This Stage used 3 Reward Models

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

The DeepSeek-R1 design provides actions equivalent to other modern large 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 needs a tenth of the computing power of a similar LLM. [2] [3] [4] DeepSeek’s AI designs were established in the middle of United States sanctions on India and China for Nvidia chips, [5] which were planned to limit the capability of these 2 nations to develop sophisticated AI systems. [6] [7]

On 10 January 2025, DeepSeek released its very first free chatbot app, based upon the DeepSeek-R1 design, for iOS and Android; by 27 January, DeepSeek-R1 had gone beyond ChatGPT as the most-downloaded complimentary app on the iOS App Store in the United States, [8] causing Nvidia’s share price to stop by 18%. [9] [10] DeepSeek’s success versus bigger and more established rivals has actually been explained as “overthrowing AI”, [8] constituting “the first shot at what is emerging as a global AI space race”, [11] and ushering in “a new period of AI brinkmanship”. [12]

DeepSeek makes its generative expert system algorithms, designs, and training information open-source, enabling its code to be easily readily available for usage, modification, watching, and creating documents for developing purposes. [13] The business reportedly strongly hires young AI researchers from top Chinese universities, [8] and hires 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 considering that 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 using AI trading algorithms. By 2021, High-Flyer specifically used AI in trading. [15] DeepSeek has made its generative expert system chatbot open source, suggesting its code is freely available for use, adjustment, and watching. This consists of permission to gain access to and utilize the source code, as well as design files, for constructing purposes. [13]

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

In April 2023, High-Flyer started an artificial basic intelligence laboratory dedicated to research developing AI tools separate from High-Flyer’s financial company. [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 firms hesitated in providing funding as it was not likely that it would have the ability to create an exit in a brief amount of time. [15]

After launching DeepSeek-V2 in May 2024, which used strong efficiency for a low price, DeepSeek became known as the driver for China’s AI model rate war. It was quickly dubbed the “Pinduoduo of AI”, and other major tech giants such as ByteDance, Tencent, Baidu, and Alibaba began to cut the rate of their AI designs to contend with the business. Despite the low cost charged by DeepSeek, it paid compared to its competitors that were losing cash. [20]

DeepSeek is concentrated on research and has no detailed strategies for commercialization; [20] this likewise enables its innovation to prevent the most rigid arrangements of China’s AI policies, such as requiring consumer-facing innovation to comply with the federal government’s controls on information. [3]

DeepSeek’s employing choices target technical capabilities instead of work experience, leading to many new hires being either current university graduates or designers whose AI professions are less developed. [18] [3] Likewise, the business hires people with no computer technology background to help its technology comprehend other subjects and understanding locations, including having the ability to produce poetry and perform well on the notoriously challenging Chinese college admissions examinations (Gaokao). [3]

Development and release history

DeepSeek LLM

On 2 November 2023, DeepSeek released its first series of model, DeepSeek-Coder, which is readily available for totally free to both scientists and business 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 usage” for the design itself. [21]

They are of the exact same architecture as DeepSeek LLM detailed below. The series consists of 8 designs, 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 guideline data. This produced the Instruct models.

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 specifications in both Base and Chat forms (no Instruct was launched). It was developed to take on other LLMs available at the time. The paper declared benchmark outcomes greater than many open source LLMs at the time, particularly Llama 2. [26]: section 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 very 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 two Base models was also launched simultaneously, gotten by training Base by supervised finetuning (SFT) followed by direct policy optimization (DPO). [26]

On 9 January 2024, they launched 2 DeepSeek-MoE models (Base, Chat), each of 16B parameters (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 claimed equivalent performance with a 16B MoE as a 7B non-MoE. In architecture, it is a variant of the basic sparsely-gated MoE, with “shared experts” that are constantly queried, and “routed experts” that might not be. They found this to assist with expert balancing. In basic MoE, some experts can become extremely relied on, while other specialists may be hardly ever utilized, losing criteria. Attempting to stabilize the experts so that they are equally utilized then causes experts to duplicate the same capability. They proposed the shared specialists to learn core capacities that are often utilized, and let the routed professionals to learn 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 model.
3. Train an instruction-following design by SFT Base with 776K math issues and their tool-use-integrated step-by-step options. This produced the Instruct model.
Reinforcement learning (RL): The reward design was a procedure reward model (PRM) trained from Base according to the Math-Shepherd technique. [30] This reward model was then used to train Instruct utilizing group relative policy optimization (GRPO) on a dataset of 144K mathematics questions “associated to GSM8K and MATH”. The reward design was continually updated during training to prevent reward hacking. This led to the RL design.

V2

In May 2024, they launched the DeepSeek-V2 series. The series includes 4 models, 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 utilizing YaRN. [32] This led to DeepSeek-V2.
3. SFT with 1.2 M circumstances 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 very first phase was trained to resolve math and coding problems. This phase utilized 1 reward design, trained on compiler feedback (for coding) and ground-truth labels (for math). The second phase was trained to be handy, safe, and follow rules. This stage utilized 3 reward models. The helpfulness and security benefit models were trained on human choice data. The rule-based benefit design was manually set. All trained reward designs were initialized from DeepSeek-V2-Chat (SFT). This resulted in the released version of DeepSeek-V2-Chat.

They selected 2-staged RL, due to the fact that they discovered that RL on reasoning data had “special qualities” various from RL on general information. For instance, RL on thinking could enhance over more training steps. [31]

The two V2-Lite models were smaller sized, and skilled likewise, though DeepSeek-V2-Lite-Chat only went through SFT, not RL. They trained the Lite version to help “further research and development on MLA and DeepSeekMoE”. [31]

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

The Financial Times reported that it was cheaper than its peers with a cost 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 launched 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 variation 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 create 20K code-related and 30K math-related instruction data, then combined with a direction dataset of 300M tokens. This was utilized for SFT.
2. RL with GRPO. The benefit for math problems was calculated by comparing with the ground-truth label. The reward for code issues was created by a reward model trained to forecast whether a program would pass the unit 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 released a base model DeepSeek-V3-Base and a chat design DeepSeek-V3. The model architecture is basically the 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 greater ratio of math and programming than the pretraining dataset of V2.
2. Extend context length two times, from 4K to 32K and then to 128K, using YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 dates on 1.5 M samples of reasoning (math, programs, reasoning) and non-reasoning (creative writing, roleplay, easy question answering) information. Reasoning data was generated by “skilled designs”. Non-reasoning information was produced by DeepSeek-V2.5 and examined by human beings. – The “expert models” were trained by starting with an undefined base model, then SFT on both data, and synthetic information generated by an internal DeepSeek-R1 design. The system prompt asked the R1 to reflect and validate during thinking. Then the expert models were RL utilizing an undefined benefit function.
– Each expert model was trained to create just synthetic reasoning data in one specific domain (math, programming, reasoning).
– Expert models were used, rather of R1 itself, since 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 containing both final benefit and chain-of-thought causing the final reward. The benefit design produced reward signals for both concerns with unbiased but free-form answers, and questions without objective answers (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 reward was calculated for math problems with a last answer (put in a box), and for programming issues by system tests. This produced DeepSeek-V3.

The DeepSeek team performed extensive low-level engineering to attain effectiveness. They used mixed-precision arithmetic. Much of the forward pass was performed in 8-bit drifting point numbers (5E2M: 5-bit exponent and 2-bit mantissa) instead of the basic 32-bit, needing unique GEMM regimens to build up precisely. They utilized a custom-made 12-bit float (E5M6) for only the inputs to the linear layers after the attention modules. Optimizer states were in 16-bit (BF16). They decreased the communication latency by overlapping extensively computation and communication, such as dedicating 20 streaming multiprocessors out of 132 per H800 for only inter-GPU communication. They reduced interaction by rearranging (every 10 minutes) the exact machine each specialist was on in order to avoid specific devices being queried more frequently 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 linked by NVLink, and the clusters are connected by InfiniBand. [37]

Benchmark tests reveal 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 available by means of DeepSeek’s API, as well as by means of a chat user interface after logging in. [42] [43] [note 3] It was trained for rational inference, mathematical thinking, and real-time analytical. DeepSeek declared that it exceeded performance of OpenAI o1 on benchmarks such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal mentioned when it utilized 15 problems from the 2024 edition of AIME, the o1 model reached an option 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, however rather are initialized from other pretrained open-weight designs, consisting of LLaMA and Qwen, then fine-tuned on synthetic data generated by R1. [47]

A conversation between User and Assistant. The user asks a question, and the Assistant fixes it. The assistant initially thinks of the reasoning process in the mind and after that offers the user with the response. The thinking procedure and answer are enclosed within and tags, respectively, i.e., thinking process here address here. User:. Assistant:

DeepSeek-R1-Zero was trained specifically using GRPO RL without SFT. Unlike previous variations, they used no model-based reward. All benefit functions were rule-based, “mainly” of 2 types (other types were not defined): accuracy benefits and format benefits. Accuracy benefit was examining whether a boxed response is proper (for mathematics) or whether a code passes tests (for shows). 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 resolve these problems and further improve thinking: [47]

1. SFT DeepSeek-V3-Base on “thousands” of “cold-start” information all with the standard format of|special_token|| special_token|summary >.
2. Apply the very same RL process as R1-Zero, but likewise with a “language consistency benefit” to motivate it to react monolingually. This produced an internal model not released.
3. Synthesize 600K thinking data from the internal design, with rejection tasting (i.e. if the produced thinking had a wrong final response, then it is gotten rid of). Synthesize 200K non-reasoning data (writing, accurate QA, self-cognition, translation) using DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K artificial data for 2 epochs.
5. GRPO RL with rule-based reward (for reasoning jobs) and model-based benefit (for non-reasoning jobs, helpfulness, and harmlessness). This produced DeepSeek-R1.

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

Assessment and responses

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 exceeded ChatGPT as the highest-rated complimentary app on the iOS App Store in the United States; its chatbot reportedly answers concerns, solves reasoning issues and composes computer system programs on par with other chatbots on the marketplace, according to benchmark tests used by American AI business. [3]

DeepSeek-V3 utilizes significantly less resources compared to its peers; for instance, whereas the world’s leading AI companies train their chatbots with supercomputers utilizing as numerous as 16,000 graphics processing systems (GPUs), if not more, DeepSeek claims to have needed only about 2,000 GPUs, namely 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 huge Meta invested developing its most current AI technology. [3]

DeepSeek’s competitive efficiency at fairly very little expense has actually been recognized as possibly challenging the worldwide 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 model was supposedly “on par with” one of OpenAI’s latest models when utilized for tasks such as mathematics, coding, and natural language thinking; [51] echoing other commentators, American Silicon Valley investor Marc Andreessen likewise 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 property. [53] [54] On 20 January 2025, China’s Premier Li Qiang invited Liang Wenfeng to his seminar with professionals and asked him to provide viewpoints and suggestions on a draft for remarks of the yearly 2024 federal government work report. [55]

DeepSeek’s optimization of minimal resources has actually highlighted potential limits of United States sanctions on China’s AI advancement, that include export constraints on advanced AI chips to China [18] [56] The success of the business’s AI models consequently “stimulated market turmoil” [57] and triggered shares in significant global technology companies to plunge on 27 January 2025: Nvidia’s stock fell by as much as 17-18%, [58] as did the stock of rival 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 global selloff of innovation stocks on Nasdaq, prompted by the release of the R1 design, had actually led to tape losses of about $593 billion in the market capitalizations of AI and computer hardware business; [59] by 28 January 2025, an overall of $1 trillion of value was wiped off American stocks. [50]

Leading figures in the American AI sector had blended responses to DeepSeek’s success and performance. [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 “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, consisting of Scale AI CEO Alexandr Wang, Anthropic cofounder and CEO Dario Amodei, and Elon Musk expressed suspicion of the app’s performance or of the sustainability of its success. [60] [66] [67] Various companies, consisting of Amazon Web Services, Toyota, and Stripe, are looking for to utilize the design in their program. [68]

On 27 January 2025, DeepSeek limited its brand-new user registration to contact number from mainland China, e-mail addresses, or Google account logins, following a “massive” cyberattack disrupted the appropriate performance of its servers. [69] [70]

Some sources have observed that the main application programming user interface (API) variation of R1, which ranges from servers found in China, uses censorship mechanisms for topics that are thought about politically delicate for the government of China. For instance, the design refuses to address concerns about the 1989 Tiananmen Square protests and massacre, persecution of Uyghurs, contrasts between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI may initially generate a response, however then deletes it quickly 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 systems and restrictions can only be eliminated to a restricted level in the open-source version of the R1 design. If the “core socialist worths” defined by the Chinese Internet regulatory authorities are touched upon, or the political status of Taiwan is raised, discussions are ended. [74] When tested by NBC News, DeepSeek’s R1 described Taiwan as “an inalienable part of China’s area,” and specified: “We strongly oppose any form of ‘Taiwan independence’ separatist activities and are committed to achieving the complete reunification of the motherland through serene means.” [75] In January 2025, Western scientists were able to trick DeepSeek into offering specific responses to some of these subjects by requesting in its answer to switch specific letters for similar-looking numbers. [73]

Security and privacy

Some professionals fear that the federal government of China might utilize the AI system for foreign impact operations, spreading out disinformation, monitoring and the development of cyberweapons. [76] [77] [78] DeepSeek’s privacy terms and conditions say “We save the details we collect in protected servers found in the People’s Republic of China … We might collect your text or audio input, prompt, uploaded files, feedback, chat history, or other content that you offer to our design and Services”. Although the data storage and collection policy follows ChatGPT’s personal privacy policy, [79] a Wired post reports this as security issues. [80] In reaction, the Italian information protection authority is looking for additional information on DeepSeek’s collection and usage of individual data, and the United States National Security Council revealed that it had actually started a national 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 an inquiry into DeepSeek’s usage of personal information. [83]

Artificial intelligence industry in China.

Notes

^ a b c The variety of heads does not equivalent the variety of KV heads, due to GQA.
^ Inexplicably, the design 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 might use it only 50 times a day.
References

^ Gibney, Elizabeth (23 January 2025). “China’s low-cost, 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 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 City Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Cosgrove, Emma (27 January 2025). “DeepSeek’s less expensive designs and weaker chips cast doubt on trillions in AI facilities costs”. Business Insider.
^ Mallick, Subhrojit (16 January 2024). “Biden admin’s cap on GPU exports may strike India’s AI ambitions”. 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 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 triggering Nvidia and other stocks to plunge?”. 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 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 got rid of 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 altering 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, 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 main”. huggingface.co. 26 December 2024. Retrieved 28 January 2025.
^ Jiang, Ben (27 December 2024). “Chinese start-up DeepSeek’s new AI model outshines Meta, OpenAI items”. 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, outperforms 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 new AI design seems one of the very best ‘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. 28 January 2025.
^ Franzen, Carl (20 November 2024). “DeepSeek’s very first thinking design R1-Lite-Preview turns heads, beating OpenAI o1 efficiency”. 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 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 by means of Reinforcement Learning, arXiv:2501.12948.
^ “Chinese AI start-up 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 moment”. 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” – 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 postures 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 thrashing, 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 development 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, cleaning $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 global AI selloff, Nvidia losses about $593 billion of worth”. 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 “very excellent”: “We need to take the advancements out of China really, extremely seriously””. Windows Central. Retrieved 28 January 2025.
^ Nazzaro, Miranda (28 January 2025). “OpenAI’s Sam Altman calls DeepSeek model ‘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 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” – by means of NYTimes.com.
^ Sharma, Manoj (6 January 2025). “Musk dismisses, Altman praises: What leaders state on DeepSeek’s disturbance”. Fortune India. Retrieved 28 January 2025.
^ “Elon Musk ‘questions’ DeepSeek’s claims, suggests enormous Nvidia GPU infrastructure”. Financialexpress. 28 January 2025. Retrieved 28 January 2025.
^ Kim, Eugene. “Big AWS consumers, including Stripe and Toyota, are pestering the cloud giant for access to DeepSeek AI designs”. Business Insider.
^ Kerr, Dara (27 January 2025). “DeepSeek hit with ‘large-scale’ cyber-attack after AI chatbot tops app shops”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek temporarily limited new sign-ups, mentioning ‘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 appears 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, till 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, innovation 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 poses formidable cyber, information personal privacy dangers”. Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). “Experts prompt caution 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 big 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 information from DeepSeek on data defense”. Reuters. 28 January 2025. Retrieved 28 January 2025.
^ Shalal, Andrea; Shepardson, David (28 January 2025). “White House evaluates result of China AI app DeepSeek on national security, official states”. Reuters. Retrieved 28 January 2025.