AndersonChiaramonte 2025.03.23 10:02 查看 : 2
Lewkowycz, Aitor; Andreassen, Anders; Dohan, David; Dyer, Ethan; Michalewski, Henryk; Ramasesh, Vinay; Slone, Ambrose; Anil, Cem; Schlag, Imanol; Gutman-Solo, Theo; Wu, Yuhuai; Neyshabur, Behnam; Gur-Ari, Guy; Misra, Vedant (30 June 2022). "Solving Quantitative Reasoning Problems with Language Models". Narang, Sharan; Chowdhery, Aakanksha (April 4, 2022). "Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance". Wiggers, Kyle (28 April 2022). "The rising sorts of language fashions and why they matter". Hoffmann, Jordan; Borgeaud, Sebastian; Mensch, Arthur; Sifre, Laurent (12 April 2022). "An empirical analysis of compute-optimum large language model coaching". Hoffmann, Jordan; Borgeaud, Sebastian; Mensch, Arthur; et al. Wu, Shijie; Irsoy, Ozan; Lu, Steven; Dabravolski, Vadim; Dredze, Mark; Gehrmann, Sebastian; Kambadur, Prabhanjan; Rosenberg, David; Mann, Gideon (March 30, 2023). "BloombergGPT: A large Language Model for Finance". Wang, Shuohuan; Sun, Yu; Xiang, Yang; Wu, Zhihua; Ding, Siyu; Gong, Weibao; Feng, Shikun; Shang, Junyuan; Zhao, Yanbin; Pang, Chao; Liu, Jiaxiang; Chen, Xuyi; Lu, Yuxiang; Liu, Weixin; Wang, Xi; Bai, Yangfan; Chen, Qiuliang; Zhao, Li; Li, Shiyong; Sun, Peng; Yu, Dianhai; Ma, Yanjun; Tian, Hao; Wu, Hua; Wu, Tian; Zeng, Wei; Li, Ge; Gao, Wen; Wang, Haifeng (December 23, 2021). "ERNIE 3.Zero Titan: Exploring Larger-scale Knowledge Enhanced Pre-coaching for Language Understanding and Generation".
Smith, Shaden; Patwary, Mostofa; Norick, Brandon; LeGresley, Patrick; Rajbhandari, Samyam; Casper, Jared; Liu, Zhun; Prabhumoye, Shrimai; Zerveas, George; Korthikanti, Vijay; Zhang, Elton; Child, Rewon; Aminabadi, Reza Yazdani; Bernauer, Julie; Song, Xia (2022-02-04). "Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A large-Scale Generative Language Model". Rajbhandari et al. (2020) S. Rajbhandari, J. Rasley, O. Ruwase, and Y. He. Yang, Zhilin; Dai, Zihang; Yang, Yiming; Carbonell, Jaime; Salakhutdinov, Ruslan; Le, Quoc V. (2 January 2020). "XLNet: Generalized Autoregressive Pretraining for Language Understanding". Raffel, Colin; Shazeer, Noam; Roberts, Adam; Lee, Katherine; Narang, Sharan; Matena, Michael; Zhou, Yanqi; Li, Wei; Liu, Peter J. (2020). "Exploring the limits of Transfer Learning with a Unified Text-to-Text Transformer". Ren, Xiaozhe; Zhou, Pingyi; Meng, Xinfan; Huang, Xinjing; Wang, Yadao; Wang, Weichao; Li, Pengfei; Zhang, Xiaoda; Podolskiy, Alexander; Arshinov, Grigory; Bout, Andrey; Piontkovskaya, Irina; Wei, Jiansheng; Jiang, Xin; Su, Teng; Liu, Qun; Yao, Jun (March 19, 2023). "PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing". March 13, 2023. Archived from the unique on January 13, 2021. Retrieved March 13, 2023 - by way of GitHub. Cheng, Heng-Tze; Thoppilan, Romal (January 21, 2022). "LaMDA: Towards Safe, Grounded, and High-Quality Dialog Models for Everything". On January 20, DeepSeek launched another mannequin, known as R1.
With a growth cost of just USD 5.6 million, DeepSeek AI has sparked conversations on AI effectivity, monetary funding, and power consumption. As identified in the analysis, this stylistic resemblance poses questions about DeepSeek's originality and transparency in its AI improvement course of. However, Artificial Analysis, which compares the performance of various AI fashions, has but to independently rank DeepSeek's Janus-Pro-7B amongst its opponents. DeepSeek, a Chinese AI agency, is disrupting the business with its low-value, open source giant language models, difficult US tech giants. Conventional knowledge holds that giant language fashions like ChatGPT and DeepSeek must be skilled on an increasing number of high-high quality, human-created text to enhance; DeepSeek took another approach. The smaller models including 66B are publicly available, whereas the 175B mannequin is on the market on request. Qwen2.5 Max is Alibaba’s most advanced AI mannequin to this point, designed to rival main fashions like GPT-4, Claude 3.5 Sonnet, and DeepSeek V3. Microsoft is fascinated by providing inference to its clients, however much much less enthused about funding $100 billion information centers to practice leading edge models which might be prone to be commoditized lengthy before that $100 billion is depreciated. The payoffs from each model and infrastructure optimization additionally counsel there are important positive factors to be had from exploring different approaches to inference in particular.
A large language mannequin (LLM) is a type of machine studying mannequin designed for pure language processing tasks corresponding to language era. Journal of Machine Learning Research. Therefore, the developments of exterior companies resembling DeepSeek are broadly a part of Apple's continued involvement in AI research. DeepSeek apparently just shattered that notion. DeepSeek launched its DeepSeek-V3 in December, adopted up with the R1 model earlier this month. In addition, on GPQA-Diamond, a PhD-stage analysis testbed, DeepSeek-V3 achieves outstanding outcomes, ranking simply behind Claude 3.5 Sonnet and outperforming all other opponents by a substantial margin. DeepSeek has shaken up the idea that Chinese AI firms are years behind their U.S. Currently, DeepSeek lacks such flexibility, making future improvements desirable. For now, DeepSeek’s rise has known as into query the longer term dominance of established AI giants, shifting the dialog towards the growing competitiveness of Chinese companies and the importance of cost-effectivity. Nvidia, marks the beginning of a broader competitors that could reshape the future of AI and expertise investments.
Copyright © youlimart.com All Rights Reserved.鲁ICP备18045292号-2 鲁公网安备 37021402000770号