This Stage used 3 Reward Models

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

The DeepSeek-R1 design provides responses similar to other modern big language models, such as OpenAI’s GPT-4o and o1. [1] It is trained at a significantly lower cost-stated at US$ 6 million compared to $100 million for OpenAI’s GPT-4 in 2023 [2] -and requires a tenth of the computing power of an equivalent 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 ability of these 2 countries to develop advanced AI systems. [6] [7]

On 10 January 2025, DeepSeek launched its very first complimentary chatbot app, based on the DeepSeek-R1 model, for iOS and Android; by 27 January, DeepSeek-R1 had actually exceeded ChatGPT as the most-downloaded complimentary app on the iOS App Store in the United States, [8] causing Nvidia’s share rate to stop by 18%. [9] [10] DeepSeek’s success against bigger and more recognized rivals has been described as “upending AI”, [8] constituting “the first chance at what is becoming a global AI space race”, [11] and introducing “a new era of AI brinkmanship”. [12]

DeepSeek makes its generative expert system algorithms, designs, and training information open-source, permitting its code to be freely available for usage, adjustment, viewing, and developing documents for constructing functions. [13] The company reportedly strongly hires young AI scientists from top Chinese universities, [8] and employs from outside the computer system science field to diversify its models’ knowledge and capabilities. [3]

In February 2016, High-Flyer was co-founded by AI lover Liang Wenfeng, who had actually been trading given that the 2007-2008 monetary crisis while going to Zhejiang University. [14] By 2019, he established High-Flyer as a hedge fund concentrated on developing and using AI trading algorithms. By 2021, High-Flyer solely used AI in trading. [15] DeepSeek has actually made its generative expert system chatbot open source, indicating its code is easily available for usage, modification, and watching. This includes consent to access and utilize the source code, in addition to style documents, for constructing purposes. [13]

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

In April 2023, High-Flyer started a synthetic basic intelligence laboratory devoted to research study establishing AI tools different from High-Flyer’s financial service. [17] [18] In May 2023, with High-Flyer as one of the financiers, the lab became its own business, DeepSeek. [15] [19] [18] Equity capital companies hesitated in offering funding as it was unlikely that it would be able to generate an exit in a brief amount of time. [15]

After launching DeepSeek-V2 in May 2024, which offered strong performance for a low price, DeepSeek ended up being referred to as the catalyst for China’s AI design rate war. It was quickly dubbed the “Pinduoduo of AI”, and other significant tech giants such as ByteDance, Tencent, Baidu, and Alibaba began to cut the cost of their AI designs to take on the company. Despite the low price charged by DeepSeek, it was rewarding compared to its rivals that were losing cash. [20]

DeepSeek is concentrated on research study and has no comprehensive plans for commercialization; [20] this likewise permits its innovation to avoid the most stringent provisions of China’s AI regulations, such as requiring consumer-facing innovation to abide by the government’s controls on information. [3]

DeepSeek’s hiring preferences target technical abilities rather than work experience, resulting in the majority of brand-new hires being either current university graduates or developers whose AI professions are less established. [18] [3] Likewise, the business hires individuals with no computer science background to help its technology understand other topics and understanding areas, consisting of being able to generate poetry and perform well on the notoriously difficult 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 readily available free of charge to both researchers and industrial users. The code for the design was made open-source under the MIT license, with an additional license contract (“DeepSeek license”) regarding “open and responsible downstream use” for the model 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 models.
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 models, with 7B and 67B criteria in both Base and Chat kinds (no Instruct was released). It was developed to take on other LLMs readily available at the time. The paper claimed benchmark results greater than most open source LLMs at the time, specifically 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 essentially the like 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 obtained by deduplicating the Common Crawl. [26]

The Chat variations of the two Base designs was likewise launched 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 models (Base, Chat), each of 16B specifications (2.7 B triggered per token, 4K context length). The training was basically the very same as DeepSeek-LLM 7B, and was trained on a part of its training dataset. They claimed comparable performance with a 16B MoE as a 7B non-MoE. In architecture, it is a version of the standard sparsely-gated MoE, with “shared experts” that are always queried, and “routed professionals” that may not be. They found this to assist with professional balancing. In standard MoE, some specialists can become excessively relied on, while other specialists may be seldom utilized, squandering criteria. Attempting to stabilize the specialists so that they are equally utilized then causes professionals to reproduce the same capacity. They proposed the shared professionals to find out core capacities that are often utilized, and let the routed professionals to discover the peripheral capacities that are hardly ever utilized. [28]

In April 2024, they released 3 DeepSeek-Math designs specialized for doing math: 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 problems and their tool-use-integrated detailed solutions. This produced the Instruct design.
Reinforcement learning (RL): The benefit design was a procedure reward design (PRM) trained from Base according to the Math-Shepherd approach. [30] This reward design was then utilized to train Instruct using group relative policy optimization (GRPO) on a dataset of 144K math questions “associated to GSM8K and MATH”. The reward model was continuously updated throughout training to avoid benefit hacking. This resulted in the RL design.

V2

In May 2024, they released the DeepSeek-V2 series. The series includes 4 designs, 2 base designs (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (-Chat). The 2 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. Extend context length from 4K to 128K using YaRN. [32] This led to DeepSeek-V2.
3. SFT with 1.2 M instances for helpfulness and 0.3 M for safety. This resulted in DeepSeek-V2-Chat (SFT) which was not released.
4. RL utilizing GRPO in two stages. The very first phase was trained to fix mathematics and coding problems. This stage utilized 1 benefit model, trained on compiler feedback (for coding) and ground-truth labels (for mathematics). The second phase was trained to be useful, safe, and follow guidelines. This stage used 3 benefit designs. The helpfulness and safety benefit models were trained on human preference information. The rule-based benefit model was by hand configured. All skilled reward designs were initialized from DeepSeek-V2-Chat (SFT). This resulted in the launched variation of DeepSeek-V2-Chat.

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

The 2 V2-Lite designs were smaller, and skilled similarly, though DeepSeek-V2-Lite-Chat only underwent SFT, not RL. They trained the Lite variation to assist “more research study and advancement on MLA and DeepSeekMoE”. [31]

Architecturally, the V2 models 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 utilized the mixture of professionals (MoE) variant formerly released 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 launched 4 designs 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 direction information, then integrated with a guideline dataset of 300M tokens. This was utilized for SFT.
2. RL with GRPO. The benefit for math problems was computed by comparing to the ground-truth label. The reward for code issues was generated by a benefit design trained to anticipate 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 launched a base design DeepSeek-V3-Base and a chat model DeepSeek-V3. The design architecture is essentially the like V2. They were trained as follows: [37]

1. Pretraining on 14.8 T tokens of a multilingual corpus, mostly English and Chinese. It consisted of a higher ratio of mathematics and programming than the pretraining dataset of V2.
2. Extend context length twice, from 4K to 32K and then to 128K, utilizing YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 epochs on 1.5 M samples of thinking (math, programming, reasoning) and non-reasoning (innovative writing, roleplay, easy concern answering) data. Reasoning information was generated by “expert models”. Non-reasoning data was produced by DeepSeek-V2.5 and inspected by humans. – The “professional models” were trained by starting with an undefined base design, then SFT on both data, and artificial information produced by an internal DeepSeek-R1 model. The system timely asked the R1 to reflect and verify during thinking. Then the expert designs were RL using an unspecified reward function.
– Each specialist design was trained to generate just artificial thinking data in one particular domain (math, programs, reasoning).
– Expert models were used, instead of R1 itself, since the output from R1 itself suffered “overthinking, poor format, and excessive length”.

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

The DeepSeek team carried out substantial low-level engineering to attain effectiveness. They utilized mixed-precision math. Much of the forward pass was carried out in 8-bit drifting point numbers (5E2M: 5-bit exponent and 2-bit mantissa) instead of the basic 32-bit, needing unique GEMM regimens to build up precisely. They utilized a custom 12-bit float (E5M6) for just the inputs to the direct layers after the attention modules. Optimizer states were in 16-bit (BF16). They minimized the communication latency by overlapping thoroughly calculation and communication, such as devoting 20 streaming multiprocessors out of 132 per H800 for only inter-GPU interaction. They reduced interaction by rearranging (every 10 minutes) the specific maker each specialist was on in order to prevent certain makers being queried more frequently than the others, adding auxiliary load-balancing losses to the training loss function, and other load-balancing methods. [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 show that DeepSeek-V3 surpassed Llama 3.1 and Qwen 2.5 whilst matching GPT-4o and Claude 3.5 Sonnet. [18] [39] [40] [41]

R1

On 20 November 2024, DeepSeek-R1-Lite-Preview ended up being accessible by means of DeepSeek’s API, in addition to by means of a chat interface after visiting. [42] [43] [note 3] It was trained for sensible inference, mathematical thinking, and real-time analytical. DeepSeek declared that it exceeded efficiency of OpenAI o1 on benchmarks such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal stated when it utilized 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 launched DeepSeek-R1 and DeepSeek-R1-Zero. [46] Both were initialized from DeepSeek-V3-Base, and share its architecture. The business also released some “DeepSeek-R1-Distill” models, which are not initialized on V3-Base, however rather are initialized from other pretrained open-weight designs, including LLaMA and Qwen, then fine-tuned on synthetic data created by R1. [47]

A discussion between User and Assistant. The user asks a concern, and the Assistant fixes it. The assistant first thinks about the reasoning procedure in the mind and then offers the user with the answer. The reasoning procedure and response are enclosed within and tags, respectively, i.e., thinking procedure here address here. User:. Assistant:

DeepSeek-R1-Zero was trained specifically using GRPO RL without SFT. Unlike previous variations, they used no model-based benefit. All benefit functions were rule-based, “generally” of 2 types (other types were not specified): accuracy benefits and format benefits. Accuracy benefit was checking whether a boxed response is right (for math) or whether a code passes tests (for programs). Format benefit was checking whether the model puts its thinking trace within … [47]

As R1-Zero has issues with readability and mixing languages, R1 was trained to address these concerns and more 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, however likewise with a “language consistency benefit” to motivate it to react monolingually. This produced an internal design not launched.
3. Synthesize 600K thinking information from the internal design, with rejection tasting (i.e. if the created reasoning had an incorrect last answer, then it is eliminated). Synthesize 200K non-reasoning data (writing, accurate QA, self-cognition, translation) using DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K synthetic information for 2 dates.
5. GRPO RL with rule-based reward (for thinking tasks) and model-based benefit (for non-reasoning jobs, helpfulness, and harmlessness). This produced DeepSeek-R1.

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

Assessment and reactions

DeepSeek launched its AI Assistant, which uses the V3 design as a chatbot app for Apple IOS and Android. By 27 January 2025 the app had actually exceeded ChatGPT as the highest-rated free app on the iOS App Store in the United States; its chatbot apparently responds to questions, solves reasoning issues and composes computer system programs on par with other chatbots on the marketplace, according to benchmark tests used by American AI companies. [3]

DeepSeek-V3 utilizes significantly less resources compared to its peers; for example, whereas the world’s leading AI business train their chatbots with supercomputers utilizing as many as 16,000 graphics processing systems (GPUs), if not more, DeepSeek declares to require just 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 approximately one tenth of what United States tech huge Meta invested building its most current AI technology. [3]

DeepSeek’s competitive efficiency at fairly minimal expense has actually been acknowledged as potentially challenging the international supremacy of American AI designs. [48] Various publications and news media, such as The Hill and The Guardian, described the release of its chatbot as a “Sputnik moment” for American AI. [49] [50] The performance of its R1 design was reportedly “on par with” one of OpenAI’s latest models when used for tasks such as mathematics, coding, and natural language reasoning; [51] echoing other analysts, American Silicon Valley investor Marc Andreessen likewise explained 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 praised DeepSeek as a nationwide asset. [53] [54] On 20 January 2025, China’s Premier Li Qiang invited Liang Wenfeng to his symposium with specialists 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 limited resources has highlighted potential limits of United States sanctions on China’s AI development, that include export restrictions on sophisticated AI chips to China [18] [56] The success of the business’s AI designs as a result “triggered market turmoil” [57] and caused shares in major global technology business 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 firms also sank, including 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 technology 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 computer hardware companies; [59] by 28 January 2025, an overall of $1 trillion of worth was wiped off American stocks. [50]

Leading figures in the American AI sector had mixed responses to DeepSeek’s success and efficiency. [60] Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman-whose business are included in the United States government-backed “Stargate Project” to establish American AI infrastructure-both called DeepSeek “very 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 revealed skepticism of the app’s efficiency or of the sustainability of its success. [60] [66] [67] Various business, including Amazon Web Services, Toyota, and Stripe, are seeking 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, email addresses, or Google account logins, following a “massive” cyberattack interfered with the correct performance of its servers. [69] [70]

Some sources have actually observed that the main application programming interface (API) version of R1, which ranges from servers found in China, utilizes censorship systems for topics that are considered politically delicate for the government of China. For example, the model declines 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 erases it quickly later on and changes it with a message such as: “Sorry, that’s beyond my existing scope. Let’s discuss something else.” [72] The incorporated censorship mechanisms and constraints can only be removed to a restricted degree in the open-source variation of the R1 model. If the “core socialist values” defined by the Chinese Internet regulatory authorities are discussed, or the political status of Taiwan is raised, conversations are ended. [74] When tested by NBC News, DeepSeek’s R1 explained Taiwan as “an inalienable part of China’s territory,” and stated: “We strongly oppose any kind of ‘Taiwan independence’ separatist activities and are devoted to attaining the complete reunification of the motherland through peaceful ways.” [75] In January 2025, Western researchers were able to fool DeepSeek into offering specific answers to some of these subjects by requesting in its answer to switch certain letters for similar-looking numbers. [73]

Security and personal privacy

Some specialists fear that the federal government of China might utilize the AI system for foreign influence operations, spreading out disinformation, security and the development of cyberweapons. [76] [77] [78] DeepSeek’s privacy terms state “We save the information we gather in safe and secure servers located in individuals’s Republic of China … We might gather your text or audio input, prompt, uploaded files, feedback, chat history, or other material that you supply to our 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 reaction, the Italian data defense authority is seeking additional information on DeepSeek’s collection and usage of individual data, and the United States National Security Council revealed that it had started a nationwide security evaluation. [81] [82] Taiwan’s government prohibited using DeepSeek at federal government ministries on security premises and South Korea’s Personal Information Protection Commission opened a query into DeepSeek’s use of personal details. [83]

Expert system 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 called DeepSeek-Coder-V2 Chat in the paper was launched as DeepSeek-Coder-V2-Instruct in HuggingFace.
^ At that time, the R1-Lite-Preview required selecting “Deep Think enabled”, and every user could use it just 50 times a day.
References

^ Gibney, Elizabeth (23 January 2025). “China’s low-cost, open AI design DeepSeek thrills researchers”. Nature. doi:10.1038/ d41586-025-00229-6. ISSN 1476-4687. PMID 39849139.
^ a b Vincent, James (28 January 2025). “The DeepSeek panic exposes an AI world ready to blow”. The Guardian.
^ a b c d e f g Metz, Cade; Tobin, Meaghan (23 January 2025). “How Chinese A.I. Start-Up DeepSeek Is Taking On Silicon Valley Giants”. The New York Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Cosgrove, Emma (27 January 2025). “DeepSeek’s less expensive models and weaker chips bring into question trillions in AI facilities 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 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 brand-new chip war probe”. BBC. Retrieved 27 January 2025.
^ a b c Metz, Cade (27 January 2025). “What is DeepSeek? And How Is It Upending A.I.?”. The New York 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 triggering Nvidia and other stocks to slump?”. CBS News.
^ Zahn, Max (27 January 2025). “Nvidia, Microsoft shares topple as China-based AI app DeepSeek hammers tech giants”. ABC News. Retrieved 27 January 2025.
^ Roose, Kevin (28 January 2025). “Why DeepSeek Could Change What Silicon Valley Believe About A.I.” The New York 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 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 altering how AI models 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 leader”. Financial Times. Archived from the initial on 17 July 2024. Retrieved 28 December 2024.
^ a b Schneider, Jordan (27 November 2024). “Deepseek: The Quiet Giant Leading China’s AI Race”. ChinaTalk. Retrieved 28 December 2024.
^ “DeepSeek-Coder/LICENSE-MODEL at main · deepseek-ai/DeepSeek-Coder”. GitHub. Archived from the original on 22 January 2025. Retrieved 24 January 2025.
^ a b c Guo, Daya; Zhu, Qihao; Yang, Dejian; Xie, Zhenda; Dong, Kai; Zhang, Wentao; Chen, Guanting; Bi, Xiao; Wu, Y. (26 January 2024), DeepSeek-Coder: When the Large Language Model Meets Programming – The Rise of Code Intelligence, arXiv:2401.14196.
^ “DeepSeek Coder”. deepseekcoder.github.io. Retrieved 27 January 2025.
^ deepseek-ai/DeepSeek-Coder, DeepSeek, 27 January 2025, 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 new AI model outshines Meta, OpenAI products”. South China Morning Post. Archived from the initial on 27 December 2024. Retrieved 28 December 2024.
^ Sharma, Shubham (26 December 2024). “DeepSeek-V3, ultra-large open-source AI, outshines Llama and Qwen on launch”. VentureBeat. Archived from the initial on 27 December 2024. Retrieved 28 December 2024.
^ Wiggers, Kyle (26 December 2024). “DeepSeek’s brand-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: unleashing supercharged thinking power!”. DeepSeek API Docs. Archived from the initial on 20 November 2024. Retrieved 28 January 2025.
^ Franzen, Carl (20 November 2024). “DeepSeek’s first reasoning 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, however China’s AI Is Catching Up Fast”. The Wall Street Journal. Archived from the initial on 27 December 2024. Retrieved 28 December 2024.
^ “Release DeepSeek-R1 · deepseek-ai/DeepSeek-R1@23807ce”. GitHub. Archived from the 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 through Reinforcement Learning, arXiv:2501.12948.
^ “Chinese AI start-up DeepSeek overtakes ChatGPT on Apple App Store”. Reuters. 27 January 2025. Retrieved 27 January 2025.
^ Wade, David (6 December 2024). “American AI has reached its Sputnik moment”. 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 minute’: $1tn cleaned off 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 start-up that shook the tech world?|CNN Business”. CNN. Retrieved 29 January 2025.
^ “DeepSeek poses 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 business’s AI model breakthrough highlights limits 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 turmoil, 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 sparks worldwide AI selloff, Nvidia losses about $593 billion of value”. Reuters.
^ a b Sherry, Ben (28 January 2025). “DeepSeek, Calling It ‘Impressive’ however Staying Skeptical”. Inc. Retrieved 29 January 2025.
^ Okemwa, Kevin (28 January 2025). “Microsoft CEO Satya Nadella promotes DeepSeek’s open-source AI as “incredibly impressive”: “We should take the advancements out of China very, very seriously””. Windows Central. Retrieved 28 January 2025.
^ Nazzaro, Miranda (28 January 2025). “OpenAI’s Sam Altman calls DeepSeek design ‘outstanding'”. The Hill. Retrieved 28 January 2025.
^ Dou, Eva; Gregg, Aaron; Zakrzewski, Cat; Tiku, Nitasha; Najmabadi, Shannon (28 January 2025). “Trump calls China’s DeepSeek AI app a ‘wake-up call’ after tech stocks slide”. The Washington Post. Retrieved 28 January 2025.
^ Habeshian, Sareen (28 January 2025). “Johnson slams 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 applauds: What leaders state on DeepSeek’s disturbance”. Fortune India. Retrieved 28 January 2025.
^ “Elon Musk ‘questions’ DeepSeek’s claims, suggests huge Nvidia GPU infrastructure”. Financialexpress. 28 January 2025. Retrieved 28 January 2025.
^ Kim, Eugene. “Big AWS consumers, including Stripe and Toyota, are hounding the cloud giant for access to DeepSeek AI models”. Business Insider.
^ Kerr, Dara (27 January 2025). “DeepSeek struck with ‘large-scale’ cyber-attack after AI chatbot tops app stores”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek momentarily limited brand-new sign-ups, pointing out ‘large-scale destructive attacks'”. .
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has triggered 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, until we asked it about Tiananmen Square and Taiwan”. The Guardian. ISSN 0261-3077. Retrieved 30 January 2025.
^ “The Guardian view on an international AI race: geopolitics, development and the rise of mayhem”. The Guardian. 26 January 2025. ISSN 0261-3077. Retrieved 27 January 2025.
^ Yang, Angela; Cui, Jasmine (27 January 2025). “Chinese AI DeepSeek shocks Silicon Valley, offering the AI race its ‘Sputnik minute'”. NBC News. Retrieved 27 January 2025.
^ Kimery, Anthony (26 January 2025). “China’s DeepSeek AI postures formidable cyber, data personal privacy dangers”. Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). “Experts advise care over usage of Chinese AI DeepSeek”. The Guardian. ISSN 0261-3077. Retrieved 28 January 2025.
^ Hornby, Rael (28 January 2025). “DeepSeek’s success has actually painted a 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 defense”. Reuters. 28 January 2025. Retrieved 28 January 2025.
^ Shalal, Andrea; Shepardson, David (28 January 2025). “White House assesses effect of China AI app DeepSeek on national security, authorities says”. Reuters. Retrieved 28 January 2025.