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Company Description

This Stage Utilized 3 Reward Models

DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese synthetic intelligence 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, developed the company in 2023 and functions as its CEO.

The DeepSeek-R1 model supplies responses similar to other modern big 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 needs a tenth of the computing power of a similar LLM. [2] [3] [4] DeepSeek’s AI models were established amid United States sanctions on India and China for Nvidia chips, [5] which were intended to limit the capability of these two nations to establish innovative AI systems. [6] [7]

On 10 January 2025, DeepSeek released its very first totally free chatbot app, based on the DeepSeek-R1 design, for iOS and Android; by 27 January, DeepSeek-R1 had surpassed ChatGPT as the most-downloaded free app on the iOS App Store in the United States, [8] triggering Nvidia’s share rate to visit 18%. [9] [10] DeepSeek’s success versus bigger and more recognized rivals has been referred to as “overthrowing AI”, [8] constituting “the first shot at what is emerging as an international AI area race”, [11] and ushering in “a new era of AI brinkmanship”. [12]

DeepSeek makes its generative artificial intelligence algorithms, models, and training information open-source, enabling its code to be freely available for use, modification, watching, and developing files for building purposes. [13] The company apparently vigorously recruits young AI researchers from leading 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 because the 2007-2008 monetary crisis while going to Zhejiang University. [14] By 2019, he established High-Flyer as a hedge fund focused on establishing and using AI trading algorithms. By 2021, High-Flyer exclusively used AI in trading. [15] DeepSeek has actually made its generative synthetic intelligence chatbot open source, implying its code is easily available for usage, modification, and watching. This includes consent to access and utilize the source code, in addition to style files, for building purposes. [13]

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

In April 2023, High-Flyer began an artificial general intelligence laboratory committed to research study establishing AI tools separate from High-Flyer’s monetary company. [17] [18] In May 2023, with High-Flyer as one of the investors, the lab became its own business, DeepSeek. [15] [19] [18] Venture capital companies hesitated in offering funding as it was not likely that it would be able to produce an exit in a short amount of time. [15]

After releasing DeepSeek-V2 in May 2024, which provided strong performance for a low rate, DeepSeek ended up being understood as the driver for China’s AI model cost 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 take on the business. Despite the low cost charged by DeepSeek, it paid compared to its rivals that were losing money. [20]

DeepSeek is concentrated on research and has no in-depth plans for commercialization; [20] this also enables its innovation to avoid the most strict provisions of China’s AI regulations, such as needing consumer-facing technology to adhere to the federal government’s controls on info. [3]

DeepSeek’s employing preferences target technical abilities instead of work experience, leading to many new hires being either recent university graduates or designers whose AI professions are less established. [18] [3] Likewise, the company recruits individuals without any computer science background to assist its innovation comprehend other topics and knowledge areas, consisting of having the ability to generate poetry and perform well on the notoriously challenging Chinese college admissions exams (Gaokao). [3]

Development and release history

DeepSeek LLM

On 2 November 2023, DeepSeek released its very first series of model, DeepSeek-Coder, which is readily available for complimentary to both scientists and commercial users. The code for the design was made open-source under the MIT license, with an extra license arrangement (“DeepSeek license”) concerning “open and accountable downstream usage” for the design itself. [21]

They are of the exact same architecture as DeepSeek LLM detailed listed 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 information. This produced the Instruct designs.

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 specifications in both Base and Chat types (no Instruct was launched). It was developed to take on other LLMs offered at the time. The paper declared benchmark outcomes greater than most open source LLMs at the time, particularly Llama 2. [26]: section 5 Like DeepSeek Coder, the code for the design was under MIT license, with DeepSeek license for the model itself. [27]

The architecture was essentially the like those of the Llama series. They utilized 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 designs (Base, Chat), each of 16B specifications (2.7 B triggered per token, 4K context length). The training was basically the same as DeepSeek-LLM 7B, and was trained on a part of its training dataset. They declared comparable efficiency with a 16B MoE as a 7B non-MoE. In architecture, it is a version of the basic sparsely-gated MoE, with “shared experts” that are always queried, and “routed professionals” that may not be. They discovered this to help with skilled balancing. In standard MoE, some specialists can become excessively counted on, while other professionals might be rarely used, squandering specifications. Attempting to stabilize the professionals so that they are equally used then triggers specialists to duplicate the exact same capability. They proposed the shared experts to find out core capacities that are frequently used, and let the routed professionals to discover the peripheral capacities that are rarely 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 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 mathematics problems and their tool-use-integrated detailed services. This produced the Instruct model.
Reinforcement knowing (RL): The benefit design was a process reward model (PRM) trained from Base according to the Math-Shepherd method. [30] This benefit model was then used to train Instruct utilizing group relative policy optimization (GRPO) on a dataset of 144K mathematics concerns “associated to GSM8K and MATH”. The benefit design was constantly upgraded during training to prevent benefit 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 2 bigger 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 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 solve mathematics and coding issues. This phase used 1 benefit design, trained on compiler feedback (for coding) and ground-truth labels (for mathematics). The second stage was trained to be valuable, safe, and follow guidelines. This phase used 3 reward designs. The helpfulness and security reward designs were trained on human choice information. The rule-based reward model was by hand programmed. All trained benefit models were initialized from DeepSeek-V2-Chat (SFT). This resulted in the launched variation of DeepSeek-V2-Chat.

They chose 2-staged RL, because they discovered that RL on thinking data had “distinct attributes” various from RL on basic information. For example, RL on thinking might enhance over more training actions. [31]

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

Architecturally, the V2 models were substantially modified from the DeepSeek LLM series. They changed the basic attention system by a low-rank approximation called multi-head latent attention (MLA), and used the mixture of specialists (MoE) variant previously released in January. [28]

The Financial Times reported that it was more affordable than its peers with a price 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 models were initialized from corresponding intermediate checkpoints after pretraining on 4.2 T tokens (not the version at the end of pretraining), then pretrained even more for 6T tokens, then context-extended to 128K context length. This produced the Base designs.
DeepSeek-Coder and DeepSeek-Math were utilized to generate 20K code-related and 30K math-related guideline information, then integrated with an instruction dataset of 300M tokens. This was used for SFT.
2. RL with GRPO. The benefit for math issues was computed by comparing to the ground-truth label. The benefit for code issues was produced by a benefit model trained to anticipate whether a program would pass the unit tests.

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

V3

In December 2024, they launched a base model DeepSeek-V3-Base and a chat design DeepSeek-V3. The design architecture is basically 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 math and programs 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 reasoning (mathematics, programs, logic) and non-reasoning (innovative writing, roleplay, basic question answering) data. Reasoning information was produced by “professional designs”. Non-reasoning data was produced by DeepSeek-V2.5 and checked by human beings. – The “professional models” were trained by beginning with an undefined base design, then SFT on both data, and synthetic data generated by an internal DeepSeek-R1 model. The system timely asked the R1 to reflect and validate during thinking. Then the specialist designs were RL using an undefined benefit function.
– Each specialist model was trained to create just artificial reasoning data in one specific domain (math, programs, logic).
– Expert models were used, instead of R1 itself, because the output from R1 itself suffered “overthinking, poor formatting, and extreme length”.

4. Model-based benefit designs were made by beginning with a SFT checkpoint of V3, then finetuning on human choice data including both final reward and chain-of-thought leading to the final reward. The benefit model produced benefit signals for both questions with unbiased but free-form answers, and concerns without objective responses (such as innovative writing).
5. A SFT checkpoint of V3 was trained by GRPO using both reward designs and rule-based benefit. The rule-based reward was computed for mathematics issues with a final response (put in a box), and for shows problems by unit tests. This produced DeepSeek-V3.

The DeepSeek team carried out comprehensive low-level engineering to attain effectiveness. They utilized mixed-precision math. Much of the forward pass was performed in 8-bit drifting point numbers (5E2M: 5-bit exponent and 2-bit mantissa) rather than the basic 32-bit, needing unique GEMM regimens to build up accurately. 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 thoroughly calculation and interaction, such as devoting 20 streaming multiprocessors out of 132 per H800 for just inter-GPU communication. They reduced interaction by rearranging (every 10 minutes) the precise machine each expert was on in order to avoid certain machines being queried regularly than the others, including 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 linked by NVLink, and the clusters are connected by InfiniBand. [37]

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

R1

On 20 November 2024, DeepSeek-R1-Lite-Preview ended up being accessible through DeepSeek’s API, in addition to through a chat user interface after logging in. [42] [43] [note 3] It was trained for sensible reasoning, mathematical reasoning, and real-time problem-solving. DeepSeek claimed that it exceeded efficiency of OpenAI o1 on standards 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 model reached a service 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 company also released some “DeepSeek-R1-Distill” models, which are not initialized on V3-Base, but instead are initialized from other pretrained open-weight models, consisting of LLaMA and Qwen, then fine-tuned on artificial data generated by R1. [47]

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

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

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

1. SFT DeepSeek-V3-Base on “thousands” of “cold-start” data all with the basic format of|special_token|| special_token|summary >.
2. Apply the exact same RL process as R1-Zero, however also with a “language consistency benefit” to encourage it to respond monolingually. This produced an internal model not launched.
3. Synthesize 600K reasoning data from the internal design, with rejection sampling (i.e. if the created thinking had a wrong last response, then it is eliminated). Synthesize 200K non-reasoning information (writing, factual QA, self-cognition, translation) utilizing DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K artificial 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 way 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 exceeded ChatGPT as the highest-rated free app on the iOS App Store in the United States; its chatbot apparently responds to concerns, solves reasoning issues and writes 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 fewer resources compared to its peers; for example, whereas the world’s leading AI companies train their chatbots with supercomputers using as numerous as 16,000 graphics processing systems (GPUs), if not more, DeepSeek declares to have required only about 2,000 GPUs, specifically 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 spent building its newest AI technology. [3]

DeepSeek’s competitive efficiency at relatively very little expense has actually been recognized as potentially challenging the global dominance 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 minute” for American AI. [49] [50] The efficiency of its R1 model was reportedly “on par with” one of OpenAI’s most current models when used for tasks such as mathematics, coding, and natural language reasoning; [51] echoing other analysts, American Silicon Valley venture capitalist Marc Andreessen similarly explained R1 as “AI’s Sputnik minute”. [51]

DeepSeek’s creator, Liang Wenfeng has actually 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 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 tips on a draft for comments of the annual 2024 federal government work report. [55]

DeepSeek’s optimization of limited resources has highlighted prospective limits of United States sanctions on China’s AI development, which consist of export restrictions on innovative AI chips to China [18] [56] The success of the business’s AI designs consequently “triggered market turmoil” [57] and caused shares in major worldwide technology companies to plunge on 27 January 2025: Nvidia’s stock fell by as much as 17-18%, [58] as did the stock of competing Broadcom. Other tech companies likewise sank, including 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 technology stocks on Nasdaq, triggered by the release of the R1 model, had led to tape losses of about $593 billion in the market capitalizations of AI and hardware business; [59] by 28 January 2025, a total of $1 trillion of worth was rubbed out American stocks. [50]

Leading figures in the American AI sector had combined reactions to DeepSeek’s success and performance. [60] Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman-whose companies are involved 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, consisting of 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 companies, including Amazon Web Services, Toyota, and Stripe, are looking for to utilize the model in their program. [68]

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

Some sources have actually observed that the official application shows interface (API) variation of R1, which runs from servers located in China, uses censorship mechanisms for subjects that are thought about politically sensitive for the government of China. For example, the design declines to respond to questions about the 1989 Tiananmen Square demonstrations and massacre, persecution of Uyghurs, contrasts between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI might initially produce an answer, however then erases it quickly afterwards and replaces it with a message such as: “Sorry, that’s beyond my current scope. Let’s discuss something else.” [72] The incorporated censorship mechanisms and restrictions can only be eliminated to a minimal extent in the open-source version of the R1 model. If the “core socialist values” specified by the Chinese Internet regulatory authorities are touched upon, or the political status of Taiwan is raised, discussions are terminated. [74] When checked by NBC News, DeepSeek’s R1 described Taiwan as “an inalienable part of China’s territory,” and specified: “We firmly oppose any form of ‘Taiwan self-reliance’ separatist activities and are devoted to attaining the complete reunification of the motherland through serene methods.” [75] In January 2025, Western scientists were able to deceive DeepSeek into giving particular answers to a few of these by asking for in its answer to switch particular letters for similar-looking numbers. [73]

Security and privacy

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

Artificial intelligence market in China.

Notes

^ a b c The number 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 picking “Deep Think enabled”, and every user could utilize it only 50 times a day.
References

^ Gibney, Elizabeth (23 January 2025). “China’s low-cost, open AI model DeepSeek delights scientists”. Nature. doi:10.1038/ d41586-025-00229-6. ISSN 1476-4687. PMID 39849139.
^ a b Vincent, James (28 January 2025). “The DeepSeek panic 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 cast doubt on trillions in AI infrastructure costs”. Business Insider.
^ Mallick, Subhrojit (16 January 2024). “Biden admin’s cap on GPU exports might hit 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 brand-new chip war probe”. BBC. Retrieved 27 January 2025.
^ a b c Metz, Cade (27 January 2025). “What is DeepSeek? And How Is It Upending A.I.?”. The New York City Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Field, Hayden (27 January 2025). “China’s DeepSeek AI dethrones ChatGPT on App Store: Here’s what you ought to understand”. CNBC.
^ Picchi, Aimee (27 January 2025). “What is DeepSeek, and why is it causing Nvidia and other stocks to drop?”. 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 model overcame 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 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 changing 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 original on 17 July 2024. Retrieved 28 December 2024.
^ a b Schneider, Jordan (27 November 2024). “Deepseek: The Quiet Giant Leading China’s AI Race”. ChinaTalk. Retrieved 28 December 2024.
^ “DeepSeek-Coder/LICENSE-MODEL at main · deepseek-ai/DeepSeek-Coder”. GitHub. Archived from the 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, retrieved 27 January 2025.
^ a b Dai, Damai; Deng, Chengqi; Zhao, Chenggang; Xu, R. X.; Gao, Huazuo; Chen, Deli; Li, Jiashi; Zeng, Wangding; Yu, Xingkai (11 January 2024), DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models, arXiv:2401.06066.
^ Shao, Zhihong; Wang, Peiyi; Zhu, Qihao; Xu, Runxin; Song, Junxiao; Bi, Xiao; Zhang, Haowei; Zhang, Mingchuan; Li, Y. K. (27 April 2024), DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models, arXiv:2402.03300.
^ Wang, Peiyi; Li, Lei; Shao, Zhihong; Xu, R. X.; Dai, Damai; Li, Yifei; Chen, Deli; Wu, Y.; Sui, Zhifang (19 February 2024), Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations, arXiv:2312.08935. ^ a b c d DeepSeek-AI; Liu, Aixin; Feng, Bei; Wang, Bin; Wang, Bingxuan; Liu, Bo; Zhao, Chenggang; Dengr, Chengqi; Ruan, Chong (19 June 2024), DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model, arXiv:2405.04434.
^ a b Peng, Bowen; Quesnelle, Jeffrey; Fan, Honglu; Shippole, Enrico (1 November 2023), YaRN: Efficient Context Window Extension of Large Language Models, arXiv:2309.00071.
^ “config.json · deepseek-ai/DeepSeek-V 2-Lite at main”. huggingface.co. 15 May 2024. Retrieved 28 January 2025.
^ “config.json · deepseek-ai/DeepSeek-V 2 at main”. huggingface.co. 6 May 2024. Retrieved 28 January 2025.
^ DeepSeek-AI; Zhu, Qihao; Guo, Daya; Shao, Zhihong; Yang, Dejian; Wang, Peiyi; Xu, Runxin; Wu, Y.; Li, Yukun (17 June 2024), DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence, arXiv:2406.11931.
^ “deepseek-ai/DeepSeek-V 2.5 · Hugging Face”. huggingface.co. 3 January 2025. Retrieved 28 January 2025.
^ a b c d e f g DeepSeek-AI; Liu, Aixin; Feng, Bei; Xue, Bing; Wang, Bingxuan; Wu, Bochao; Lu, Chengda; Zhao, Chenggang; Deng, Chengqi (27 December 2024), DeepSeek-V3 Technical Report, arXiv:2412.19437.
^ “config.json · deepseek-ai/DeepSeek-V 3 at main”. huggingface.co. 26 December 2024. Retrieved 28 January 2025.
^ Jiang, Ben (27 December 2024). “Chinese start-up DeepSeek’s brand-new AI design surpasses 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, exceeds Llama and Qwen on launch”. VentureBeat. Archived from the original on 27 December 2024. Retrieved 28 December 2024.
^ Wiggers, Kyle (26 December 2024). “DeepSeek’s brand-new AI design seems among the finest ‘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: releasing supercharged reasoning power!”. DeepSeek API Docs. Archived from the initial on 20 November 2024. Retrieved 28 January 2025.
^ Franzen, Carl (20 November 2024). “DeepSeek’s first reasoning model R1-Lite-Preview turns heads, beating OpenAI o1 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 original on 21 January 2025. Retrieved 21 January 2025.
^ a b c d DeepSeek-AI; Guo, Daya; Yang, Dejian; Zhang, Haowei; Song, Junxiao; Zhang, Ruoyu; Xu, Runxin; Zhu, Qihao; Ma, Shirong (22 January 2025), DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning, arXiv:2501.12948.
^ “Chinese AI 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 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 moment’: $1tn cleaned off US stocks after Chinese company unveils AI chatbot” – by means of 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 states”. Reuters. Retrieved 30 January 2025.
^ 澎湃新闻 (22 January 2025). “量化巨头幻方创始人梁文锋参加总理座谈会并发言 , 他还创办了” AI界拼多多””. finance.sina.com.cn. Retrieved 31 January 2025.
^ Shilov, Anton (27 December 2024). “Chinese AI business’s AI model breakthrough 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, wiping $500bn off Nvidia”. BBC News. Retrieved 27 January 2025.
^ Nazareth, Rita (26 January 2025). “Stock Rout Gets Ugly as Nvidia Extends Loss to 17%: Markets Wrap”. Bloomberg. Retrieved 27 January 2025.
^ Carew, Sinéad; Cooper, Amanda; Banerjee, Ankur (27 January 2025). “DeepSeek sparks global AI selloff, Nvidia losses about $593 billion of 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 promotes DeepSeek’s open-source AI as “very remarkable”: “We need to 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 model ‘excellent'”. 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 development “favorable””. Axios.
^ Karaian, Jason; Rennison, Joe (27 January 2025). “China’s A.I. Advances Spook Big Tech Investors on Wall Street” – through NYTimes.com.
^ Sharma, Manoj (6 January 2025). “Musk dismisses, Altman applauds: What leaders say on DeepSeek’s disruption”. Fortune India. Retrieved 28 January 2025.
^ “Elon Musk ‘questions’ DeepSeek’s claims, recommends huge Nvidia GPU infrastructure”. Financialexpress. 28 January 2025. Retrieved 28 January 2025.
^ Kim, Eugene. “Big AWS clients, 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 ‘massive’ cyber-attack after AI chatbot tops app shops”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek momentarily limited new sign-ups, citing ‘massive harmful attacks'”. Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has sparked a $1 trillion panic – and it doesn’t care about totally 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 checked out 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 a global 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, offering 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 privacy threats”. Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). “Experts prompt care over use of Chinese AI DeepSeek”. The Guardian. ISSN 0261-3077. Retrieved 28 January 2025.
^ Hornby, Rael (28 January 2025). “DeepSeek’s success has 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 protection”. 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, official says”. Reuters. Retrieved 28 January 2025.

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