This Stage Utilized 3 Reward Models

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

The DeepSeek-R1 design provides actions comparable to other contemporary large 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 needs a tenth of the computing power of an equivalent LLM. [2] [3] [4] DeepSeek’s AI models 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 develop advanced AI systems. [6] [7]

On 10 January 2025, DeepSeek launched its very first free chatbot app, based upon the DeepSeek-R1 model, for iOS and Android; by 27 January, DeepSeek-R1 had actually surpassed ChatGPT as the most-downloaded free app on the iOS App Store in the United States, [8] triggering Nvidia’s share rate to drop by 18%. [9] [10] DeepSeek’s success against larger and more recognized rivals has actually been described as “overthrowing AI”, [8] constituting “the first chance at what is becoming an international AI area race”, [11] and ushering in “a new period of AI brinkmanship”. [12]

DeepSeek makes its generative expert system algorithms, models, and training details open-source, allowing its code to be freely available for usage, modification, viewing, and developing documents for building functions. [13] The business apparently intensely hires young AI researchers from leading Chinese universities, [8] and hires from outside the computer science field to diversify its models’ understanding and capabilities. [3]

In February 2016, High-Flyer was co-founded by AI lover Liang Wenfeng, who had been trading because the 2007-2008 financial crisis while going to Zhejiang University. [14] By 2019, he developed High-Flyer as a hedge fund concentrated on developing and utilizing AI trading algorithms. By 2021, High-Flyer specifically used AI in trading. [15] DeepSeek has actually made its generative artificial intelligence chatbot open source, indicating its code is freely offered for use, modification, and viewing. This includes authorization to access and utilize the source code, as well as design files, for developing purposes. [13]

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

In April 2023, High-Flyer began a synthetic basic intelligence laboratory committed to research developing AI tools different from High-Flyer’s financial organization. [17] [18] In May 2023, with High-Flyer as one of the investors, the lab became its own company, DeepSeek. [15] [19] [18] Venture capital firms were hesitant in providing funding as it was unlikely that it would have the ability to create an exit in a short period of time. [15]

After releasing DeepSeek-V2 in May 2024, which provided strong efficiency for a low price, DeepSeek ended up being referred to as the driver for China’s AI design price war. It was quickly called 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 compete with the business. Despite the low price charged by DeepSeek, it paid compared to its rivals that were losing money. [20]

DeepSeek is focused on research and has no detailed strategies for commercialization; [20] this also permits its innovation to avoid the most rigid provisions of China’s AI guidelines, such as requiring consumer-facing innovation to adhere to the government’s controls on details. [3]

DeepSeek’s hiring choices target technical abilities rather than work experience, leading to most brand-new hires being either recent university graduates or designers whose AI professions are less established. [18] [3] Likewise, the company recruits people without any computer system science background to help its innovation comprehend other topics and knowledge locations, including being able to create poetry and carry out well on the notoriously tough Chinese college admissions examinations (Gaokao). [3]

Development and release history

DeepSeek LLM

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

They are of the exact 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 designs.
3. Supervised finetuning (SFT): 2B tokens of guideline information. 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 released the DeepSeek-LLM series of designs, with 7B and 67B parameters in both Base and Chat forms (no Instruct was released). It was established to complete with other LLMs readily available at the time. The paper declared benchmark outcomes higher than a lot of open source LLMs at the time, specifically 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 basically 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 gotten by deduplicating the Common Crawl. [26]

The Chat variations of the 2 Base designs was also released simultaneously, obtained by training Base by supervised finetuning (SFT) followed by direct policy optimization (DPO). [26]

On 9 January 2024, they released 2 DeepSeek-MoE designs (Base, Chat), each of 16B criteria (2.7 B activated 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 equivalent performance with a 16B MoE as a 7B non-MoE. In architecture, it is a version of the standard sparsely-gated MoE, with “shared specialists” that are constantly queried, and “routed specialists” that might not be. They found this to assist with expert balancing. In standard MoE, some professionals can become extremely counted on, while other experts may be hardly ever utilized, squandering parameters. Attempting to balance the professionals so that they are similarly used then triggers professionals to replicate the same capacity. They proposed the shared professionals to find out core capabilities that are frequently used, and let the routed experts to learn the peripheral capacities that are rarely utilized. [28]

In April 2024, they released 3 DeepSeek-Math models 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 model by with 776K mathematics problems and their tool-use-integrated detailed solutions. This produced the Instruct model.
Reinforcement learning (RL): The reward design was a procedure reward model (PRM) trained from Base according to the Math-Shepherd technique. [30] This benefit model was then used to train Instruct using group relative policy optimization (GRPO) on a dataset of 144K math questions “associated to GSM8K and MATH”. The benefit design was continually upgraded throughout training to prevent reward hacking. This led to the RL design.

V2

In May 2024, they released the DeepSeek-V2 series. The series consists of 4 designs, 2 base models (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 security. This resulted in DeepSeek-V2-Chat (SFT) which was not launched.
4. RL using GRPO in two phases. The first stage was trained to resolve math and coding issues. This phase utilized 1 reward design, trained on compiler feedback (for coding) and ground-truth labels (for mathematics). The 2nd stage was trained to be practical, safe, and follow guidelines. This stage used 3 reward designs. The helpfulness and security reward designs were trained on human choice information. The rule-based benefit design was by hand configured. All experienced benefit designs were initialized from DeepSeek-V2-Chat (SFT). This resulted in the released version of DeepSeek-V2-Chat.

They selected 2-staged RL, since they found that RL on reasoning information had “special qualities” various from RL on general data. For example, RL on reasoning could improve over more training actions. [31]

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

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

The Financial Times reported that it was less expensive than its peers with a cost 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 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 even more for 6T tokens, then context-extended to 128K context length. This produced the Base designs.
DeepSeek-Coder and DeepSeek-Math were used to generate 20K code-related and 30K math-related guideline information, then integrated with a guideline dataset of 300M tokens. This was used for SFT.
2. RL with GRPO. The reward for math issues was calculated by comparing with the ground-truth label. The benefit for code issues was produced by a benefit design trained to forecast whether a program would pass the system tests.

DeepSeek-V2.5 was released 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 released a base design DeepSeek-V3-Base and a chat design DeepSeek-V3. The model architecture is essentially the very 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 contained a higher ratio of math and programs than the pretraining dataset of V2.
2. Extend context length two times, from 4K to 32K and then to 128K, using YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 dates on 1.5 M samples of reasoning (math, shows, logic) and non-reasoning (innovative writing, roleplay, simple concern answering) data. Reasoning data was produced by “skilled models”. Non-reasoning information was produced by DeepSeek-V2.5 and examined by people. – The “professional models” were trained by beginning with an undefined base design, then SFT on both information, and artificial data created by an internal DeepSeek-R1 model. The system prompt asked the R1 to show and verify throughout thinking. Then the specialist designs were RL using an unspecified reward function.
– Each expert model was trained to generate simply synthetic thinking information in one specific domain (math, programs, logic).
– Expert designs were utilized, instead of R1 itself, because the output from R1 itself suffered “overthinking, poor format, and excessive length”.

4. Model-based benefit designs were made by beginning with a SFT checkpoint of V3, then finetuning on human preference data consisting of both last benefit and chain-of-thought resulting in the final benefit. The reward model produced reward signals for both questions with objective however free-form responses, and concerns without objective responses (such as innovative writing).
5. A SFT checkpoint of V3 was trained by GRPO utilizing both reward designs and rule-based reward. The rule-based benefit was computed for mathematics issues with a final answer (put in a box), and for programs issues by unit tests. This produced DeepSeek-V3.

The DeepSeek group carried out comprehensive low-level engineering to attain efficiency. They used 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 properly. They utilized a customized 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 committing 20 streaming multiprocessors out of 132 per H800 for only inter-GPU communication. They lowered interaction by rearranging (every 10 minutes) the specific maker each specialist was on in order to prevent particular makers being queried more frequently than the others, including auxiliary load-balancing losses to the training loss function, and other load-balancing techniques. [37]

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

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

R1

On 20 November 2024, DeepSeek-R1-Lite-Preview became available through DeepSeek’s API, in addition to through a chat interface after visiting. [42] [43] [note 3] It was trained for logical inference, mathematical reasoning, and real-time problem-solving. DeepSeek declared that it went beyond performance of OpenAI o1 on standards such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal mentioned when it utilized 15 problems from the 2024 edition of AIME, the o1 design reached a service faster 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 also launched some “DeepSeek-R1-Distill” models, which are not initialized on V3-Base, however rather are initialized from other pretrained open-weight models, including LLaMA and Qwen, then fine-tuned on synthetic data created by R1. [47]

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

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

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

1. SFT DeepSeek-V3-Base on “thousands” of “cold-start” information all with the basic format of|special_token|| special_token|summary >.
2. Apply the same RL procedure as R1-Zero, however likewise with a “language consistency benefit” to motivate it to react monolingually. This produced an internal design not released.
3. Synthesize 600K reasoning data from the internal design, with rejection sampling (i.e. if the produced thinking had an incorrect last answer, then it is eliminated). Synthesize 200K non-reasoning information (writing, factual 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 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 manufactured from DeepSeek-R1, in a comparable method as action 3 above. They were not trained with RL. [47]

Assessment and responses

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

DeepSeek-V3 uses significantly less resources compared to its peers; for example, whereas the world’s leading AI companies train their chatbots with supercomputers using as many as 16,000 graphics processing systems (GPUs), if not more, DeepSeek declares to have actually needed only about 2,000 GPUs, namely the H800 series chip from Nvidia. [37] It was trained in around 55 days at a cost of US$ 5.58 million, [37] which is roughly one tenth of what United States tech huge Meta invested developing its most current AI technology. [3]

DeepSeek’s competitive performance at relatively very little cost has been acknowledged 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 most current designs when utilized for tasks such as mathematics, coding, and natural language thinking; [51] echoing other commentators, American Silicon Valley endeavor capitalist Marc Andreessen also explained R1 as “AI’s Sputnik minute”. [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 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 experts and asked him to provide viewpoints and tips on a draft for remarks of the yearly 2024 government work report. [55]

DeepSeek’s optimization of limited resources has actually highlighted potential limitations of United States sanctions on China’s AI development, that include export restrictions on advanced AI chips to China [18] [56] The success of the company’s AI designs as a result “triggered market turmoil” [57] and triggered shares in 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 firms likewise sank, consisting of Microsoft (down 2.5%), Google’s owner Alphabet (down over 4%), and Dutch chip equipment maker ASML (down over 7%). [51] A global selloff of innovation stocks on Nasdaq, triggered by the release of the R1 design, had led to tape-record losses of about $593 billion in the market capitalizations of AI and hardware companies; [59] by 28 January 2025, an overall of $1 trillion of worth was rubbed out American stocks. [50]

Leading figures in the American AI sector had combined responses to DeepSeek’s success and performance. [60] Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman-whose business are associated with the United States government-backed “Stargate Project” to establish American AI infrastructure-both called DeepSeek “very excellent”. [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, consisting of 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 looking for to use the design in their program. [68]

On 27 January 2025, DeepSeek restricted its brand-new user registration to phone numbers from mainland China, email addresses, or Google account logins, following a “large-scale” cyberattack interrupted the appropriate performance of its servers. [69] [70]

Some sources have observed that the main application programs interface (API) version of R1, which runs from servers located in China, utilizes censorship systems for topics that are considered politically sensitive for the federal government of China. For instance, the model refuses to respond to concerns about the 1989 Tiananmen Square protests and massacre, persecution of Uyghurs, contrasts between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI might at first produce a response, but then erases it soon later on and changes it with a message such as: “Sorry, that’s beyond my present scope. Let’s discuss something else.” [72] The integrated censorship mechanisms and constraints can only be gotten rid of to a minimal level in the open-source variation of the R1 model. If the “core socialist worths” specified 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 explained Taiwan as “an inalienable part of China’s territory,” and specified: “We securely oppose any type of ‘Taiwan self-reliance’ separatist activities and are devoted to achieving the complete reunification of the motherland through peaceful ways.” [75] In January 2025, Western researchers were able to trick DeepSeek into offering certain answers to a few of these topics by asking for in its response to swap specific letters for similar-looking numbers. [73]

Security and privacy

Some experts fear that the federal government of China might utilize the AI system for foreign impact operations, spreading out disinformation, surveillance and the development of cyberweapons. [76] [77] [78] DeepSeek’s privacy terms and conditions say “We store the information we collect in protected servers located in individuals’s Republic of China … We may gather your text or audio input, timely, uploaded files, feedback, chat history, or other content that you offer to our model and Services”. Although the information storage and collection policy follows ChatGPT’s privacy policy, [79] a Wired short article reports this as security issues. [80] In response, the Italian information security authority is looking for additional details on DeepSeek’s collection and usage of individual information, and the United States National Security Council revealed that it had begun a national security evaluation. [81] [82] Taiwan’s federal government banned using DeepSeek at federal government ministries on security premises and South Korea’s Personal Information Protection Commission opened a questions into DeepSeek’s usage of individual information. [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 named DeepSeek-Coder-V2 Chat in the paper was launched as DeepSeek-Coder-V2-Instruct in HuggingFace.
^ At that time, the R1-Lite-Preview required choosing “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 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 reveals an AI world ready to blow”. The Guardian.
^ a b c d e f g Metz, Cade; Tobin, Meaghan (23 January 2025). “How Chinese A.I. Start-Up DeepSeek Is Competing With Silicon Valley Giants”. The New York City 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 costs”. Business Insider.
^ Mallick, Subhrojit (16 January 2024). “Biden admin’s cap on GPU exports may hit India’s AI ambitions”. The Economic Times. Retrieved 29 January 2025.
^ Saran, Cliff (10 December 2024). “Nvidia examination signals expanding 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 dismisses ChatGPT on App Store: Here’s what you should understand”. CNBC.
^ Picchi, Aimee (27 January 2025). “What is DeepSeek, and why is it triggering Nvidia and other stocks to plunge?”. CBS News.
^ Zahn, Max (27 January 2025). “Nvidia, Microsoft shares 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 City Times. ISSN 0362-4331. Retrieved 28 January 2025.
^ a b Romero, Luis E. (28 January 2025). “ChatGPT, DeepSeek, Or Llama? Meta’s LeCun Says Open-Source Is The Key”. Forbes.
^ Chen, Caiwei (24 January 2025). “How a top Chinese AI model 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 designs are trained”. South China Morning Post. Archived from the original on 22 January 2025. Retrieved 1 January 2025.
^ a b McMorrow, Ryan; Olcott, Eleanor (9 June 2024). “The Chinese quant fund-turned-AI 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, recovered 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 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 primary”. huggingface.co. 26 December 2024. Retrieved 28 January 2025.
^ Jiang, Ben (27 December 2024). “Chinese start-up DeepSeek’s brand-new AI design exceeds Meta, OpenAI items”. South China Morning Post. Archived from the original on 27 December 2024. Retrieved 28 December 2024.
^ Sharma, Shubham (26 December 2024). “DeepSeek-V3, ultra-large open-source AI, exceeds Llama and Qwen on launch”. VentureBeat. Archived from the initial on 27 December 2024. Retrieved 28 December 2024.
^ Wiggers, Kyle (26 December 2024). “DeepSeek’s new AI model seems among the best ‘open’ oppositions yet”. TechCrunch. Archived from the initial on 2 January 2025. Retrieved 31 December 2024.
^ “Deepseek Log in page”. DeepSeek. Retrieved 30 January 2025.
^ “News|DeepSeek-R1-Lite Release 2024/11/20: DeepSeek-R1-Lite-Preview is now live: letting loose supercharged thinking power!”. DeepSeek API Docs. Archived from the original on 20 November 2024. Retrieved 28 January 2025.
^ Franzen, Carl (20 November 2024). “DeepSeek’s very first reasoning design R1-Lite-Preview turns heads, beating OpenAI o1 performance”. VentureBeat. Archived from the initial on 22 November 2024. Retrieved 28 December 2024.
^ Huang, Raffaele (24 December 2024). “Don’t Look Now, but China’s AI Is Catching Up Fast”. The Wall Street Journal. Archived from the 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 startup 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 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 minute’: $1tn wiped off US stocks after Chinese company reveals 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 start-up that shook the tech world?|CNN Business”. CNN. Retrieved 29 January 2025.
^ “DeepSeek postures 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 design 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 sparks US market turmoil, wiping $500bn off Nvidia”. BBC News. Retrieved 27 January 2025.
^ Nazareth, Rita (26 January 2025). “Stock Rout Gets Ugly as Nvidia Extends Loss to 17%: Markets Wrap”. Bloomberg. Retrieved 27 January 2025.
^ Carew, Sinéad; Cooper, Amanda; Banerjee, Ankur (27 January 2025). “DeepSeek stimulates global AI selloff, Nvidia losses about $593 billion of value”. 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 “extremely excellent”: “We ought 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 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 bashes China on AI, Trump calls DeepSeek advancement “positive””. Axios.
^ Karaian, Jason; Rennison, Joe (27 January 2025). “China’s A.I. Advances Spook Big Tech Investors on Wall Street” – by means of NYTimes.com.
^ Sharma, Manoj (6 January 2025). “Musk dismisses, Altman praises: What leaders say on DeepSeek’s interruption”. Fortune India. Retrieved 28 January 2025.
^ “Elon Musk ‘questions’ DeepSeek’s claims, suggests enormous Nvidia GPU facilities”. 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 designs”. Business Insider.
^ Kerr, Dara (27 January 2025). “DeepSeek hit with ‘massive’ cyber-attack after AI chatbot tops app shops”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek briefly limited new sign-ups, pointing out ‘massive destructive attacks'”. Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has actually stimulated a $1 trillion panic – and it doesn’t appreciate 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 looks like in the Chinese AI chatbot”. Trending Topics. Retrieved 27 January 2025.
^ a b Lu, Donna (28 January 2025). “We tried 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 chaos”. 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 moment'”. NBC News. Retrieved 27 January 2025.
^ Kimery, Anthony (26 January 2025). “China’s DeepSeek AI positions formidable cyber, information personal privacy threats”. Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). “Experts advise caution over use of Chinese AI DeepSeek”. The Guardian. ISSN 0261-3077. Retrieved 28 January 2025.
^ Hornby, Rael (28 January 2025). “DeepSeek’s success has actually painted a substantial TikTok-shaped target on its back”. LaptopMag. Retrieved 28 January 2025.
^ “Privacy policy”. Open AI. Retrieved 28 January 2025.
^ Burgess, Matt; Newman, Lily Hay (27 January 2025). “DeepSeek’s Popular AI App Is Explicitly Sending US Data to China”. Wired. ISSN 1059-1028. Retrieved 28 January 2025.
^ “Italy regulator seeks details from DeepSeek on data protection”. Reuters. 28 January 2025. Retrieved 28 January 2025.
^ Shalal, Andrea; Shepardson, David (28 January 2025). “White House evaluates effect of China AI app DeepSeek on national security, authorities states”. Reuters. Retrieved 28 January 2025.