Overview

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Company Description

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 large language models (LLMs). Based in Hangzhou, Zhejiang, it is owned and funded by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, developed the business in 2023 and acts as its CEO.

The DeepSeek-R1 model supplies responses equivalent to other contemporary big language models, such as OpenAI’s GPT-4o and o1. [1] It is trained at a substantially 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 developed in the middle of United States sanctions on India and China for Nvidia chips, [5] which were planned to restrict the capability of these 2 countries to establish sophisticated 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 gone beyond ChatGPT as the most-downloaded free app on the iOS App Store in the United States, [8] triggering Nvidia’s share rate to visit 18%. [9] [10] DeepSeek’s success against larger and more recognized rivals has been referred to as “overthrowing AI”, [8] constituting “the very first chance at what is becoming a global AI area race”, [11] and ushering in “a brand-new era of AI brinkmanship”. [12]

DeepSeek makes its generative expert system algorithms, models, and training information open-source, enabling its code to be freely available for use, modification, viewing, and designing documents for constructing functions. [13] The company apparently strongly hires young AI scientists from top Chinese universities, [8] and works with from outside the computer technology field to diversify its models’ understanding and capabilities. [3]

In February 2016, High-Flyer was co-founded by AI lover Liang Wenfeng, who had been trading because the 2007-2008 monetary crisis while participating in Zhejiang University. [14] By 2019, he developed High-Flyer as a hedge fund concentrated on developing and utilizing AI trading algorithms. By 2021, High-Flyer exclusively utilized AI in trading. [15] DeepSeek has actually made its generative expert system chatbot open source, suggesting its code is easily offered for usage, adjustment, and watching. This consists of consent to gain access to and use the source code, as well as style documents, for building functions. [13]

According to 36Kr, Liang had constructed up a shop of 10,000 Nvidia A100 GPUs, which are utilized to train AI [16], before the United States federal government enforced AI chip constraints on China. [15]

In April 2023, High-Flyer began a synthetic basic intelligence laboratory committed to research establishing AI tools different from High-Flyer’s monetary organization. [17] [18] In May 2023, with High-Flyer as one of the investors, the lab became its own business, DeepSeek. [15] [19] [18] Equity capital companies hesitated in supplying funding as it was not likely that it would be able to generate an exit in a short time period. [15]

After launching DeepSeek-V2 in May 2024, which offered strong efficiency for a low cost, DeepSeek ended up being called the catalyst for China’s AI model cost war. It was rapidly called the “Pinduoduo of AI”, and other significant tech giants such as ByteDance, Tencent, Baidu, and Alibaba began to cut the rate of their AI designs to compete with the company. Despite the low rate charged by DeepSeek, it paid compared to its rivals that were losing money. [20]

DeepSeek is focused on research study and has no detailed strategies for commercialization; [20] this also enables its innovation to prevent the most strict provisions of China’s AI regulations, such as requiring consumer-facing innovation to comply with the federal government’s controls on details. [3]

DeepSeek’s hiring choices target technical capabilities instead of work experience, leading to the majority of new hires being either recent university graduates or developers whose AI professions are less developed. [18] [3] Likewise, the company recruits individuals without any computer technology background to help its technology understand other subjects and knowledge locations, including being able to produce poetry and perform well on the notoriously hard Chinese college admissions examinations (Gaokao). [3]

Development and release history

DeepSeek LLM

On 2 November 2023, DeepSeek launched its first series of design, DeepSeek-Coder, which is available totally free to both researchers and commercial users. The code for the model was made open-source under the MIT license, with an additional license contract (“DeepSeek license”) concerning “open and responsible downstream use” for the design itself. [21]

They are of the 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 models.
3. Supervised finetuning (SFT): 2B tokens of instruction data. This produced the Instruct designs.

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

On 29 November 2023, DeepSeek released the DeepSeek-LLM series of designs, with 7B and 67B criteria in both Base and Chat types (no Instruct was launched). It was established to take on other LLMs offered at the time. The paper declared benchmark results higher than most open source LLMs at the time, especially Llama 2. [26]: area 5 Like DeepSeek Coder, the code for the model was under MIT license, with DeepSeek license for the model itself. [27]

The architecture was basically the same as those of the Llama series. They used 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 versions of the 2 Base designs was also released simultaneously, acquired by training Base by monitored finetuning (SFT) followed by direct policy optimization (DPO). [26]

On 9 January 2024, they launched 2 DeepSeek-MoE designs (Base, Chat), each of 16B specifications (2.7 B activated per token, 4K context length). The training was essentially the same as DeepSeek-LLM 7B, and was trained on a part of its training dataset. They declared comparable efficiency with a 16B MoE as a 7B non-MoE. In architecture, it is a variant of the standard sparsely-gated MoE, with “shared experts” that are always queried, and “routed professionals” that might not be. They found this to assist with expert balancing. In basic MoE, some specialists can become overly relied on, while other experts may be seldom used, squandering criteria. Attempting to balance the experts so that they are equally used then triggers professionals to replicate the very same capacity. They proposed the shared experts to find out core capabilities that are typically utilized, and let the routed specialists to learn the peripheral capabilities that are rarely used. [28]

In April 2024, they launched 3 DeepSeek-Math designs 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 model.
3. Train an instruction-following model by SFT Base with 776K mathematics problems and their tool-use-integrated step-by-step services. This produced the Instruct model.
Reinforcement knowing (RL): The reward design was a process reward design (PRM) trained from Base according to the Math-Shepherd method. [30] This reward model was then used to train Instruct using group relative policy optimization (GRPO) on a dataset of 144K mathematics questions “associated to GSM8K and MATH”. The reward design was continually updated throughout training to prevent benefit hacking. This resulted in the RL model.

V2

In May 2024, they released the DeepSeek-V2 series. The series includes 4 designs, 2 base designs (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (-Chat). The 2 larger designs 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 resulted in DeepSeek-V2.
3. SFT with 1.2 M circumstances for helpfulness and 0.3 M for safety. This resulted in DeepSeek-V2-Chat (SFT) which was not released.
4. RL utilizing GRPO in 2 phases. The very first stage was trained to resolve math and coding issues. This phase used 1 reward design, trained on compiler feedback (for coding) and ground-truth labels (for math). The second phase was trained to be helpful, safe, and follow rules. This stage used 3 benefit models. The helpfulness and security benefit designs were trained on human choice data. The rule-based reward design was manually set. All skilled benefit designs were initialized from DeepSeek-V2-Chat (SFT). This resulted in the launched variation of DeepSeek-V2-Chat.

They chose 2-staged RL, since they found that RL on reasoning data had “distinct qualities” various from RL on basic information. For example, RL on thinking might improve over more training steps. [31]

The 2 V2-Lite models were smaller, and experienced likewise, though DeepSeek-V2-Lite-Chat only underwent SFT, not RL. They trained the Lite variation to help “more research and development on MLA and DeepSeekMoE”. [31]

Architecturally, the V2 models were significantly modified from the DeepSeek LLM series. They altered the standard attention mechanism by a low-rank approximation called multi-head latent attention (MLA), and used the mix of professionals (MoE) variant formerly published in January. [28]

The Financial Times reported that it was cheaper than its peers with a rate of 2 RMB for each 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 designs were initialized from corresponding intermediate checkpoints after pretraining on 4.2 T tokens (not the version at the end of pretraining), then pretrained further for 6T tokens, then context-extended to 128K context length. This produced the Base designs.
DeepSeek-Coder and DeepSeek-Math were utilized to create 20K code-related and 30K math-related instruction data, then integrated with an instruction dataset of 300M tokens. This was used for SFT.
2. RL with GRPO. The reward for mathematics problems was calculated by comparing to the ground-truth label. The benefit for code problems was created by a reward model trained to predict whether a program would pass the system tests.

DeepSeek-V2.5 was released in September and upgraded in December 2024. It was made by integrating DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. [36]

V3

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

1. Pretraining on 14.8 T tokens of a multilingual corpus, mainly English and Chinese. It contained a higher ratio of mathematics and shows than the pretraining dataset of V2.
2. Extend context length twice, from 4K to 32K and then to 128K, utilizing YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 dates on 1.5 M samples of thinking (math, programming, logic) and non-reasoning (innovative writing, roleplay, easy question answering) information. Reasoning data was produced by “professional designs”. Non-reasoning data was produced by DeepSeek-V2.5 and examined by humans. – The “expert designs” were trained by beginning with an unspecified base model, then SFT on both data, and synthetic information produced by an internal DeepSeek-R1 design. The system prompt asked the R1 to reflect and validate throughout thinking. Then the expert designs were RL using an undefined reward function.
– Each expert design was trained to produce just synthetic thinking information in one particular domain (math, shows, reasoning).
– Expert models were utilized, instead of R1 itself, since the output from R1 itself suffered “overthinking, bad formatting, and excessive length”.

4. Model-based benefit models were made by starting with a SFT checkpoint of V3, then finetuning on human choice data including both last benefit and chain-of-thought resulting in the final reward. The benefit design produced benefit signals for both questions with unbiased however free-form answers, and concerns without unbiased answers (such as innovative writing).
5. A SFT checkpoint of V3 was trained by GRPO utilizing both benefit models and rule-based benefit. The rule-based benefit was calculated for math problems with a final response (put in a box), and for programming issues by unit tests. This produced DeepSeek-V3.

The DeepSeek group performed extensive low-level engineering to achieve performance. They utilized mixed-precision math. Much of the forward pass was carried out in 8-bit drifting point numbers (5E2M: 5-bit exponent and 2-bit mantissa) instead of the standard 32-bit, needing special GEMM routines to collect properly. They used a customized 12-bit float (E5M6) for just the inputs to the linear layers after the attention modules. Optimizer states remained in 16-bit (BF16). They decreased the communication latency by overlapping thoroughly calculation and interaction, such as devoting 20 streaming multiprocessors out of 132 per H800 for just inter-GPU communication. They lowered interaction by rearranging (every 10 minutes) the precise device each expert was on in order to prevent specific makers being queried regularly 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 show that DeepSeek-V3 surpassed 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 ended up being accessible via DeepSeek’s API, as well as through a chat user interface after visiting. [42] [43] [note 3] It was trained for logical inference, mathematical thinking, and real-time analytical. DeepSeek declared that it surpassed efficiency of OpenAI o1 on benchmarks such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal stated when it used 15 problems from the 2024 edition of AIME, the o1 model reached an option quicker than DeepSeek-R1-Lite-Preview. [45]

On 20 January 2025, DeepSeek released DeepSeek-R1 and DeepSeek-R1-Zero. [46] Both were initialized from DeepSeek-V3-Base, and share its architecture. The company also released some “DeepSeek-R1-Distill” models, which are not initialized on V3-Base, however rather are initialized from other pretrained open-weight designs, including LLaMA and Qwen, then fine-tuned on artificial information generated by R1. [47]

A discussion between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first believes about the thinking process in the mind and then offers the user with the response. The thinking procedure and answer are confined within and tags, respectively, i.e., reasoning procedure here respond to here. User:. Assistant:

DeepSeek-R1-Zero was trained solely utilizing GRPO RL without SFT. Unlike previous versions, they used no model-based benefit. All reward functions were rule-based, “primarily” of 2 types (other types were not specified): precision benefits and format benefits. Accuracy reward was checking whether a boxed answer is appropriate (for math) or whether a code passes tests (for programs). Format reward was inspecting whether the model puts its thinking trace within … [47]

As R1-Zero has issues with readability and blending languages, R1 was trained to deal with these concerns and further enhance reasoning: [47]

1. SFT DeepSeek-V3-Base on “thousands” of “cold-start” data all with the standard format of|special_token|| special_token|summary >.
2. Apply the very same RL procedure as R1-Zero, but also with a “language consistency reward” to encourage it to react monolingually. This produced an internal design not released.
3. Synthesize 600K reasoning information from the internal model, with rejection tasting (i.e. if the generated thinking had an incorrect last answer, then it is removed). Synthesize 200K non-reasoning information (writing, factual QA, self-cognition, translation) using DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K synthetic data for 2 dates.
5. GRPO RL with rule-based benefit (for reasoning tasks) and model-based reward (for non-reasoning jobs, helpfulness, and harmlessness). This produced DeepSeek-R1.

Distilled designs were trained by SFT on 800K data synthesized from DeepSeek-R1, in a comparable way as step 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 gone beyond ChatGPT as the highest-rated totally free app on the iOS App Store in the United States; its chatbot apparently responds to concerns, solves reasoning problems and writes computer system programs on par with other chatbots on the market, according to benchmark tests utilized by American AI companies. [3]

DeepSeek-V3 utilizes substantially fewer resources compared to its peers; for instance, whereas the world’s leading AI business train their chatbots with supercomputers utilizing as numerous as 16,000 graphics processing units (GPUs), if not more, DeepSeek claims to require only about 2,000 GPUs, particularly the H800 series chip from Nvidia. [37] It was trained in around 55 days at a cost of US$ 5.58 million, [37] which is approximately one tenth of what United States tech huge Meta spent developing its latest AI technology. [3]

DeepSeek’s competitive performance at relatively very little expense has actually been acknowledged as potentially challenging the worldwide 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 minute” for American AI. [49] [50] The efficiency of its R1 design was reportedly “on par with” among OpenAI’s newest designs when utilized for tasks such as mathematics, coding, and natural language thinking; [51] echoing other commentators, American Silicon Valley endeavor capitalist Marc Andreessen similarly explained R1 as “AI‘s Sputnik minute”. [51]

DeepSeek’s creator, Liang Wenfeng has 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 extensively applauded DeepSeek as a national asset. [53] [54] On 20 January 2025, China’s Premier Li Qiang invited Liang Wenfeng to his symposium with specialists and asked him to offer opinions and tips on a draft for comments of the annual 2024 work report. [55]

DeepSeek’s optimization of restricted resources has actually highlighted potential limits of United States sanctions on China’s AI development, which include export constraints on advanced AI chips to China [18] [56] The success of the company’s AI models consequently “triggered market turmoil” [57] and triggered shares in significant international technology 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 companies likewise sank, consisting of Microsoft (down 2.5%), Google’s owner Alphabet (down over 4%), and Dutch chip equipment maker ASML (down over 7%). [51] A worldwide selloff of technology stocks on Nasdaq, prompted by the release of the R1 model, had resulted in tape-record losses of about $593 billion in the market capitalizations of AI and hardware business; [59] by 28 January 2025, a total of $1 trillion of worth was cleaned off American stocks. [50]

Leading figures in the American AI sector had combined reactions 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 impressive”. [61] [62] American President Donald Trump, who announced 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 uncertainty of the app’s efficiency or of the sustainability of its success. [60] [66] [67] Various companies, including Amazon Web Services, Toyota, and Stripe, are seeking to utilize the design in their program. [68]

On 27 January 2025, DeepSeek limited its new user registration to contact number from mainland China, email addresses, or Google account logins, following a “large-scale” cyberattack interrupted the proper functioning of its servers. [69] [70]

Some sources have actually observed that the main application programs interface (API) version of R1, which ranges from servers located in China, uses censorship mechanisms for topics that are considered politically delicate for the federal government of China. For instance, the model declines to respond to concerns about the 1989 Tiananmen Square protests 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 initially produce a response, but then deletes it shortly afterwards and changes it with a message such as: “Sorry, that’s beyond my existing scope. Let’s speak about something else.” [72] The incorporated censorship systems and restrictions can only be gotten rid of to a restricted degree in the open-source variation of the R1 design. If the “core socialist worths” specified by the Chinese Internet regulatory authorities are touched upon, or the political status of Taiwan is raised, conversations are terminated. [74] When evaluated by NBC News, DeepSeek’s R1 explained Taiwan as “an inalienable part of China’s territory,” and mentioned: “We strongly oppose any kind of ‘Taiwan self-reliance’ separatist activities and are dedicated to attaining the complete reunification of the motherland through peaceful methods.” [75] In January 2025, Western researchers had the ability to deceive DeepSeek into providing certain responses to a few of these topics by requesting in its answer to switch specific letters for similar-looking numbers. [73]

Security and privacy

Some professionals fear that the government of China could utilize the AI system for foreign impact operations, spreading out disinformation, surveillance and the development of cyberweapons. [76] [77] [78] DeepSeek’s personal privacy terms and conditions state “We save the information we gather in safe servers located in individuals’s Republic of China … We might gather your text or audio input, prompt, uploaded files, feedback, chat history, or other material that you supply to our design and Services”. Although the data storage and collection policy follows ChatGPT’s personal privacy policy, [79] a Wired article reports this as security concerns. [80] In action, the Italian information defense authority is looking for additional information on DeepSeek’s collection and usage of individual information, and the United States National Security Council announced that it had started a national security review. [81] [82] Taiwan’s government banned making use of DeepSeek at federal government ministries on security grounds and South Korea’s Personal Information Protection Commission opened a questions into DeepSeek’s usage of individual information. [83]

Expert system industry in China.

Notes

^ a b c The number of heads does not equal the variety of KV heads, due to GQA.
^ Inexplicably, the model called DeepSeek-Coder-V2 Chat in the paper was released as DeepSeek-Coder-V2-Instruct in HuggingFace.
^ At that time, the R1-Lite-Preview required picking “Deep Think enabled”, and every user might utilize it just 50 times a day.
References

^ Gibney, Elizabeth (23 January 2025). “China’s cheap, open AI design DeepSeek delights scientists”. 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 ready 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 cheaper designs and weaker chips call into question trillions in AI infrastructure spending”. Business Insider.
^ Mallick, Subhrojit (16 January 2024). “Biden admin’s cap on GPU exports might strike India’s AI aspirations”. The Economic Times. Retrieved 29 January 2025.
^ Saran, Cliff (10 December 2024). “Nvidia investigation signals broadening 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 dethrones ChatGPT on App Store: Here’s what you ought to know”. CNBC.
^ Picchi, Aimee (27 January 2025). “What is DeepSeek, and why is it causing Nvidia and other stocks to plunge?”. CBS News.
^ Zahn, Max (27 January 2025). “Nvidia, Microsoft shares topple 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 leading Chinese AI model conquered 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 initial 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 initial on 22 January 2025. Retrieved 1 January 2025.
^ a b McMorrow, Ryan; Olcott, Eleanor (9 June 2024). “The Chinese quant fund-turned-AI leader”. 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 original 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 new AI model exceeds Meta, OpenAI items”. South China Morning Post. Archived from the original on 27 December 2024. Retrieved 28 December 2024.
^ Sharma, Shubham (26 December 2024). “DeepSeek-V3, ultra-large open-source AI, outperforms 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 model appears to be one of the very best ‘open’ challengers 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 thinking power!”. DeepSeek API Docs. Archived from the initial on 20 November 2024. Retrieved 28 January 2025.
^ Franzen, Carl (20 November 2024). “DeepSeek’s first reasoning model R1-Lite-Preview turns heads, beating OpenAI o1 performance”. VentureBeat. Archived from the initial 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 original 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 through 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 moment’: $1tn rubbed out US stocks after Chinese firm reveals AI chatbot” – via 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 startup that shook the tech world?|CNN Business”. CNN. Retrieved 29 January 2025.
^ “DeepSeek poses a challenge 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 business’s AI design advancement highlights limitations of US sanctions”. Tom’s Hardware. Archived from the original on 28 December 2024. Retrieved 28 December 2024.
^ “DeepSeek updates – Chinese AI chatbot triggers US market turmoil, 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 sparks global AI selloff, Nvidia losses about $593 billion of value”. Reuters.
^ a b Sherry, Ben (28 January 2025). “DeepSeek, Calling It ‘Impressive’ however Staying Skeptical”. Inc. Retrieved 29 January 2025.
^ Okemwa, Kevin (28 January 2025). “Microsoft CEO Satya Nadella promotes DeepSeek’s open-source AI as “extremely impressive”: “We should take the developments out of China extremely, really seriously””. Windows Central. Retrieved 28 January 2025.
^ Nazzaro, Miranda (28 January 2025). “OpenAI’s Sam Altman calls DeepSeek model ‘outstanding'”. 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 bashes China on AI, Trump calls DeepSeek advancement “favorable””. Axios.
^ Karaian, Jason; Rennison, Joe (27 January 2025). “China’s A.I. Advances Spook Big Tech Investors on Wall Street” – through NYTimes.com.
^ Sharma, Manoj (6 January 2025). “Musk dismisses, Altman praises: What leaders say on DeepSeek’s disturbance”. Fortune India. Retrieved 28 January 2025.
^ “Elon Musk ‘questions’ DeepSeek’s claims, recommends enormous Nvidia GPU infrastructure”. 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 designs”. Business Insider.
^ Kerr, Dara (27 January 2025). “DeepSeek struck with ‘large-scale’ cyber-attack after AI chatbot tops app stores”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek temporarily limited brand-new sign-ups, pointing out ‘large-scale harmful attacks'”. Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has actually stimulated a $1 trillion panic – and it doesn’t appreciate complimentary speech”. The Daily Telegraph. ISSN 0307-1235. Retrieved 27 January 2025.
^ a b Steinschaden, Jakob (27 January 2025). “DeepSeek: This is what live censorship looks like in the Chinese AI chatbot”. Trending Topics. Retrieved 27 January 2025.
^ a b Lu, Donna (28 January 2025). “We experimented with DeepSeek. It worked well, up until we asked it about Tiananmen Square and Taiwan”. The Guardian. ISSN 0261-3077. Retrieved 30 January 2025.
^ “The Guardian view on an international AI race: geopolitics, development and the rise of turmoil”. The Guardian. 26 January 2025. ISSN 0261-3077. Retrieved 27 January 2025.
^ Yang, Angela; Cui, Jasmine (27 January 2025). “Chinese AI DeepSeek jolts Silicon Valley, providing the AI race its ‘Sputnik moment'”. NBC News. Retrieved 27 January 2025.
^ Kimery, Anthony (26 January 2025). “China’s DeepSeek AI positions formidable cyber, information personal privacy hazards”. Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). “Experts advise 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 actually 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 seeks info from DeepSeek on information defense”. Reuters. 28 January 2025. Retrieved 28 January 2025.
^ Shalal, Andrea; Shepardson, David (28 January 2025). “White House examines effect of China AI app DeepSeek on nationwide security, authorities says”. Reuters. Retrieved 28 January 2025.