This Stage Utilized 3 Reward Models

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

The DeepSeek-R1 design offers actions equivalent to other contemporary large language designs, 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 comparable LLM. [2] [3] [4] DeepSeek’s AI models were developed in the middle of United States sanctions on India and China for Nvidia chips, [5] which were meant to limit the capability of these two countries to develop sophisticated AI systems. [6] [7]

On 10 January 2025, DeepSeek launched its first complimentary chatbot app, based upon the DeepSeek-R1 design, for iOS and Android; by 27 January, DeepSeek-R1 had actually surpassed ChatGPT as the most-downloaded free app on the iOS App Store in the United States, [8] causing Nvidia’s share cost to visit 18%. [9] [10] DeepSeek’s success against bigger and more recognized rivals has been referred to as “upending AI”, [8] constituting “the first chance at what is emerging as an international AI area race”, [11] and introducing “a new era 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 use, modification, viewing, and developing files for developing functions. [13] The company supposedly intensely hires young AI researchers from top Chinese universities, [8] and works with from outside the computer science field to diversify its designs’ understanding and capabilities. [3]

In February 2016, High-Flyer was co-founded by AI enthusiast Liang Wenfeng, who had actually been trading since the 2007-2008 monetary crisis while participating in Zhejiang University. [14] By 2019, he developed High-Flyer as a hedge fund concentrated on establishing and utilizing AI trading algorithms. By 2021, High-Flyer solely used AI in trading. [15] DeepSeek has made its generative synthetic intelligence chatbot open source, indicating its code is freely readily available for use, modification, and viewing. This includes approval to access and utilize the source code, along with design files, for building functions. [13]

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

In April 2023, High-Flyer began an artificial basic intelligence lab committed to research establishing AI tools separate from High-Flyer’s monetary company. [17] [18] In May 2023, with High-Flyer as one of the financiers, the lab became its own company, DeepSeek. [15] [19] [18] Equity capital companies hesitated in supplying financing as it was not likely that it would have the ability to generate an exit in a short duration of time. [15]

After releasing DeepSeek-V2 in May 2024, which provided strong efficiency for a low rate, DeepSeek became understood as the catalyst for China’s AI design 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 price of their AI models to take on the business. Despite the low rate charged by DeepSeek, it paid compared to its rivals that were losing cash. [20]

DeepSeek is concentrated on research study and has no detailed plans for commercialization; [20] this likewise permits its innovation to avoid the most strict provisions of China’s AI guidelines, such as needing consumer-facing innovation to abide by the federal government’s controls on info. [3]

DeepSeek’s working with preferences target technical abilities instead of work experience, resulting in a lot of brand-new hires being either recent university graduates or designers whose AI professions are less developed. [18] [3] Likewise, the company hires people without any computer technology background to assist its technology understand other topics and knowledge locations, consisting of having the ability to create poetry and perform well on the infamously challenging Chinese college admissions exams (Gaokao). [3]

Development and release history

DeepSeek LLM

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

They are of the very same architecture as DeepSeek LLM detailed listed below. The series includes 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 information. 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 models, with 7B and 67B criteria in both Base and Chat types (no Instruct was released). It was developed to take on other LLMs available at the time. The paper declared benchmark outcomes higher than many open source LLMs at the time, particularly 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 gotten by deduplicating the Common Crawl. [26]

The Chat variations of the two Base designs was also released simultaneously, obtained 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 criteria (2.7 B triggered per token, 4K context length). The training was basically the like DeepSeek-LLM 7B, and was trained on a part of its training dataset. They declared similar efficiency with a 16B MoE as a 7B non-MoE. In architecture, it is a variant of the basic sparsely-gated MoE, with “shared specialists” that are always queried, and “routed specialists” that might not be. They discovered this to aid with professional balancing. In standard MoE, some experts can end up being excessively relied on, while other specialists may be rarely utilized, losing criteria. Attempting to stabilize the experts so that they are similarly used then triggers professionals to duplicate the exact same capability. They proposed the shared specialists to discover core capacities that are typically utilized, and let the routed professionals to learn the peripheral capacities that are hardly ever used. [28]

In April 2024, they launched 3 DeepSeek-Math designs specialized for doing mathematics: 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 model by SFT Base with 776K math issues and their tool-use-integrated step-by-step services. This produced the Instruct model.
Reinforcement knowing (RL): The reward model was a procedure reward design (PRM) trained from Base according to the Math-Shepherd technique. [30] This benefit design was then utilized to train Instruct using group relative policy optimization (GRPO) on a dataset of 144K mathematics concerns “related to GSM8K and MATH”. The benefit design was continuously upgraded during training to avoid 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 two 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 using YaRN. [32] This resulted in DeepSeek-V2.
3. SFT with 1.2 M instances for helpfulness and 0.3 M for security. This resulted in DeepSeek-V2-Chat (SFT) which was not launched.
4. RL using GRPO in two phases. The first stage was trained to resolve math and coding problems. This phase used 1 reward design, trained on compiler feedback (for coding) and ground-truth labels (for mathematics). The second phase was trained to be practical, safe, and follow guidelines. This stage used 3 reward models. The helpfulness and security benefit models were trained on human preference information. The rule-based benefit design was manually set. All qualified reward designs were initialized from DeepSeek-V2-Chat (SFT). This resulted in the launched version of DeepSeek-V2-Chat.

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

The two V2-Lite models were smaller sized, and qualified similarly, though DeepSeek-V2-Lite-Chat only went through SFT, not RL. They trained the Lite variation to assist “more research study and advancement on MLA and DeepSeekMoE”. [31]

Architecturally, the V2 designs were significantly modified from the DeepSeek LLM series. They altered the basic attention mechanism by a low-rank approximation called multi-head hidden attention (MLA), and used the mixture of experts (MoE) variant previously published in January. [28]

The Financial Times reported that it was more affordable than its peers with a rate 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 variation at the end of pretraining), then pretrained even more for 6T tokens, then context-extended to 128K context length. This produced the Base designs.
DeepSeek-Coder and DeepSeek-Math were used to produce 20K code-related and 30K math-related direction information, then combined with an instruction dataset of 300M tokens. This was utilized for SFT.
2. RL with GRPO. The benefit for math issues was calculated by comparing with the ground-truth label. The reward for code problems was generated by a reward design trained to forecast whether a program would pass the system tests.

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

V3

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

1. Pretraining on 14.8 T tokens of a multilingual corpus, mainly English and Chinese. It consisted of a higher ratio of mathematics and programs than the pretraining dataset of V2.
2. Extend context length two times, 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 (mathematics, programs, reasoning) and non-reasoning (creative writing, roleplay, basic question answering) information. Reasoning data was produced by “professional designs”. Non-reasoning information was created by DeepSeek-V2.5 and examined by humans. – The “expert designs” were trained by starting with an unspecified base model, then SFT on both data, and artificial information produced by an internal DeepSeek-R1 model. The system prompt asked the R1 to show and validate throughout thinking. Then the specialist designs were RL utilizing an unspecified reward function.
– Each expert design was trained to produce just artificial thinking information in one specific domain (mathematics, shows, reasoning).
– Expert designs were utilized, rather of R1 itself, given that the output from R1 itself suffered “overthinking, bad format, 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 final reward and chain-of-thought causing the last benefit. The benefit model produced reward signals for both concerns with unbiased but free-form answers, and questions without unbiased answers (such as imaginative writing).
5. A SFT checkpoint of V3 was trained by GRPO using both reward designs and rule-based reward. The rule-based benefit was calculated for math problems with a last 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 attain effectiveness. They used mixed-precision arithmetic. Much of the forward pass was carried out in 8-bit floating point numbers (5E2M: 5-bit exponent and 2-bit mantissa) instead of the basic 32-bit, needing special GEMM routines to collect accurately. They utilized a custom 12-bit float (E5M6) for just the inputs to the direct layers after the attention modules. Optimizer states remained in 16-bit (BF16). They decreased the communication latency by overlapping thoroughly calculation and communication, such as dedicating 20 streaming multiprocessors out of 132 per H800 for just inter-GPU communication. They lowered communication by rearranging (every 10 minutes) the specific maker each specialist was on in order to avoid certain devices being queried more often than the others, including auxiliary load-balancing losses to the training loss function, and other load-balancing strategies. [37]

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

Benchmark tests reveal that DeepSeek-V3 exceeded 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 through DeepSeek’s API, as well as by means of a chat interface after visiting. [42] [43] [note 3] It was trained for rational inference, mathematical reasoning, and real-time analytical. DeepSeek claimed that it surpassed performance of OpenAI o1 on criteria such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal specified when it used 15 problems from the 2024 edition of AIME, the o1 design 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 company also launched some “DeepSeek-R1-Distill” models, which are not initialized on V3-Base, but instead are initialized from other pretrained open-weight designs, consisting of LLaMA and Qwen, then fine-tuned on synthetic information produced by R1. [47]

A conversation in between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first considers the thinking process in the mind and then provides the user with the response. The reasoning procedure and response are enclosed within and tags, respectively, i.e., thinking procedure here answer here. User:. Assistant:

DeepSeek-R1-Zero was trained specifically using GRPO RL without SFT. Unlike previous versions, they utilized no model-based benefit. All benefit functions were rule-based, “generally” of two types (other types were not defined): accuracy benefits and format benefits. Accuracy benefit was inspecting whether a boxed answer is correct (for mathematics) or whether a code passes tests (for programs). Format reward was examining whether the design 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 further enhance reasoning: [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 model not released.
3. Synthesize 600K reasoning data from the internal design, with rejection tasting (i.e. if the generated thinking had a wrong last answer, then it is gotten rid of). Synthesize 200K non-reasoning information (writing, factual QA, self-cognition, translation) utilizing DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K synthetic information for 2 epochs.
5. GRPO RL with rule-based benefit (for reasoning jobs) and model-based reward (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 similar way as step 3 above. They were not trained with RL. [47]

Assessment and reactions

DeepSeek launched its AI Assistant, which utilizes the V3 model as a chatbot app for Apple IOS and Android. By 27 January 2025 the app had gone beyond ChatGPT as the highest-rated free app on the iOS App Store in the United States; its chatbot reportedly addresses questions, solves logic problems and composes computer system programs on par with other chatbots on the market, according to benchmark tests utilized by American AI companies. [3]

DeepSeek-V3 uses substantially less resources compared to its peers; for instance, whereas the world’s leading AI companies train their chatbots with supercomputers using as many as 16,000 graphics processing units (GPUs), if not more, DeepSeek claims to have required just 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 roughly one tenth of what United States tech giant Meta spent building its latest AI innovation. [3]

DeepSeek’s competitive efficiency at fairly minimal cost has actually been acknowledged as potentially challenging the global dominance of American AI models. [48] Various publications and news media, such as The Hill and The Guardian, described the release of its chatbot as a “Sputnik minute” for American AI. [49] [50] The performance of its R1 design was reportedly “on par with” among OpenAI’s newest models when utilized for tasks such as mathematics, coding, and natural language thinking; [51] echoing other analysts, American Silicon Valley investor Marc Andreessen also described R1 as “AI‘s Sputnik moment”. [51]

DeepSeek’s founder, 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 professionals and asked him to provide opinions and suggestions on a draft for remarks of the yearly 2024 federal government work report. [55]

DeepSeek’s optimization of restricted resources has highlighted prospective limits of United States sanctions on China’s AI advancement, that include export limitations on sophisticated AI chips to China [18] [56] The success of the company’s AI designs as a result “triggered market turmoil” [57] and triggered shares in significant international innovation business 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 devices maker ASML (down over 7%). [51] A global selloff of innovation stocks on Nasdaq, prompted by the release of the R1 model, had led to tape 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 rubbed out American stocks. [50]

Leading figures in the American AI sector had blended 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 establish American AI infrastructure-both called DeepSeek “incredibly remarkable”. [61] [62] American President Donald Trump, who revealed The Stargate Project, called DeepSeek a wake-up call [63] and a favorable 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, including Amazon Web Services, Toyota, and Stripe, are looking for to utilize the model in their program. [68]

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

Some sources have observed that the main application programs user interface (API) version of R1, which runs from servers located in China, utilizes censorship mechanisms for topics that are thought about politically sensitive for the government of China. For instance, the model refuses to address concerns about the 1989 Tiananmen Square protests and massacre, persecution of Uyghurs, contrasts in between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI may at first create an answer, however then deletes it soon afterwards and replaces it with a message such as: “Sorry, that’s beyond my existing scope. Let’s talk about something else.” [72] The incorporated censorship mechanisms and constraints can only be gotten rid of to a restricted degree in the open-source variation of the R1 model. If the “core socialist worths” defined by the Chinese Internet regulative authorities are touched upon, or the political status of Taiwan is raised, conversations are ended. [74] When tested by NBC News, DeepSeek’s R1 described Taiwan as “an inalienable part of China’s area,” and stated: “We firmly oppose any type of ‘Taiwan independence’ separatist activities and are committed to accomplishing the complete reunification of the motherland through peaceful ways.” [75] In January 2025, Western scientists were able to fool DeepSeek into providing particular answers to some of these subjects by asking for in its response to swap certain letters for similar-looking numbers. [73]

Security and personal privacy

Some specialists fear that the federal government of China might use the AI system for foreign impact operations, spreading out disinformation, surveillance and the advancement of cyberweapons. [76] [77] [78] DeepSeek’s personal privacy terms state “We save the info we collect in protected servers found in the People’s Republic of China … We might gather your text or audio input, prompt, uploaded files, feedback, chat history, or other content that you provide to our design and Services”. Although the information storage and collection policy follows ChatGPT’s personal privacy policy, [79] a Wired post reports this as security concerns. [80] In response, the Italian information protection authority is seeking extra information on DeepSeek’s collection and usage of personal data, and the United States National Security Council announced that it had started a national security review. [81] [82] Taiwan’s government prohibited making use of DeepSeek at government ministries on security premises and South Korea’s Personal Information Protection Commission opened an inquiry into DeepSeek’s usage of individual information. [83]

Artificial intelligence market in China.

Notes

^ a b c The number of heads does not equal the number of KV heads, due to GQA.
^ Inexplicably, the model named DeepSeek-Coder-V2 Chat in the paper was released as DeepSeek-Coder-V2-Instruct in HuggingFace.
^ At that time, the R1-Lite-Preview needed selecting “Deep Think made it possible for”, and every user could use it only 50 times a day.
References

^ Gibney, Elizabeth (23 January 2025). “China’s cheap, open AI design DeepSeek thrills 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 prepared to blow”. The Guardian.
^ a b c d e f g Metz, Cade; Tobin, (23 January 2025). “How Chinese A.I. Start-Up DeepSeek Is Competing With Silicon Valley Giants”. The New York Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Cosgrove, Emma (27 January 2025). “DeepSeek’s more affordable designs and weaker chips bring into question trillions in AI infrastructure spending”. Business Insider.
^ Mallick, Subhrojit (16 January 2024). “Biden admin’s cap on GPU exports may strike India’s AI aspirations”. The Economic Times. Retrieved 29 January 2025.
^ Saran, Cliff (10 December 2024). “Nvidia investigation signals widening of US and China chip war|Computer Weekly”. Computer Weekly. Retrieved 27 January 2025.
^ Sherman, Natalie (9 December 2024). “Nvidia targeted by China in 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 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 should know”. CNBC.
^ Picchi, Aimee (27 January 2025). “What is DeepSeek, and why is it causing Nvidia and other stocks to slump?”. 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 got rid of US sanctions”. MIT Technology Review. Archived from the initial 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 original 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 altering how AI models 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 original 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 main · 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, obtained 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, obtained 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 primary”. 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 design 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, exceeds Llama and Qwen on launch”. VentureBeat. Archived from the original on 27 December 2024. Retrieved 28 December 2024.
^ Wiggers, Kyle (26 December 2024). “DeepSeek’s brand-new AI design appears to be one of the finest ‘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: unleashing 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 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, but 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 original 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 via 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 wiped off US stocks after Chinese company 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 start-up that shook the tech world?|CNN Business”. CNN. Retrieved 29 January 2025.
^ “DeepSeek presents 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 thrashing, Graphika states”. 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 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 sparks 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’ however Staying Skeptical”. Inc. Retrieved 29 January 2025.
^ Okemwa, Kevin (28 January 2025). “Microsoft CEO Satya Nadella touts DeepSeek’s open-source AI as “super excellent”: “We should take the advancements out of China extremely, extremely seriously””. Windows Central. Retrieved 28 January 2025.
^ Nazzaro, Miranda (28 January 2025). “OpenAI’s Sam Altman calls DeepSeek model ‘remarkable'”. 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 “positive””. 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 applauds: What leaders say on DeepSeek’s disturbance”. Fortune India. Retrieved 28 January 2025.
^ “Elon Musk ‘concerns’ DeepSeek’s claims, suggests massive Nvidia GPU infrastructure”. Financialexpress. 28 January 2025. Retrieved 28 January 2025.
^ Kim, Eugene. “Big AWS clients, 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 shops”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek briefly limited new sign-ups, pointing out ‘massive malicious attacks'”. Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has stimulated a $1 trillion panic – and it doesn’t care about 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 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, until 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 rise of mayhem”. 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 presents formidable cyber, data privacy threats”. Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). “Experts advise care over use 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 huge 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 inquires from DeepSeek on information security”. 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 national security, authorities says”. Reuters. Retrieved 28 January 2025.