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Company Description
This Stage Utilized 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 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 design supplies responses similar to other contemporary big language models, such as OpenAI’s GPT-4o and o1. [1] It is trained at a substantially lower cost-stated at US$ 6 million compared to $100 million for OpenAI’s GPT-4 in 2023 [2] -and needs a tenth of the computing power of an equivalent LLM. [2] [3] [4] DeepSeek’s AI models were established in the middle of United States sanctions on India and China for Nvidia chips, [5] which were planned to limit the capability of these two countries to establish advanced AI systems. [6] [7]
On 10 January 2025, DeepSeek released its very first totally free chatbot app, based upon the DeepSeek-R1 model, for iOS and Android; by 27 January, DeepSeek-R1 had actually gone beyond ChatGPT as the most-downloaded free app on the iOS App Store in the United States, [8] causing Nvidia’s share rate to visit 18%. [9] [10] DeepSeek’s success against larger and more recognized rivals has actually been described as “overthrowing AI”, [8] constituting “the very first shot at what is becoming a global AI space race”, [11] and introducing “a brand-new age of AI brinkmanship”. [12]
DeepSeek makes its generative artificial intelligence algorithms, designs, and training details open-source, enabling its code to be easily available for use, adjustment, viewing, and developing files for building purposes. [13] The business apparently intensely hires young AI scientists from top Chinese universities, [8] and hires from outside the computer system science field to diversify its models’ knowledge and abilities. [3]
In February 2016, High-Flyer was co-founded by AI enthusiast Liang Wenfeng, who had actually been trading considering that 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 utilizing AI trading algorithms. By 2021, High-Flyer exclusively utilized AI in trading. [15] DeepSeek has made its generative expert system chatbot open source, suggesting its code is freely offered for usage, adjustment, and viewing. This includes authorization to gain access to and use the source code, as well as style files, for constructing functions. [13]
According to 36Kr, Liang had actually constructed up a store of 10,000 Nvidia A100 GPUs, which are utilized to train AI [16], before the United States federal government imposed AI chip restrictions on China. [15]
In April 2023, High-Flyer started a synthetic basic intelligence laboratory committed to research study establishing AI tools different from High-Flyer’s financial company. [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 companies were hesitant in offering funding as it was unlikely that it would be able to create an exit in a brief amount of time. [15]
After launching DeepSeek-V2 in May 2024, which provided strong efficiency for a low cost, DeepSeek became known as the catalyst for China’s AI design cost war. It was rapidly 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 models to take on the business. Despite the low price charged by DeepSeek, it was profitable compared to its competitors that were losing cash. [20]
DeepSeek is concentrated on research study and has no in-depth plans for commercialization; [20] this likewise permits its innovation to avoid the most stringent arrangements of China’s AI guidelines, such as needing consumer-facing technology to adhere to the government’s controls on details. [3]
DeepSeek’s hiring preferences target technical abilities instead of work experience, leading to the majority of new hires being either recent university graduates or designers whose AI professions are less established. [18] [3] Likewise, the company hires individuals with no computer technology background to assist its innovation comprehend other subjects and understanding areas, consisting of having the ability to generate poetry and perform well on the notoriously tough Chinese college admissions tests (Gaokao). [3]
Development and release history
DeepSeek LLM
On 2 November 2023, DeepSeek launched its first series of model, DeepSeek-Coder, which is available for totally free to both scientists and business users. The code for the model was made open-source under the MIT license, with an extra license contract (“DeepSeek license”) regarding “open and responsible downstream usage” for the design itself. [21]
They are of the exact same architecture as DeepSeek LLM detailed below. The series consists of 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 data. 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 released the DeepSeek-LLM series of designs, with 7B and 67B criteria in both Base and Chat forms (no Instruct was launched). It was developed 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, particularly Llama 2. [26]: section 5 Like DeepSeek Coder, the code for the model was under MIT license, with DeepSeek license for the model itself. [27]
The architecture was basically the exact same as 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 models was likewise released simultaneously, acquired by training Base by supervised finetuning (SFT) followed by direct policy optimization (DPO). [26]
On 9 January 2024, they launched 2 DeepSeek-MoE models (Base, Chat), each of 16B specifications (2.7 B activated per token, 4K context length). The training was essentially the exact same as DeepSeek-LLM 7B, and was trained on a part of its training dataset. They claimed equivalent efficiency 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 experts” that may not be. They found this to help with professional balancing. In basic MoE, some experts can become overly depended on, while other experts may be seldom utilized, wasting specifications. Attempting to balance the experts so that they are similarly utilized then triggers experts to replicate the same capacity. They proposed the shared experts to find out core capacities that are often used, and let the routed experts to learn the peripheral capacities that are rarely used. [28]
In April 2024, they launched 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 design.
3. Train an instruction-following model by SFT Base with 776K math issues and their tool-use-integrated step-by-step options. This produced the Instruct model.
Reinforcement knowing (RL): The benefit design was a process benefit design (PRM) trained from Base according to the Math-Shepherd approach. [30] This benefit design was then used to train Instruct utilizing group relative policy optimization (GRPO) on a dataset of 144K math concerns “related to GSM8K and MATH”. The reward design was continuously updated throughout training to prevent reward hacking. This led to the RL design.
V2
In May 2024, they launched the DeepSeek-V2 series. The series consists of 4 designs, 2 base designs (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (-Chat). The two larger models were trained as follows: [31]
1. Pretrain on a dataset of 8.1 T tokens, where Chinese tokens are 12% more than English ones.
2. Extend context length from 4K to 128K using YaRN. [32] This led to DeepSeek-V2.
3. SFT with 1.2 M circumstances for helpfulness and 0.3 M for safety. This led to DeepSeek-V2-Chat (SFT) which was not launched.
4. RL using GRPO in two stages. The very first stage was trained to fix mathematics and coding problems. This stage used 1 benefit design, trained on compiler feedback (for coding) and ground-truth labels (for mathematics). The second stage was trained to be practical, safe, and follow rules. This stage used 3 benefit models. The helpfulness and safety reward models were trained on human choice information. The rule-based benefit model was by hand programmed. All qualified benefit designs were initialized from DeepSeek-V2-Chat (SFT). This resulted in the launched variation of DeepSeek-V2-Chat.
They selected 2-staged RL, since they discovered that RL on thinking information had “special qualities” different from RL on basic information. For example, RL on thinking could improve over more training actions. [31]
The 2 V2-Lite designs were smaller sized, and trained likewise, though DeepSeek-V2-Lite-Chat only went through SFT, not RL. They trained the Lite version to assist “additional research study and advancement on MLA and DeepSeekMoE”. [31]
Architecturally, the V2 models were considerably customized from the DeepSeek LLM series. They changed the basic attention mechanism by a low-rank approximation called multi-head latent attention (MLA), and utilized the mixture of experts (MoE) variant formerly released in January. [28]
The Financial Times reported that it was cheaper 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 released 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 further for 6T tokens, then context-extended to 128K context length. This produced the Base designs.
DeepSeek-Coder and DeepSeek-Math were used to 20K code-related and 30K math-related direction data, then integrated with a direction dataset of 300M tokens. This was used for SFT.
2. RL with GRPO. The reward for mathematics issues was calculated by comparing with the ground-truth label. The reward for code issues was generated 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 design DeepSeek-V3-Base and a chat model DeepSeek-V3. The model architecture is essentially the like V2. They were trained as follows: [37]
1. Pretraining on 14.8 T tokens of a multilingual corpus, mainly English and Chinese. It included a higher ratio of math and programming than the pretraining dataset of V2.
2. Extend context length two times, from 4K to 32K and then to 128K, using YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 epochs on 1.5 M samples of reasoning (mathematics, shows, logic) and non-reasoning (imaginative writing, roleplay, simple concern answering) information. Reasoning information was produced by “expert designs”. Non-reasoning data was created by DeepSeek-V2.5 and checked by humans. – The “professional models” were trained by beginning with an undefined base model, then SFT on both information, and synthetic data produced by an internal DeepSeek-R1 model. The system timely asked the R1 to show and validate during thinking. Then the professional designs were RL utilizing an undefined benefit function.
– Each specialist design was trained to generate just synthetic reasoning data in one particular domain (mathematics, programming, reasoning).
– Expert models were utilized, rather of R1 itself, since the output from R1 itself suffered “overthinking, poor formatting, and excessive length”.
4. Model-based reward models were made by starting with a SFT checkpoint of V3, then finetuning on human preference information including both last benefit and chain-of-thought leading to the final benefit. The reward model produced reward signals for both questions with objective but free-form answers, and concerns without objective answers (such as imaginative writing).
5. A SFT checkpoint of V3 was trained by GRPO utilizing both benefit designs and rule-based reward. The rule-based benefit was computed for mathematics issues with a last response (put in a box), and for shows issues by unit tests. This produced DeepSeek-V3.
The DeepSeek team performed comprehensive low-level engineering to attain effectiveness. They used mixed-precision arithmetic. Much of the forward pass was performed in 8-bit floating point numbers (5E2M: 5-bit exponent and 2-bit mantissa) rather than the basic 32-bit, requiring special GEMM routines to build up properly. They utilized a custom 12-bit float (E5M6) for just the inputs to the linear layers after the attention modules. Optimizer states remained in 16-bit (BF16). They reduced the communication latency by overlapping extensively calculation and communication, such as devoting 20 streaming multiprocessors out of 132 per H800 for only inter-GPU communication. They reduced communication by rearranging (every 10 minutes) the exact device each professional was on in order to prevent certain makers being queried more frequently than the others, including auxiliary load-balancing losses to the training loss function, and other load-balancing strategies. [37]
After training, it was deployed on H800 clusters. The H800 cards within a cluster are linked by NVLink, and the clusters are linked by InfiniBand. [37]
Benchmark tests reveal 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 ended up being available by means of DeepSeek’s API, along with via a chat interface after logging in. [42] [43] [note 3] It was trained for rational reasoning, mathematical thinking, and real-time problem-solving. DeepSeek declared that it surpassed 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 model reached an option much 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 company also released some “DeepSeek-R1-Distill” models, which are not initialized on V3-Base, however instead are initialized from other pretrained open-weight designs, consisting of LLaMA and Qwen, then fine-tuned on artificial data generated by R1. [47]
A conversation in between User and Assistant. The user asks a concern, and the Assistant resolves it. The assistant initially thinks of the thinking procedure in the mind and then offers the user with the response. The thinking procedure and answer are enclosed within and tags, respectively, i.e., thinking process here respond to here. User:. Assistant:
DeepSeek-R1-Zero was trained solely utilizing GRPO RL without SFT. Unlike previous variations, they used no model-based reward. All benefit functions were rule-based, “generally” of 2 types (other types were not specified): precision benefits and format rewards. Accuracy reward was checking whether a boxed response is correct (for math) or whether a code passes tests (for programming). Format benefit was examining whether the design puts its thinking trace within … [47]
As R1-Zero has concerns with readability and mixing languages, R1 was trained to deal with these concerns and more enhance thinking: [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 process as R1-Zero, however also with a “language consistency reward” to motivate it to react monolingually. This produced an internal model not launched.
3. Synthesize 600K reasoning data from the internal design, with rejection sampling (i.e. if the produced thinking had an incorrect final 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 artificial information for 2 dates.
5. GRPO RL with rule-based benefit (for reasoning jobs) and model-based benefit (for non-reasoning tasks, helpfulness, and harmlessness). This produced DeepSeek-R1.
Distilled designs were trained by SFT on 800K data synthesized from DeepSeek-R1, in a comparable 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 complimentary app on the iOS App Store in the United States; its chatbot supposedly responds to questions, fixes reasoning issues and composes computer programs on par with other chatbots on the marketplace, according to benchmark tests utilized by American AI companies. [3]
DeepSeek-V3 uses considerably less resources compared to its peers; for instance, whereas the world’s leading AI business train their chatbots with supercomputers utilizing as numerous as 16,000 graphics processing units (GPUs), if not more, DeepSeek declares to require just about 2,000 GPUs, particularly 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 giant Meta spent constructing its latest AI innovation. [3]
DeepSeek’s competitive efficiency at fairly minimal cost has actually been recognized as potentially challenging the international dominance of American AI models. [48] Various publications and news media, such as The Hill and The Guardian, described the release of its chatbot as a “Sputnik moment” for American AI. [49] [50] The performance of its R1 model was apparently “on par with” one of OpenAI’s newest models when used for jobs such as mathematics, coding, and natural language reasoning; [51] echoing other analysts, American Silicon Valley investor Marc Andreessen also explained R1 as “AI‘s Sputnik moment”. [51]
DeepSeek’s founder, 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 widely applauded DeepSeek as a nationwide asset. [53] [54] On 20 January 2025, China’s Premier Li Qiang invited Liang Wenfeng to his seminar with professionals and asked him to supply 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 advancement, that include export limitations on advanced AI chips to China [18] [56] The success of the company’s AI designs subsequently “stimulated market chaos” [57] and triggered shares in significant global innovation 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 also sank, consisting of Microsoft (down 2.5%), Google’s owner Alphabet (down over 4%), and Dutch chip equipment maker ASML (down over 7%). [51] An international selloff of innovation 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 hardware business; [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 combined responses to DeepSeek’s success and performance. [60] Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman-whose business are involved in the United States government-backed “Stargate Project” to develop American AI infrastructure-both called DeepSeek “extremely remarkable”. [61] [62] American President Donald Trump, who revealed The Stargate Project, called DeepSeek a wake-up call [63] and a favorable advancement. [64] [50] [51] [65] Other leaders in the field, including Scale AI CEO Alexandr Wang, Anthropic cofounder and CEO Dario Amodei, and Elon Musk expressed apprehension of the app’s efficiency or of the sustainability of its success. [60] [66] [67] Various companies, consisting of Amazon Web Services, Toyota, and Stripe, are seeking to use the design in their program. [68]
On 27 January 2025, DeepSeek limited its brand-new user registration to phone numbers from mainland China, email addresses, or Google account logins, following a “large-scale” cyberattack interfered with the appropriate functioning of its servers. [69] [70]
Some sources have observed that the main application programs user interface (API) version of R1, which ranges from servers located in China, utilizes censorship mechanisms for subjects that are considered politically delicate for the federal government of China. For instance, the model declines to answer questions about the 1989 Tiananmen Square protests and massacre, persecution of Uyghurs, comparisons between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI may at first produce an answer, but then deletes it soon afterwards and changes it with a message such as: “Sorry, that’s beyond my present scope. Let’s discuss something else.” [72] The integrated censorship systems 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 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 explained Taiwan as “an inalienable part of China’s area,” and specified: “We strongly oppose any kind of ‘Taiwan independence’ separatist activities and are committed to achieving the total reunification of the motherland through tranquil methods.” [75] In January 2025, Western scientists were able to deceive DeepSeek into giving certain responses to some of these topics by requesting in its response to switch certain letters for similar-looking numbers. [73]
Security and personal privacy
Some professionals fear that the federal government of China could use the AI system for foreign influence operations, spreading out disinformation, monitoring and the advancement of cyberweapons. [76] [77] [78] DeepSeek’s privacy conditions state “We store the details we gather in safe servers found in the People’s Republic of China … We might gather your text or audio input, timely, uploaded files, feedback, chat history, or other content that you supply to our model and Services”. Although the data storage and collection policy follows ChatGPT’s personal privacy policy, [79] a Wired post reports this as security concerns. [80] In action, the Italian information protection authority is looking for extra details on DeepSeek’s collection and use of personal information, and the United States National Security Council revealed that it had started a national security review. [81] [82] Taiwan’s government banned making use of DeepSeek at government ministries on security premises and South Korea’s Personal Information Protection Commission opened a query into DeepSeek’s use of individual info. [83]
Artificial intelligence market in China.
Notes
^ a b c The number of heads does not equivalent the number of KV heads, due to GQA.
^ Inexplicably, the model called DeepSeek-Coder-V2 Chat in the paper was launched as DeepSeek-Coder-V2-Instruct in HuggingFace.
^ At that time, the R1-Lite-Preview needed picking “Deep Think made it possible for”, and every user might utilize it just 50 times a day.
References
^ Gibney, Elizabeth (23 January 2025). “China’s inexpensive, open AI design DeepSeek delights 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 more affordable designs and weaker chips bring into question trillions in AI infrastructure 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 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 dethrones ChatGPT on App Store: Here’s what you must know”. 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 City Times. ISSN 0362-4331. Retrieved 28 January 2025.
^ a b Romero, Luis E. (28 January 2025). “ChatGPT, DeepSeek, Or Llama? Meta’s LeCun Says Open-Source Is The Key”. Forbes.
^ Chen, Caiwei (24 January 2025). “How a top Chinese AI design 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 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, 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, 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 primary”. huggingface.co. 6 May 2024. Retrieved 28 January 2025.
^ DeepSeek-AI; Zhu, Qihao; Guo, Daya; Shao, Zhihong; Yang, Dejian; Wang, Peiyi; Xu, Runxin; Wu, Y.; Li, Yukun (17 June 2024), DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence, arXiv:2406.11931.
^ “deepseek-ai/DeepSeek-V 2.5 · Hugging Face”. huggingface.co. 3 January 2025. Retrieved 28 January 2025.
^ a b c d e f g DeepSeek-AI; Liu, Aixin; Feng, Bei; Xue, Bing; Wang, Bingxuan; Wu, Bochao; Lu, Chengda; Zhao, Chenggang; Deng, Chengqi (27 December 2024), DeepSeek-V3 Technical Report, arXiv:2412.19437.
^ “config.json · deepseek-ai/DeepSeek-V 3 at main”. huggingface.co. 26 December 2024. Retrieved 28 January 2025.
^ Jiang, Ben (27 December 2024). “Chinese start-up DeepSeek’s new AI model outperforms Meta, OpenAI products”. South China Morning Post. Archived from the original on 27 December 2024. Retrieved 28 December 2024.
^ Sharma, Shubham (26 December 2024). “DeepSeek-V3, ultra-large open-source AI, outshines Llama and Qwen on launch”. VentureBeat. Archived from the original on 27 December 2024. Retrieved 28 December 2024.
^ Wiggers, Kyle (26 December 2024). “DeepSeek’s new AI model appears to be one of 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: unleashing supercharged reasoning power!”. DeepSeek API Docs. Archived from the original on 20 November 2024. Retrieved 28 January 2025.
^ Franzen, Carl (20 November 2024). “DeepSeek’s first thinking design R1-Lite-Preview turns heads, beating OpenAI o1 performance”. VentureBeat. Archived from the initial 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 initial on 21 January 2025. Retrieved 21 January 2025.
^ a b c d DeepSeek-AI; Guo, Daya; Yang, Dejian; Zhang, Haowei; Song, Junxiao; Zhang, Ruoyu; Xu, Runxin; Zhu, Qihao; Ma, Shirong (22 January 2025), DeepSeek-R1: Incentivizing Reasoning Capability in LLMs through Reinforcement Learning, arXiv:2501.12948.
^ “Chinese AI startup DeepSeek surpasses ChatGPT on Apple App Store”. Reuters. 27 January 2025. Retrieved 27 January 2025.
^ Wade, David (6 December 2024). “American AI has actually reached its Sputnik minute”. The Hill. Archived from the initial on 8 December 2024. Retrieved 25 January 2025.
^ a b c Milmo, Dan; Hawkins, Amy; Booth, Robert; Kollewe, Julia (28 January 2025). “‘ Sputnik minute’: $1tn rubbed out US stocks after Chinese firm 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 poses an obstacle to Beijing as much as to Silicon Valley”. The Economist. 29 January 2025. ISSN 0013-0613. Retrieved 31 January 2025.
^ Paul, Katie; Nellis, Stephen (30 January 2025). “Chinese state-linked accounts hyped DeepSeek AI launch ahead of US stock rout, 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 advancement 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 sparks 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 triggers international AI selloff, Nvidia losses about $593 billion of worth”. Reuters.
^ a b Sherry, Ben (28 January 2025). “DeepSeek, Calling It ‘Impressive’ however Staying Skeptical”. Inc. Retrieved 29 January 2025.
^ Okemwa, Kevin (28 January 2025). “Microsoft CEO Satya Nadella touts DeepSeek’s open-source AI as “incredibly remarkable”: “We should take the developments out of China very, very seriously””. Windows Central. Retrieved 28 January 2025.
^ Nazzaro, Miranda (28 January 2025). “OpenAI’s Sam Altman calls DeepSeek design ‘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 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 disruption”. Fortune India. Retrieved 28 January 2025.
^ “Elon Musk ‘questions’ DeepSeek’s claims, suggests huge Nvidia GPU facilities”. Financialexpress. 28 January 2025. Retrieved 28 January 2025.
^ Kim, Eugene. “Big AWS customers, consisting of Stripe and Toyota, are pestering the cloud giant for access to DeepSeek AI designs”. Business Insider.
^ Kerr, Dara (27 January 2025). “DeepSeek struck with ‘large-scale’ cyber-attack after AI chatbot tops app shops”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek briefly restricted brand-new sign-ups, citing ‘large-scale destructive attacks'”. Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has actually triggered a $1 trillion panic – and it does not 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 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 worldwide 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 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 poses 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 actually painted a big TikTok-shaped target on its back”. LaptopMag. Retrieved 28 January 2025.
^ “Privacy policy”. Open AI. Retrieved 28 January 2025.
^ Burgess, Matt; Newman, Lily Hay (27 January 2025). “DeepSeek’s Popular AI App Is Explicitly Sending US Data to China”. Wired. ISSN 1059-1028. Retrieved 28 January 2025.
^ “Italy regulator looks for info 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 nationwide security, authorities says”. Reuters. Retrieved 28 January 2025.