This Stage Utilized 3 Reward Models

DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese expert system business that develops open-source big language models (LLMs). Based in Hangzhou, Zhejiang, it is owned and moneyed by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, established the company in 2023 and functions as its CEO.

The DeepSeek-R1 model supplies reactions equivalent to other contemporary large language models, such as OpenAI’s GPT-4o and o1. [1] It is trained at a significantly lower cost-stated at US$ 6 million compared to $100 million for OpenAI’s GPT-4 in 2023 [2] -and requires a tenth of the computing power of a similar LLM. [2] [3] [4] DeepSeek’s AI designs were established amidst United States sanctions on India and China for Nvidia chips, [5] which were planned to limit the capability of these 2 countries to develop advanced AI systems. [6] [7]

On 10 January 2025, DeepSeek released its very first free chatbot app, based upon the DeepSeek-R1 model, for iOS and Android; by 27 January, DeepSeek-R1 had actually exceeded ChatGPT as the most-downloaded complimentary app on the iOS App Store in the United States, [8] triggering Nvidia’s share rate to drop by 18%. [9] [10] DeepSeek’s success versus larger and more recognized competitors has been referred to as «upending AI», [8] constituting «the first shot at what is becoming a global AI area race», [11] and introducing «a brand-new period of AI brinkmanship». [12]

DeepSeek makes its generative synthetic intelligence algorithms, models, and training details open-source, enabling its code to be freely available for usage, modification, viewing, and developing files for building purposes. [13] The company supposedly intensely recruits young AI scientists from top Chinese universities, [8] and hires from outside the computer technology field to diversify its models’ understanding and abilities. [3]

In February 2016, High-Flyer was co-founded by AI enthusiast Liang Wenfeng, who had actually been trading considering that the 2007-2008 monetary crisis while going to Zhejiang University. [14] By 2019, he established High-Flyer as a hedge fund concentrated on developing and using AI trading algorithms. By 2021, High-Flyer solely utilized AI in trading. [15] DeepSeek has actually made its generative artificial intelligence chatbot open source, indicating its code is easily offered for usage, modification, and viewing. This includes authorization to access and utilize the source code, along with design files, for developing functions. [13]

According to 36Kr, Liang had actually developed a shop of 10,000 Nvidia A100 GPUs, which are used to train AI [16], before the United States federal government imposed AI chip restrictions on China. [15]

In April 2023, High-Flyer began a synthetic general intelligence lab committed to research study establishing AI tools different from High-Flyer’s financial service. [17] [18] In May 2023, with High-Flyer as one of the financiers, the lab became its own company, DeepSeek. [15] [19] [18] Venture capital companies were unwilling in providing funding as it was not likely that it would have the ability to create an exit in a brief duration of time. [15]

After releasing DeepSeek-V2 in May 2024, which provided strong efficiency for a low price, DeepSeek ended up being referred to as the catalyst for China’s AI design cost war. It was quickly called the «Pinduoduo of AI», and other significant tech giants such as ByteDance, Tencent, Baidu, and Alibaba began to cut the cost of their AI designs to compete with the business. Despite the low price charged by DeepSeek, it paid compared to its competitors that were losing money. [20]

DeepSeek is focused on research study and has no comprehensive plans for commercialization; [20] this also enables its innovation to prevent the most strict provisions of China’s AI policies, such as needing consumer-facing technology to adhere to the federal government’s controls on info. [3]

DeepSeek’s working with preferences target technical abilities instead of work experience, leading to many brand-new hires being either current university graduates or designers whose AI careers are less developed. [18] [3] Likewise, the business recruits people without any computer technology background to assist its innovation understand other topics and knowledge locations, including being able to create poetry and carry out well on the infamously challenging Chinese college admissions tests (Gaokao). [3]

Development and release history

DeepSeek LLM

On 2 November 2023, DeepSeek released its first series of model, DeepSeek-Coder, which is available free of charge to both scientists and commercial users. The code for the model was made open-source under the MIT license, with an extra license arrangement («DeepSeek license») regarding «open and accountable downstream use» for the design itself. [21]

They are of the very same architecture as DeepSeek LLM detailed listed below. The series consists of 8 models, 4 pretrained (Base) and 4 instruction-finetuned (Instruct). They all have 16K context lengths. The training was as follows: [22] [23] [24]

1. Pretraining: 1.8 T tokens (87% source code, 10% code-related English (GitHub markdown and Stack Exchange), and 3% code-unrelated Chinese).
2. Long-context pretraining: 200B tokens. This extends the context length from 4K to 16K. This produced the Base designs.
3. Supervised finetuning (SFT): 2B tokens of direction information. This produced the Instruct models.

They were trained on clusters of A100 and H800 Nvidia GPUs, linked by InfiniBand, NVLink, NVSwitch. [22]

On 29 November 2023, DeepSeek released the DeepSeek-LLM series of designs, with 7B and 67B specifications in both Base and Chat kinds (no Instruct was launched). It was developed to take on other LLMs offered at the time. The paper claimed benchmark outcomes greater than most open source LLMs at the time, specifically Llama 2. [26]: area 5 Like DeepSeek Coder, the code for the design was under MIT license, with DeepSeek license for the design itself. [27]

The architecture was basically the exact same as those of the Llama series. They utilized the pre-norm decoder-only Transformer with RMSNorm as the normalization, SwiGLU in the feedforward layers, rotary positional embedding (RoPE), and grouped-query attention (GQA). Both had vocabulary size 102,400 (byte-level BPE) and context length of 4096. They trained on 2 trillion tokens of English and Chinese text acquired by deduplicating the Common Crawl. [26]

The Chat variations of the two Base designs was likewise launched simultaneously, obtained by training Base by supervised finetuning (SFT) followed by direct policy optimization (DPO). [26]

On 9 January 2024, they released 2 DeepSeek-MoE models (Base, Chat), each of 16B specifications (2.7 B activated per token, 4K context length). The training was essentially the very same as DeepSeek-LLM 7B, and was trained on a part of its training dataset. They declared equivalent efficiency with a 16B MoE as a 7B non-MoE. In architecture, it is a version of the basic sparsely-gated MoE, with «shared professionals» that are always queried, and «routed specialists» that might not be. They discovered this to assist with professional balancing. In basic MoE, some professionals can become excessively depended on, while other experts might be rarely used, wasting criteria. Attempting to balance the specialists so that they are similarly used then triggers experts to duplicate the exact same capability. They proposed the shared professionals to learn core capacities that are frequently utilized, and let the routed professionals to discover the peripheral capabilities that are hardly ever used. [28]

In April 2024, they launched 3 DeepSeek-Math models specialized for doing math: Base, Instruct, RL. It was trained as follows: [29]

1. Initialize with a previously pretrained DeepSeek-Coder-Base-v1.5 7B.
2. Further pretrain with 500B tokens (6% DeepSeekMath Corpus, 4% AlgebraicStack, 10% arXiv, 20% GitHub code, 10% Common Crawl). This produced the Base design.
3. Train an instruction-following design by SFT Base with 776K math problems and their tool-use-integrated detailed solutions. This produced the Instruct model.
Reinforcement learning (RL): The reward model was a process reward design (PRM) trained from Base according to the Math-Shepherd approach. [30] This reward model was then used to train Instruct utilizing group relative policy optimization (GRPO) on a dataset of 144K math questions «associated to GSM8K and MATH». The benefit design was constantly upgraded during training to prevent reward hacking. This led to the RL model.

V2

In May 2024, they launched the DeepSeek-V2 series. The series consists of 4 models, 2 base designs (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (-Chat). The 2 larger models were trained as follows: [31]

1. Pretrain on a dataset of 8.1 T tokens, where Chinese tokens are 12% more than English ones.
2. Extend context length from 4K to 128K utilizing YaRN. [32] This led to DeepSeek-V2.
3. SFT with 1.2 M instances for helpfulness and 0.3 M for safety. This resulted in DeepSeek-V2-Chat (SFT) which was not released.
4. RL using GRPO in 2 stages. The first stage was trained to fix math and coding issues. This stage used 1 reward model, trained on compiler feedback (for coding) and ground-truth labels (for math). The second stage was trained to be useful, safe, and follow rules. This phase utilized 3 benefit models. The helpfulness and safety reward designs were trained on human preference data. The rule-based reward design was by hand configured. All experienced benefit models were initialized from DeepSeek-V2-Chat (SFT). This led to the released version of DeepSeek-V2-Chat.

They chose 2-staged RL, since they found that RL on thinking information had «special attributes» various from RL on basic data. For instance, RL on reasoning might improve over more training steps. [31]

The 2 V2-Lite models were smaller, and trained likewise, though DeepSeek-V2-Lite-Chat just went through SFT, not RL. They trained the Lite version to assist «more research and advancement on MLA and DeepSeekMoE». [31]

Architecturally, the V2 designs were significantly customized from the DeepSeek LLM series. They changed the standard attention mechanism by a low-rank approximation called multi-head hidden attention (MLA), and utilized the mixture of experts (MoE) alternative formerly released in January. [28]

The Financial Times reported that it was more affordable than its peers with a cost of 2 RMB for every million output tokens. The University of Waterloo Tiger Lab’s leaderboard ranked DeepSeek-V2 seventh on its LLM ranking. [19]

In June 2024, they released 4 models in the DeepSeek-Coder-V2 series: V2-Base, V2-Lite-Base, V2-Instruct, V2-Lite-Instruct. They were trained as follows: [35] [note 2]

1. The Base models were initialized from corresponding intermediate checkpoints after pretraining on 4.2 T tokens (not the version at the end of pretraining), then pretrained even more for 6T tokens, then context-extended to 128K context length. This produced the Base models.
DeepSeek-Coder and DeepSeek-Math were used to generate 20K code-related and 30K math-related instruction information, then integrated with a direction dataset of 300M tokens. This was utilized for SFT.
2. RL with GRPO. The reward for math problems was computed by comparing with the ground-truth label. The benefit for code problems was created by a reward model trained to anticipate whether a program would pass the system tests.

DeepSeek-V2.5 was launched in September and updated in December 2024. It was made by combining DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. [36]

V3

In December 2024, they launched a base model DeepSeek-V3-Base and a chat design DeepSeek-V3. The model architecture is essentially the very same as V2. They were trained as follows: [37]

1. Pretraining on 14.8 T tokens of a multilingual corpus, mostly English and Chinese. It consisted of a higher ratio of mathematics and programs than the pretraining dataset of V2.
2. Extend context length twice, from 4K to 32K and after that to 128K, utilizing YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 dates on 1.5 M samples of thinking (mathematics, programs, logic) and non-reasoning (creative writing, roleplay, simple concern answering) information. Reasoning data was generated by «professional designs». Non-reasoning data was produced by DeepSeek-V2.5 and checked by human beings. — The «professional models» were trained by starting with an undefined base model, then SFT on both information, and synthetic information created by an internal DeepSeek-R1 model. The system timely asked the R1 to show and confirm throughout thinking. Then the professional designs were RL using an unspecified reward function.
— Each professional model was trained to generate simply artificial reasoning data in one specific domain (mathematics, shows, reasoning).
— Expert designs were used, instead of R1 itself, given that the output from R1 itself suffered «overthinking, poor formatting, and extreme length».

4. Model-based reward models were made by beginning with a SFT checkpoint of V3, then finetuning on human choice information consisting of both final reward and chain-of-thought leading to the final benefit. The reward design produced benefit signals for both concerns with unbiased but free-form answers, and questions without objective responses (such as innovative writing).
5. A SFT checkpoint of V3 was trained by GRPO using both benefit designs and rule-based benefit. The rule-based benefit was computed for math problems with a last response (put in a box), and for shows problems by unit tests. This produced DeepSeek-V3.

The DeepSeek group performed substantial low-level engineering to achieve efficiency. They utilized mixed-precision arithmetic. Much of the forward pass was performed in 8-bit drifting point numbers (5E2M: 5-bit exponent and 2-bit mantissa) rather than the standard 32-bit, requiring unique GEMM regimens to collect accurately. They used a customized 12-bit float (E5M6) for only the inputs to the direct layers after the attention modules. Optimizer states were in 16-bit (BF16). They decreased the interaction latency by overlapping extensively computation and communication, such as committing 20 streaming multiprocessors out of 132 per H800 for just inter-GPU communication. They lowered interaction by rearranging (every 10 minutes) the precise maker each expert was on in order to avoid specific machines being queried more often than the others, including auxiliary load-balancing losses to the training loss function, and other load-balancing methods. [37]

After training, it was deployed on H800 clusters. The H800 cards within a cluster are linked by NVLink, and the clusters are linked by InfiniBand. [37]

Benchmark tests reveal that DeepSeek-V3 outperformed Llama 3.1 and Qwen 2.5 whilst matching GPT-4o and Claude 3.5 Sonnet. [18] [39] [40] [41]

R1

On 20 November 2024, DeepSeek-R1-Lite-Preview became available by means of DeepSeek’s API, in addition to via a chat interface after logging in. [42] [43] [note 3] It was trained for rational reasoning, mathematical reasoning, and real-time problem-solving. DeepSeek claimed that it went beyond performance of OpenAI o1 on criteria such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal mentioned when it utilized 15 issues from the 2024 edition of AIME, the o1 model reached an option much faster than DeepSeek-R1-Lite-Preview. [45]

On 20 January 2025, DeepSeek launched DeepSeek-R1 and DeepSeek-R1-Zero. [46] Both were initialized from DeepSeek-V3-Base, and share its architecture. The business also launched some «DeepSeek-R1-Distill» designs, which are not initialized on V3-Base, but rather are initialized from other pretrained open-weight models, consisting of LLaMA and Qwen, then fine-tuned on synthetic data generated by R1. [47]

A conversation in between User and Assistant. The user asks a concern, and the Assistant fixes it. The assistant initially thinks of the thinking procedure in the mind and then provides the user with the answer. The reasoning process and answer are confined within and tags, respectively, i.e., thinking process here address here. User:. Assistant:

DeepSeek-R1-Zero was trained solely utilizing GRPO RL without SFT. Unlike previous variations, they utilized no model-based benefit. All reward functions were rule-based, «mainly» of two types (other types were not defined): accuracy rewards and format benefits. Accuracy reward was inspecting whether a boxed response is correct (for mathematics) or whether a code passes tests (for shows). Format benefit was checking whether the model puts its thinking trace within … [47]

As R1-Zero has problems with readability and mixing languages, R1 was trained to deal with these issues and more improve thinking: [47]

1. SFT DeepSeek-V3-Base on «thousands» of «cold-start» information all with the basic format of|special_token|| special_token|summary >.
2. Apply the exact same RL procedure as R1-Zero, however likewise with a «language consistency benefit» to motivate it to respond monolingually. This produced an internal design not launched.
3. Synthesize 600K thinking information from the internal design, with rejection sampling (i.e. if the produced reasoning had an incorrect final response, then it is gotten rid of). Synthesize 200K non-reasoning data (writing, accurate QA, self-cognition, translation) utilizing DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K synthetic information for 2 dates.
5. GRPO RL with rule-based benefit (for reasoning jobs) and model-based benefit (for non-reasoning tasks, helpfulness, and harmlessness). This produced DeepSeek-R1.

Distilled models were trained by SFT on 800K information manufactured from DeepSeek-R1, in a comparable method as action 3 above. They were not trained with RL. [47]

Assessment and reactions

DeepSeek released its AI Assistant, which utilizes the V3 design as a chatbot app for Apple IOS and Android. By 27 January 2025 the app had actually surpassed ChatGPT as the highest-rated totally free app on the iOS App Store in the United States; its chatbot reportedly responds to questions, resolves reasoning issues and writes computer programs on par with other chatbots on the market, according to benchmark tests utilized by American AI companies. [3]

DeepSeek-V3 utilizes significantly fewer resources compared to its peers; for example, whereas the world’s leading AI companies train their chatbots with supercomputers utilizing as lots of as 16,000 graphics processing units (GPUs), if not more, DeepSeek claims to require only about 2,000 GPUs, specifically the H800 series chip from Nvidia. [37] It was trained in around 55 days at an expense of US$ 5.58 million, [37] which is roughly one tenth of what United States tech giant Meta invested constructing its most current AI technology. [3]

DeepSeek’s competitive performance at relatively minimal expense has actually been recognized as potentially challenging the international dominance of American AI designs. [48] Various publications and news media, such as The Hill and The Guardian, explained the release of its chatbot as a «Sputnik moment» for American AI. [49] [50] The performance of its R1 model was apparently «on par with» among OpenAI’s latest designs when utilized for tasks such as mathematics, coding, and natural language reasoning; [51] echoing other analysts, American Silicon Valley endeavor capitalist Marc Andreessen similarly explained R1 as «AI’s Sputnik minute». [51]

DeepSeek’s founder, Liang Wenfeng has actually been compared to Open AI CEO Sam Altman, with CNN calling him the Sam Altman of China and an evangelist for AI. [52] Chinese state media widely praised DeepSeek as a national asset. [53] [54] On 20 January 2025, China’s Premier Li Qiang invited Liang Wenfeng to his seminar with specialists and asked him to supply viewpoints and suggestions on a draft for comments of the yearly 2024 government work report. [55]

DeepSeek’s optimization of minimal resources has actually highlighted prospective limits of United States sanctions on China’s AI advancement, which consist of export restrictions on sophisticated AI chips to China [18] [56] The success of the business’s AI designs consequently «triggered market chaos» [57] and caused shares in significant global innovation companies to plunge on 27 January 2025: Nvidia’s stock fell by as much as 17-18%, [58] as did the stock of competing Broadcom. Other tech firms also sank, consisting of Microsoft (down 2.5%), Google’s owner Alphabet (down over 4%), and Dutch chip equipment maker ASML (down over 7%). [51] An international selloff of technology stocks on Nasdaq, prompted by the release of the R1 design, had actually resulted in record losses of about $593 billion in the market capitalizations of AI and computer hardware companies; [59] by 28 January 2025, a total of $1 trillion of value was rubbed out American stocks. [50]

Leading figures in the American AI sector had combined responses to DeepSeek’s success and efficiency. [60] Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman-whose business are associated with the United States government-backed «Stargate Project» to develop American AI infrastructure-both called DeepSeek «super outstanding». [61] [62] American President Donald Trump, who revealed The Stargate Project, called DeepSeek a wake-up call [63] and a positive development. [64] [50] [51] [65] Other leaders in the field, including Scale AI CEO Alexandr Wang, Anthropic cofounder and CEO Dario Amodei, and Elon Musk expressed apprehension of the app’s performance or of the sustainability of its success. [60] [66] [67] Various business, consisting of Amazon Web Services, Toyota, and Stripe, are looking for to use the model in their program. [68]

On 27 January 2025, DeepSeek restricted its new user registration to telephone number from mainland China, email addresses, or Google account logins, following a «large-scale» cyberattack interrupted the proper performance of its servers. [69] [70]

Some sources have actually observed that the main application programming user interface (API) variation of R1, which runs from servers located in China, uses censorship mechanisms for topics that are thought about politically delicate for the government of China. For instance, the model declines to answer concerns about the 1989 Tiananmen Square demonstrations and massacre, persecution of Uyghurs, comparisons in between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI might at first create a response, but then deletes it soon afterwards and changes it with a message such as: «Sorry, that’s beyond my present scope. Let’s talk about something else.» [72] The incorporated censorship mechanisms and restrictions can just be removed to a restricted extent in the open-source variation of the R1 design. If the «core socialist worths» defined by the Chinese Internet regulatory authorities are discussed, or the political status of Taiwan is raised, discussions are terminated. [74] When checked by NBC News, DeepSeek’s R1 described Taiwan as «an inalienable part of China’s territory,» and specified: «We strongly oppose any type of ‘Taiwan self-reliance’ separatist activities and are committed to achieving the complete reunification of the motherland through tranquil methods.» [75] In January 2025, Western researchers were able to deceive DeepSeek into providing specific responses to some of these topics by asking for in its response to swap particular letters for similar-looking numbers. [73]

Security and privacy

Some specialists fear that the federal government of China could use the AI system for foreign influence operations, spreading out disinformation, security and the development of cyberweapons. [76] [77] [78] DeepSeek’s privacy terms and conditions state «We save the info we collect in safe and secure servers located in individuals’s Republic of China … We might collect your text or audio input, timely, uploaded files, feedback, chat history, or other material that you offer to our model and Services». Although the information storage and collection policy follows ChatGPT’s privacy policy, [79] a Wired short article reports this as security issues. [80] In reaction, the Italian information defense authority is looking for extra information on DeepSeek’s collection and usage of individual information, and the United States National Security Council revealed that it had actually begun a nationwide security review. [81] [82] Taiwan’s government banned making use of DeepSeek at government ministries on security grounds and South Korea’s Personal Information Protection Commission opened a questions into DeepSeek’s usage of personal details. [83]

Artificial intelligence market in China.

Notes

^ a b c The number of heads does not equivalent the number of KV heads, due to GQA.
^ Inexplicably, the design named DeepSeek-Coder-V2 Chat in the paper was launched as DeepSeek-Coder-V2-Instruct in HuggingFace.
^ At that time, the R1-Lite-Preview required picking «Deep Think enabled», and every user could use it just 50 times a day.
References

^ Gibney, (23 January 2025). «China’s low-cost, open AI model DeepSeek thrills researchers». Nature. doi:10.1038/ d41586-025-00229-6. ISSN 1476-4687. PMID 39849139.
^ a b Vincent, James (28 January 2025). «The DeepSeek panic reveals an AI world prepared to blow». The Guardian.
^ a b c d e f g Metz, Cade; Tobin, Meaghan (23 January 2025). «How Chinese A.I. Start-Up DeepSeek Is Taking On Silicon Valley Giants». The New York Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Cosgrove, Emma (27 January 2025). «DeepSeek’s less expensive designs and weaker chips call into question trillions in AI facilities costs». Business Insider.
^ Mallick, Subhrojit (16 January 2024). «Biden admin’s cap on GPU exports might hit India’s AI aspirations». The Economic Times. Retrieved 29 January 2025.
^ Saran, Cliff (10 December 2024). «Nvidia examination signals expanding of US and China chip war|Computer Weekly». Computer Weekly. Retrieved 27 January 2025.
^ Sherman, Natalie (9 December 2024). «Nvidia targeted by China in brand-new chip war probe». BBC. Retrieved 27 January 2025.
^ a b c Metz, Cade (27 January 2025). «What is DeepSeek? And How Is It Upending A.I.?». The New York City Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Field, Hayden (27 January 2025). «China’s DeepSeek AI dismisses ChatGPT on App Store: Here’s what you need to understand». CNBC.
^ Picchi, Aimee (27 January 2025). «What is DeepSeek, and why is it triggering Nvidia and other stocks to drop?». CBS News.
^ Zahn, Max (27 January 2025). «Nvidia, Microsoft shares tumble as China-based AI app DeepSeek hammers tech giants». ABC News. Retrieved 27 January 2025.
^ Roose, Kevin (28 January 2025). «Why DeepSeek Could Change What Silicon Valley Believe About A.I.» The New York City Times. ISSN 0362-4331. Retrieved 28 January 2025.
^ a b Romero, Luis E. (28 January 2025). «ChatGPT, DeepSeek, Or Llama? Meta’s LeCun Says Open-Source Is The Key». Forbes.
^ Chen, Caiwei (24 January 2025). «How a top Chinese AI model overcame US sanctions». MIT Technology Review. Archived from the original on 25 January 2025. Retrieved 25 January 2025.
^ a b c d Ottinger, Lily (9 December 2024). «Deepseek: From Hedge Fund to Frontier Model Maker». ChinaTalk. Archived from the initial on 28 December 2024. Retrieved 28 December 2024.
^ Leswing, Kif (23 February 2023). «Meet the $10,000 Nvidia chip powering the race for A.I.» CNBC. Retrieved 30 January 2025.
^ Yu, Xu (17 April 2023).» [Exclusive] Chinese Quant Hedge Fund High-Flyer Won’t Use AGI to Trade Stocks, MD Says». Yicai Global. Archived from the original on 31 December 2023. Retrieved 28 December 2024.
^ a b c d e Jiang, Ben; Perezi, Bien (1 January 2025). «Meet DeepSeek: the Chinese start-up that is changing how AI designs are trained». South China Morning Post. Archived from the original on 22 January 2025. Retrieved 1 January 2025.
^ a b McMorrow, Ryan; Olcott, Eleanor (9 June 2024). «The Chinese quant fund-turned-AI pioneer». Financial Times. Archived from the initial on 17 July 2024. Retrieved 28 December 2024.
^ a b Schneider, Jordan (27 November 2024). «Deepseek: The Quiet Giant Leading China’s AI Race». ChinaTalk. Retrieved 28 December 2024.
^ «DeepSeek-Coder/LICENSE-MODEL at primary · deepseek-ai/DeepSeek-Coder». GitHub. Archived from the initial on 22 January 2025. Retrieved 24 January 2025.
^ a b c Guo, Daya; Zhu, Qihao; Yang, Dejian; Xie, Zhenda; Dong, Kai; Zhang, Wentao; Chen, Guanting; Bi, Xiao; Wu, Y. (26 January 2024), DeepSeek-Coder: When the Large Language Model Meets Programming — The Rise of Code Intelligence, arXiv:2401.14196.
^ «DeepSeek Coder». deepseekcoder.github.io. Retrieved 27 January 2025.
^ deepseek-ai/DeepSeek-Coder, DeepSeek, 27 January 2025, recovered 27 January 2025.
^ «deepseek-ai/deepseek-coder -5.7 bmqa-base · Hugging Face». huggingface.co. Retrieved 27 January 2025.
^ a b c d DeepSeek-AI; Bi, Xiao; Chen, Deli; Chen, Guanting; Chen, Shanhuang; Dai, Damai; Deng, Chengqi; Ding, Honghui; Dong, Kai (5 January 2024), DeepSeek LLM: Scaling Open-Source Language Models with Longtermism, arXiv:2401.02954.
^ deepseek-ai/DeepSeek-LLM, DeepSeek, 27 January 2025, retrieved 27 January 2025.
^ a b Dai, Damai; Deng, Chengqi; Zhao, Chenggang; Xu, R. X.; Gao, Huazuo; Chen, Deli; Li, Jiashi; Zeng, Wangding; Yu, Xingkai (11 January 2024), DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models, arXiv:2401.06066.
^ Shao, Zhihong; Wang, Peiyi; Zhu, Qihao; Xu, Runxin; Song, Junxiao; Bi, Xiao; Zhang, Haowei; Zhang, Mingchuan; Li, Y. K. (27 April 2024), DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models, arXiv:2402.03300.
^ Wang, Peiyi; Li, Lei; Shao, Zhihong; Xu, R. X.; Dai, Damai; Li, Yifei; Chen, Deli; Wu, Y.; Sui, Zhifang (19 February 2024), Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations, arXiv:2312.08935. ^ a b c d DeepSeek-AI; Liu, Aixin; Feng, Bei; Wang, Bin; Wang, Bingxuan; Liu, Bo; Zhao, Chenggang; Dengr, Chengqi; Ruan, Chong (19 June 2024), DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model, arXiv:2405.04434.
^ a b Peng, Bowen; Quesnelle, Jeffrey; Fan, Honglu; Shippole, Enrico (1 November 2023), YaRN: Efficient Context Window Extension of Large Language Models, arXiv:2309.00071.
^ «config.json · deepseek-ai/DeepSeek-V 2-Lite at main». huggingface.co. 15 May 2024. Retrieved 28 January 2025.
^ «config.json · deepseek-ai/DeepSeek-V 2 at main». huggingface.co. 6 May 2024. Retrieved 28 January 2025.
^ DeepSeek-AI; Zhu, Qihao; Guo, Daya; Shao, Zhihong; Yang, Dejian; Wang, Peiyi; Xu, Runxin; Wu, Y.; Li, Yukun (17 June 2024), DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence, arXiv:2406.11931.
^ «deepseek-ai/DeepSeek-V 2.5 · Hugging Face». huggingface.co. 3 January 2025. Retrieved 28 January 2025.
^ a b c d e f g DeepSeek-AI; Liu, Aixin; Feng, Bei; Xue, Bing; Wang, Bingxuan; Wu, Bochao; Lu, Chengda; Zhao, Chenggang; Deng, Chengqi (27 December 2024), DeepSeek-V3 Technical Report, arXiv:2412.19437.
^ «config.json · deepseek-ai/DeepSeek-V 3 at primary». huggingface.co. 26 December 2024. Retrieved 28 January 2025.
^ Jiang, Ben (27 December 2024). «Chinese start-up DeepSeek’s brand-new AI model outshines Meta, OpenAI products». South China Morning Post. Archived from the initial on 27 December 2024. Retrieved 28 December 2024.
^ Sharma, Shubham (26 December 2024). «DeepSeek-V3, ultra-large open-source AI, exceeds Llama and Qwen on launch». VentureBeat. Archived from the initial on 27 December 2024. Retrieved 28 December 2024.
^ Wiggers, Kyle (26 December 2024). «DeepSeek’s new AI design appears to be among the finest ‘open’ oppositions yet». TechCrunch. Archived from the initial on 2 January 2025. Retrieved 31 December 2024.
^ «Deepseek Log in page». DeepSeek. Retrieved 30 January 2025.
^ «News|DeepSeek-R1-Lite Release 2024/11/20: DeepSeek-R1-Lite-Preview is now live: letting loose supercharged reasoning power!». DeepSeek API Docs. Archived from the original on 20 November 2024. Retrieved 28 January 2025.
^ Franzen, Carl (20 November 2024). «DeepSeek’s very first thinking design R1-Lite-Preview turns heads, beating OpenAI o1 performance». VentureBeat. Archived from the original on 22 November 2024. Retrieved 28 December 2024.
^ Huang, Raffaele (24 December 2024). «Don’t Look Now, however China’s AI Is Catching Up Fast». The Wall Street Journal. Archived from the initial on 27 December 2024. Retrieved 28 December 2024.
^ «Release DeepSeek-R1 · deepseek-ai/DeepSeek-R1@23807ce». GitHub. Archived from the initial on 21 January 2025. Retrieved 21 January 2025.
^ a b c d DeepSeek-AI; Guo, Daya; Yang, Dejian; Zhang, Haowei; Song, Junxiao; Zhang, Ruoyu; Xu, Runxin; Zhu, Qihao; Ma, Shirong (22 January 2025), DeepSeek-R1: Incentivizing Reasoning Capability in LLMs by means of Reinforcement Learning, arXiv:2501.12948.
^ «Chinese AI startup DeepSeek surpasses ChatGPT on Apple App Store». Reuters. 27 January 2025. Retrieved 27 January 2025.
^ Wade, David (6 December 2024). «American AI has actually reached its Sputnik minute». The Hill. Archived from the original on 8 December 2024. Retrieved 25 January 2025.
^ a b c Milmo, Dan; Hawkins, Amy; Booth, Robert; Kollewe, Julia (28 January 2025). «‘ Sputnik minute’: $1tn rubbed out US stocks after Chinese company reveals AI chatbot» — by means of The Guardian.
^ a b c d Hoskins, Peter; Rahman-Jones, Imran (27 January 2025). «Nvidia shares sink as Chinese AI app spooks markets». BBC. Retrieved 28 January 2025.
^ Goldman, David (27 January 2025). «What is DeepSeek, the Chinese AI start-up that shook the tech world?|CNN Business». CNN. Retrieved 29 January 2025.
^ «DeepSeek poses an obstacle to Beijing as much as to Silicon Valley». The Economist. 29 January 2025. ISSN 0013-0613. Retrieved 31 January 2025.
^ Paul, Katie; Nellis, Stephen (30 January 2025). «Chinese state-linked accounts hyped DeepSeek AI launch ahead of US stock rout, Graphika says». Reuters. Retrieved 30 January 2025.
^ 澎湃新闻 (22 January 2025). «量化巨头幻方创始人梁文锋参加总理座谈会并发言 ， 他还创办了» AI界拼多多»». finance.sina.com.cn. Retrieved 31 January 2025.
^ Shilov, Anton (27 December 2024). «Chinese AI company’s AI model advancement highlights limits of US sanctions». Tom’s Hardware. Archived from the original on 28 December 2024. Retrieved 28 December 2024.
^ «DeepSeek updates — Chinese AI chatbot stimulates US market chaos, wiping $500bn off Nvidia». BBC News. Retrieved 27 January 2025.
^ Nazareth, Rita (26 January 2025). «Stock Rout Gets Ugly as Nvidia Extends Loss to 17%: Markets Wrap». Bloomberg. Retrieved 27 January 2025.
^ Carew, Sinéad; Cooper, Amanda; Banerjee, Ankur (27 January 2025). «DeepSeek stimulates international AI selloff, Nvidia losses about $593 billion of value». Reuters.
^ a b Sherry, Ben (28 January 2025). «DeepSeek, Calling It ‘Impressive’ but Staying Skeptical». Inc. Retrieved 29 January 2025.
^ Okemwa, Kevin (28 January 2025). «Microsoft CEO Satya Nadella touts DeepSeek’s open-source AI as «incredibly impressive»: «We should take the developments out of China very, really seriously»». Windows Central. Retrieved 28 January 2025.
^ Nazzaro, Miranda (28 January 2025). «OpenAI’s Sam Altman calls DeepSeek design ‘impressive'». The Hill. Retrieved 28 January 2025.
^ Dou, Eva; Gregg, Aaron; Zakrzewski, Cat; Tiku, Nitasha; Najmabadi, Shannon (28 January 2025). «Trump calls China’s DeepSeek AI app a ‘wake-up call’ after tech stocks slide». The Washington Post. Retrieved 28 January 2025.
^ Habeshian, Sareen (28 January 2025). «Johnson slams China on AI, Trump calls DeepSeek development «favorable»». Axios.
^ Karaian, Jason; Rennison, Joe (27 January 2025). «China’s A.I. Advances Spook Big Tech Investors on Wall Street» — via NYTimes.com.
^ Sharma, Manoj (6 January 2025). «Musk dismisses, Altman applauds: What leaders say on DeepSeek’s interruption». Fortune India. Retrieved 28 January 2025.
^ «Elon Musk ‘questions’ DeepSeek’s claims, recommends massive Nvidia GPU facilities». Financialexpress. 28 January 2025. Retrieved 28 January 2025.
^ Kim, Eugene. «Big AWS customers, consisting of Stripe and Toyota, are pestering the cloud giant for access to DeepSeek AI models». Business Insider.
^ Kerr, Dara (27 January 2025). «DeepSeek struck with ‘massive’ cyber-attack after AI chatbot tops app stores». The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. «DeepSeek momentarily limited new sign-ups, citing ‘massive harmful attacks'». Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). «Chinese AI has stimulated a $1 trillion panic — and it does not care about totally free speech». The Daily Telegraph. ISSN 0307-1235. Retrieved 27 January 2025.
^ a b Steinschaden, Jakob (27 January 2025). «DeepSeek: This is what live censorship appears like in the Chinese AI chatbot». Trending Topics. Retrieved 27 January 2025.
^ a b Lu, Donna (28 January 2025). «We tried DeepSeek. It worked well, till we asked it about Tiananmen Square and Taiwan». The Guardian. ISSN 0261-3077. Retrieved 30 January 2025.
^ «The Guardian view on a global AI race: geopolitics, development and the increase of chaos». The Guardian. 26 January 2025. ISSN 0261-3077. Retrieved 27 January 2025.
^ Yang, Angela; Cui, Jasmine (27 January 2025). «Chinese AI DeepSeek shocks Silicon Valley, offering the AI race its ‘Sputnik moment'». NBC News. Retrieved 27 January 2025.
^ Kimery, Anthony (26 January 2025). «China’s DeepSeek AI poses formidable cyber, data privacy hazards». Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). «Experts urge care over usage of Chinese AI DeepSeek». The Guardian. ISSN 0261-3077. Retrieved 28 January 2025.
^ Hornby, Rael (28 January 2025). «DeepSeek’s success has painted a substantial TikTok-shaped target on its back». LaptopMag. Retrieved 28 January 2025.
^ «Privacy policy». Open AI. Retrieved 28 January 2025.
^ Burgess, Matt; Newman, Lily Hay (27 January 2025). «DeepSeek’s Popular AI App Is Explicitly Sending US Data to China». Wired. ISSN 1059-1028. Retrieved 28 January 2025.
^ «Italy regulator looks for information from DeepSeek on information defense». Reuters. 28 January 2025. Retrieved 28 January 2025.
^ Shalal, Andrea; Shepardson, David (28 January 2025). «White House examines impact of China AI app DeepSeek on nationwide security, official states». Reuters. Retrieved 28 January 2025.