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 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 company in 2023 and works as its CEO.

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

On 10 January 2025, DeepSeek released its first complimentary chatbot app, based upon the DeepSeek-R1 design, for iOS and Android; by 27 January, DeepSeek-R1 had surpassed ChatGPT as the most-downloaded totally free app on the iOS App Store in the United States, [8] causing Nvidia’s share rate to drop by 18%. [9] [10] DeepSeek’s success against larger and more established competitors has been explained as “upending AI”, [8] making up “the first shot at what is becoming a worldwide AI space race”, [11] and introducing “a new era of AI brinkmanship”. [12]

DeepSeek makes its generative artificial intelligence algorithms, models, and training information open-source, permitting its code to be freely available for usage, adjustment, viewing, and developing files for building functions. [13] The business supposedly strongly hires young AI researchers from leading 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 enthusiast Liang Wenfeng, who had actually been trading since the 2007-2008 monetary crisis while going to Zhejiang University. [14] By 2019, he established High-Flyer as a hedge fund focused on developing and utilizing AI trading algorithms. By 2021, High-Flyer solely used AI in trading. [15] DeepSeek has actually made its generative synthetic intelligence chatbot open source, meaning its code is easily available for use, modification, and watching. This includes authorization to gain access to and utilize the source code, as well as design documents, for building functions. [13]

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

In April 2023, High-Flyer began a synthetic general intelligence laboratory devoted to research establishing AI tools separate from High-Flyer’s monetary organization. [17] [18] In May 2023, with High-Flyer as one of the financiers, the lab became its own business, DeepSeek. [15] [19] [18] Venture capital companies were reluctant in supplying financing as it was not likely that it would be able to produce an exit in a short period of time. [15]

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

DeepSeek is focused on research and has no detailed prepare for commercialization; [20] this likewise enables its technology to prevent the most rigid provisions of China’s AI guidelines, such as requiring consumer-facing technology to adhere to the federal government’s controls on information. [3]

DeepSeek’s working with choices target technical abilities rather than work experience, resulting in the majority of brand-new hires being either current university graduates or developers whose AI careers are less established. [18] [3] Likewise, the company recruits individuals with no computer system science background to help its innovation comprehend other subjects and understanding locations, including having the ability to produce poetry and perform well on the notoriously tough Chinese college admissions tests (Gaokao). [3]

Development and release history

DeepSeek LLM

On 2 November 2023, DeepSeek released its very first series of model, DeepSeek-Coder, which is available 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 contract (“DeepSeek license”) regarding “open and accountable downstream use” for the model itself. [21]

They are of the very same architecture as DeepSeek LLM detailed 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 direction data. This produced the Instruct models.

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

On 29 November 2023, DeepSeek launched the DeepSeek-LLM series of designs, with 7B and 67B criteria in both Base and Chat forms (no Instruct was released). It was established to complete with other LLMs available at the time. The paper claimed benchmark results greater than many open source LLMs at the time, specifically 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 essentially 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 gotten by deduplicating the Common Crawl. [26]

The Chat versions of the two Base designs was likewise released simultaneously, acquired by training Base by supervised 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 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 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 professionals” that are always queried, and “routed specialists” that might not be. They discovered this to assist with expert balancing. In basic MoE, some professionals can become overly relied on, while other professionals may be seldom utilized, wasting specifications. Attempting to stabilize the specialists so that they are similarly used then triggers specialists to duplicate the exact same capability. They proposed the shared specialists to find out core capabilities that are often utilized, and let the routed experts to discover the peripheral capacities that are hardly ever utilized. [28]

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

1. Initialize with a formerly 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 mathematics issues and their tool-use-integrated step-by-step services. This produced the Instruct design.
Reinforcement knowing (RL): The benefit model was a process reward model (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 math questions “related to GSM8K and MATH”. The reward design was constantly updated throughout training to avoid reward hacking. This led to the RL model.

V2

In May 2024, they released the DeepSeek-V2 series. The series consists of 4 models, 2 base models (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (-Chat). The 2 bigger 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. length from 4K to 128K using YaRN. [32] This led to 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 launched.
4. RL using GRPO in 2 stages. The first stage was trained to solve mathematics and coding issues. This phase used 1 reward model, trained on compiler feedback (for coding) and ground-truth labels (for math). The second stage was trained to be practical, safe, and follow rules. This stage used 3 reward designs. The helpfulness and security benefit designs were trained on human preference information. The rule-based reward model was by hand set. All qualified benefit models were initialized from DeepSeek-V2-Chat (SFT). This resulted in the launched variation of DeepSeek-V2-Chat.

They went with 2-staged RL, since they found that RL on thinking data had “distinct qualities” various from RL on general data. For example, RL on reasoning could enhance over more training actions. [31]

The two V2-Lite models were smaller sized, and qualified similarly, though DeepSeek-V2-Lite-Chat just underwent SFT, not RL. They trained the Lite variation to help “additional research and development on MLA and DeepSeekMoE”. [31]

Architecturally, the V2 models were substantially customized from the DeepSeek LLM series. They altered the basic attention mechanism by a low-rank approximation called multi-head hidden attention (MLA), and utilized the mix of specialists (MoE) variant previously released in January. [28]

The Financial Times reported that it was cheaper than its peers with a cost of 2 RMB for every single 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 version at the end of pretraining), then pretrained further 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 guideline information, then integrated with a direction dataset of 300M tokens. This was used for SFT.
2. RL with GRPO. The benefit for math issues was calculated by comparing with the ground-truth label. The benefit for code problems was produced by a reward design trained to predict whether a program would pass the system tests.

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

V3

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

1. Pretraining on 14.8 T tokens of a multilingual corpus, mostly English and Chinese. It included a higher ratio of mathematics and shows than the pretraining dataset of V2.
2. Extend context length twice, from 4K to 32K and after that to 128K, utilizing YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 dates on 1.5 M samples of thinking (mathematics, programming, logic) and non-reasoning (creative writing, roleplay, simple concern answering) information. Reasoning data was generated by “professional designs”. Non-reasoning data was generated by DeepSeek-V2.5 and inspected by humans. – The “expert models” were trained by beginning with an undefined base model, then SFT on both information, and synthetic information created by an internal DeepSeek-R1 design. The system timely asked the R1 to reflect and verify during thinking. Then the specialist designs were RL utilizing an undefined reward function.
– Each specialist model was trained to produce just synthetic thinking information in one specific domain (mathematics, programs, reasoning).
– Expert models were utilized, instead of R1 itself, because the output from R1 itself suffered “overthinking, bad format, and excessive length”.

4. Model-based reward designs were made by starting with a SFT checkpoint of V3, then finetuning on human choice data including both last benefit and chain-of-thought leading to the last benefit. The reward design produced benefit signals for both questions with objective but free-form answers, and questions without objective responses (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 computed for math issues with a final answer (put in a box), and for programming issues by system tests. This produced DeepSeek-V3.

The DeepSeek team performed comprehensive 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, requiring unique GEMM routines to accumulate properly. They utilized a custom-made 12-bit float (E5M6) for only the inputs to the linear layers after the attention modules. Optimizer states remained in 16-bit (BF16). They minimized the communication latency by overlapping extensively calculation and communication, such as devoting 20 streaming multiprocessors out of 132 per H800 for just inter-GPU communication. They decreased communication by rearranging (every 10 minutes) the exact device each specialist was on in order to prevent certain machines being queried more frequently than the others, adding auxiliary load-balancing losses to the training loss function, and other load-balancing techniques. [37]

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

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

R1

On 20 November 2024, DeepSeek-R1-Lite-Preview became accessible through DeepSeek’s API, in addition to by means of a chat interface after logging in. [42] [43] [note 3] It was trained for sensible inference, mathematical thinking, and real-time problem-solving. DeepSeek claimed that it went beyond efficiency of OpenAI o1 on criteria such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal mentioned when it used 15 problems from the 2024 edition of AIME, the o1 design reached a solution 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 business likewise launched some “DeepSeek-R1-Distill” designs, which are not initialized on V3-Base, but rather are initialized from other pretrained open-weight designs, including LLaMA and Qwen, then fine-tuned on synthetic data created by R1. [47]

A conversation in between User and Assistant. The user asks a concern, and the Assistant resolves it. The assistant initially considers the reasoning process in the mind and then offers the user with the answer. The reasoning procedure and response are enclosed within and tags, respectively, i.e., reasoning process here respond to here. User:. Assistant:

DeepSeek-R1-Zero was trained specifically using GRPO RL without SFT. Unlike previous versions, they used no model-based reward. All benefit functions were rule-based, “primarily” of two types (other types were not defined): precision rewards and format benefits. Accuracy reward was inspecting whether a boxed answer is right (for math) 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 blending languages, R1 was trained to resolve these issues and further improve reasoning: [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 exact same RL process as R1-Zero, but likewise with a “language consistency benefit” to encourage it to react monolingually. This produced an internal design not launched.
3. Synthesize 600K thinking information from the internal design, with rejection sampling (i.e. if the produced thinking had an incorrect last answer, then it is gotten rid of). 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 epochs.
5. GRPO RL with rule-based benefit (for thinking tasks) and model-based reward (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 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 actually surpassed ChatGPT as the highest-rated free app on the iOS App Store in the United States; its chatbot supposedly addresses concerns, fixes reasoning issues and writes computer programs on par with other chatbots on the marketplace, according to benchmark tests utilized by American AI companies. [3]

DeepSeek-V3 utilizes considerably fewer resources compared to its peers; for instance, whereas the world’s leading AI business train their chatbots with supercomputers using as numerous as 16,000 graphics processing units (GPUs), if not more, DeepSeek claims to have actually needed just about 2,000 GPUs, namely the H800 series chip from Nvidia. [37] It was trained in around 55 days at an expense of US$ 5.58 million, [37] which is approximately one tenth of what United States tech huge Meta invested building its newest AI innovation. [3]

DeepSeek’s competitive performance at fairly minimal cost has been recognized as possibly challenging the worldwide supremacy of American AI models. [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 performance of its R1 model was supposedly “on par with” among OpenAI’s latest designs when utilized for tasks such as mathematics, coding, and natural language reasoning; [51] echoing other analysts, American Silicon Valley investor 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 commonly praised DeepSeek as a nationwide property. [53] [54] On 20 January 2025, China’s Premier Li Qiang welcomed Liang Wenfeng to his seminar with professionals and asked him to provide viewpoints and ideas on a draft for remarks of the yearly 2024 federal government work report. [55]

DeepSeek’s optimization of minimal resources has actually highlighted prospective limits of United States sanctions on China’s AI advancement, that include export restrictions on sophisticated AI chips to China [18] [56] The success of the business’s AI models as a result “stimulated market chaos” [57] and caused shares in significant international technology 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] An international selloff of technology stocks on Nasdaq, prompted by the release of the R1 design, had actually resulted in tape-record 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 rubbed out American stocks. [50]

Leading figures in the American AI sector had blended reactions 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 develop American AI infrastructure-both called DeepSeek “incredibly remarkable”. [61] [62] American President Donald Trump, who announced The Stargate Project, called DeepSeek a wake-up call [63] and a positive advancement. [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 hesitation of the app’s efficiency or of the sustainability of its success. [60] [66] [67] Various companies, consisting of 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 telephone number from mainland China, e-mail addresses, or Google account logins, following a “large-scale” cyberattack disrupted the correct functioning of its servers. [69] [70]

Some sources have actually observed that the official application programs interface (API) variation of R1, which runs from servers located in China, uses censorship mechanisms for topics that are considered politically delicate for the government of China. For example, the model refuses to answer questions 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 produce an answer, but then deletes it shortly afterwards and replaces 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 constraints can just be gotten rid of to a limited level in the open-source variation of the R1 design. If the “core socialist values” defined by the Chinese Internet regulative authorities are touched upon, or the political status of Taiwan is raised, conversations are ended. [74] When checked by NBC News, DeepSeek’s R1 described Taiwan as “an inalienable part of China’s area,” and mentioned: “We strongly oppose any kind of ‘Taiwan independence’ separatist activities and are committed to accomplishing the total reunification of the motherland through tranquil methods.” [75] In January 2025, Western researchers had the ability to deceive DeepSeek into giving certain responses 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 experts fear that the federal government of China might utilize the AI system for foreign influence operations, spreading disinformation, monitoring and the development of cyberweapons. [76] [77] [78] DeepSeek’s personal privacy conditions say “We keep the details we collect in secure servers located in the People’s Republic of China … We may gather your text or audio input, timely, uploaded files, feedback, chat history, or other content that you provide to our model 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 response, the Italian information defense authority is looking for additional details on DeepSeek’s collection and usage of individual data, and the United States National Security Council announced that it had actually started a national security evaluation. [81] [82] Taiwan’s federal government banned the use of DeepSeek at federal government ministries on security grounds and South Korea’s Personal Information Protection Commission opened a questions into DeepSeek’s use of personal info. [83]

Artificial intelligence industry in China.

Notes

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

^ Gibney, Elizabeth (23 January 2025). “China’s inexpensive, 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 exposes an AI world prepared to blow”. The Guardian.
^ a b c d e f g Metz, Cade; Tobin, Meaghan (23 January 2025). “How Chinese A.I. Start-Up DeepSeek Is Taking On Silicon Valley Giants”. The New York City Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Cosgrove, Emma (27 January 2025). “DeepSeek’s less expensive designs and weaker chips call into question trillions in AI facilities costs”. Business Insider.
^ Mallick, Subhrojit (16 January 2024). “Biden admin’s cap on GPU exports may strike India’s AI ambitions”. The Economic Times. Retrieved 29 January 2025.
^ Saran, Cliff (10 December 2024). “Nvidia examination 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 Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Field, Hayden (27 January 2025). “China’s DeepSeek AI dismisses ChatGPT on App Store: Here’s what you need to 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 design 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 changing 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 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, retrieved 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, recovered 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 primary”. 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 outshines Meta, OpenAI products”. 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 new AI design appears to be among the best ‘open’ challengers yet”. TechCrunch. Archived from the original on 2 January 2025. Retrieved 31 December 2024.
^ “Deepseek Log in page”. DeepSeek. Retrieved 30 January 2025.
^ “News|DeepSeek-R1-Lite Release 2024/11/20: DeepSeek-R1-Lite-Preview is now live: letting loose supercharged reasoning power!”. DeepSeek API Docs. Archived from the original on 20 November 2024. Retrieved 28 January 2025.
^ Franzen, Carl (20 November 2024). “DeepSeek’s first thinking design R1-Lite-Preview turns heads, beating OpenAI o1 efficiency”. VentureBeat. Archived from the original 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 through 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 minute”. The Hill. Archived from the initial 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 unveils AI chatbot” – through 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 positions a difficulty 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 design development highlights limitations of US sanctions”. Tom’s Hardware. Archived from the initial on 28 December 2024. Retrieved 28 December 2024.
^ “DeepSeek updates – Chinese AI chatbot triggers 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 triggers worldwide AI selloff, Nvidia losses about $593 billion of worth”. 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 “extremely outstanding”: “We must take the advancements out of China extremely, really seriously””. Windows Central. Retrieved 28 January 2025.
^ Nazzaro, Miranda (28 January 2025). “OpenAI’s Sam Altman calls DeepSeek design ‘impressive'”. The Hill. Retrieved 28 January 2025.
^ Dou, Eva; Gregg, Aaron; Zakrzewski, Cat; Tiku, Nitasha; Najmabadi, Shannon (28 January 2025). “Trump calls China’s DeepSeek AI app a ‘wake-up call’ after tech stocks slide”. The Washington Post. Retrieved 28 January 2025.
^ Habeshian, Sareen (28 January 2025). “Johnson bashes China on AI, Trump calls DeepSeek development “positive””. Axios.
^ Karaian, Jason; Rennison, Joe (27 January 2025). “China’s A.I. Advances Spook Big Tech Investors on Wall Street” – via NYTimes.com.
^ Sharma, Manoj (6 January 2025). “Musk dismisses, Altman applauds: What leaders say on DeepSeek’s disturbance”. Fortune India. Retrieved 28 January 2025.
^ “Elon Musk ‘concerns’ DeepSeek’s claims, recommends enormous Nvidia GPU infrastructure”. Financialexpress. 28 January 2025. Retrieved 28 January 2025.
^ Kim, Eugene. “Big AWS consumers, consisting of Stripe and Toyota, are pestering the cloud giant for access to DeepSeek AI models”. Business Insider.
^ Kerr, Dara (27 January 2025). “DeepSeek hit with ‘massive’ cyber-attack after AI chatbot tops app stores”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek temporarily restricted brand-new sign-ups, citing ‘large-scale harmful attacks'”. Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has actually triggered a $1 trillion panic – and it does not 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 appears like in the Chinese AI chatbot”. Trending Topics. Retrieved 27 January 2025.
^ a b Lu, Donna (28 January 2025). “We attempted out 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 an international AI race: geopolitics, innovation 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 shocks Silicon Valley, giving the AI race its ‘Sputnik minute'”. NBC News. Retrieved 27 January 2025.
^ Kimery, Anthony (26 January 2025). “China’s DeepSeek AI positions formidable cyber, data personal privacy dangers”. Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). “Experts urge care over usage of Chinese AI DeepSeek”. The Guardian. ISSN 0261-3077. Retrieved 28 January 2025.
^ Hornby, Rael (28 January 2025). “DeepSeek’s success has 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 inquires from DeepSeek on information defense”. Reuters. 28 January 2025. Retrieved 28 January 2025.
^ Shalal, Andrea; Shepardson, David (28 January 2025). “White House examines result of China AI app DeepSeek on nationwide security, authorities states”. Reuters. Retrieved 28 January 2025.