, Gu, Alex, Robinson, Jennifer, Anderson, Carolyn Jane, Dolan-Gavitt, Brendan, Contractor, Danish, Reddy, Siva, Fried, Daniel, Bahdanau, Dzmitry, Jernite, Yacine, Ferrandis, Carlos Muñoz, Hughes, Sean, Wolf, Thomas, Guha, Arjun, von Werra, Leandro, de Vries, Harm
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilitie
Externí odkaz:
http://arxiv.org/abs/2305.06161