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Company Description
This Stage used 3 Reward Models
DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese synthetic intelligence business that establishes open-source large language models (LLMs). Based in Hangzhou, Zhejiang, it is owned and moneyed by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, developed the business in 2023 and acts as its CEO.
The DeepSeek-R1 model offers reactions similar to other modern large language designs, such as OpenAI’s GPT-4o and o1. [1] It is trained at a considerably lower cost-stated at US$ 6 million compared to $100 million for OpenAI’s GPT-4 in 2023 [2] -and requires a tenth of the computing power of a comparable LLM. [2] [3] [4] DeepSeek’s AI models were developed amidst United States sanctions on India and China for Nvidia chips, [5] which were meant to limit the capability of these 2 countries to establish sophisticated AI systems. [6] [7]
On 10 January 2025, DeepSeek released its very first free chatbot app, based upon the DeepSeek-R1 design, for iOS and Android; by 27 January, DeepSeek-R1 had actually exceeded ChatGPT as the most-downloaded free app on the iOS App Store in the United States, [8] triggering Nvidia’s share rate to come by 18%. [9] [10] DeepSeek’s success versus bigger and more recognized competitors has been described as “overthrowing AI”, [8] making up “the first chance at what is emerging as a global AI space race”, [11] and introducing “a brand-new era of AI brinkmanship”. [12]
DeepSeek makes its generative artificial intelligence algorithms, models, and training information open-source, enabling its code to be easily available for usage, adjustment, watching, and developing files for developing functions. [13] The company reportedly vigorously recruits young AI scientists from top Chinese universities, [8] and employs from outside the computer system science field to diversify its designs’ understanding and capabilities. [3]
In February 2016, High-Flyer was co-founded by AI enthusiast Liang Wenfeng, who had actually been trading because the 2007-2008 monetary crisis while attending 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 used AI in trading. [15] DeepSeek has actually made its generative expert system chatbot open source, implying its code is easily available for usage, adjustment, and watching. This consists of authorization to access and use the source code, as well as style documents, for constructing functions. [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 imposed AI chip restrictions on China. [15]
In April 2023, High-Flyer started a synthetic general intelligence lab dedicated to research developing AI tools different from High-Flyer’s monetary organization. [17] [18] In May 2023, with High-Flyer as one of the investors, the laboratory became its own company, DeepSeek. [15] [19] [18] Equity capital firms were reluctant in offering financing as it was unlikely that it would have the ability to create an exit in a short time period. [15]
After releasing DeepSeek-V2 in May 2024, which provided strong efficiency for a low cost, DeepSeek ended up being referred to as the driver for China’s AI model rate 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 cost of their AI designs to complete with the company. Despite the low rate charged by DeepSeek, it paid compared to its competitors that were losing cash. [20]
DeepSeek is focused on research and has no detailed strategies for commercialization; [20] this also permits its technology to avoid the most rigid provisions of China’s AI regulations, such as requiring consumer-facing technology to abide by the federal government’s controls on details. [3]
DeepSeek’s employing preferences target technical capabilities rather than work experience, leading to most brand-new hires being either recent university graduates or developers whose AI careers are less developed. [18] [3] Likewise, the company hires people with no computer technology background to help its technology comprehend other topics and understanding locations, including having the ability to generate poetry and carry out well on the notoriously tough Chinese college admissions exams (Gaokao). [3]
Development and release history
DeepSeek LLM
On 2 November 2023, DeepSeek released its first series of design, DeepSeek-Coder, which is 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 agreement (“DeepSeek license”) regarding “open and accountable downstream use” for the model itself. [21]
They are of the exact same architecture as DeepSeek LLM detailed below. The series consists of 8 designs, 4 pretrained (Base) and 4 instruction-finetuned (Instruct). They all have 16K context lengths. The training was as follows: [22] [23] [24]
1. Pretraining: 1.8 T tokens (87% source code, 10% code-related English (GitHub markdown and Stack Exchange), and 3% code-unrelated Chinese).
2. Long-context pretraining: 200B tokens. This extends the context length from 4K to 16K. This produced the Base 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 released the DeepSeek-LLM series of models, with 7B and 67B specifications in both Base and Chat forms (no Instruct was released). It was developed to take on other LLMs available at the time. The paper declared benchmark outcomes greater than most open source LLMs at the time, especially Llama 2. [26]: area 5 Like DeepSeek Coder, the code for the model was under MIT license, with DeepSeek license for the model itself. [27]
The architecture was basically 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, gotten by training Base by monitored finetuning (SFT) followed by direct policy optimization (DPO). [26]
On 9 January 2024, they launched 2 DeepSeek-MoE models (Base, Chat), each of 16B criteria (2.7 B activated per token, 4K context length). The training was essentially the like DeepSeek-LLM 7B, and was trained on a part of its training dataset. They claimed comparable performance with a 16B MoE as a 7B non-MoE. In architecture, it is a variation of the basic sparsely-gated MoE, with “shared experts” that are constantly queried, and “routed professionals” that might not be. They discovered this to help with skilled balancing. In standard MoE, some professionals can end up being excessively counted on, while other experts might be hardly ever utilized, wasting parameters. Attempting to balance the professionals so that they are equally utilized then triggers professionals to replicate the same capability. They proposed the shared experts to discover core capacities that are often utilized, and let the routed professionals to discover the peripheral capacities that are rarely utilized. [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 model.
3. Train an instruction-following design by SFT Base with 776K mathematics issues and their tool-use-integrated step-by-step solutions. This produced the Instruct design.
Reinforcement learning (RL): The benefit design was a process benefit design (PRM) trained from Base according to the Math-Shepherd method. [30] This reward design was then used to train Instruct using group relative policy optimization (GRPO) on a dataset of 144K math questions “related to GSM8K and MATH”. The benefit design was continually updated during training to prevent reward hacking. This resulted in the RL design.
V2
In May 2024, they launched the DeepSeek-V2 series. The series includes 4 designs, 2 base models (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (-Chat). The 2 bigger designs were trained as follows: [31]
1. Pretrain on a dataset of 8.1 T tokens, where Chinese tokens are 12% more than English ones.
2. 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 led to DeepSeek-V2-Chat (SFT) which was not launched.
4. RL using GRPO in two phases. The very first phase was trained to solve math and coding issues. This phase utilized 1 reward design, trained on compiler feedback (for coding) and ground-truth labels (for math). The second phase was trained to be practical, safe, and follow guidelines. This phase used 3 reward models. The helpfulness and safety benefit designs were trained on human choice data. The rule-based benefit design was manually programmed. All skilled benefit models were initialized from DeepSeek-V2-Chat (SFT). This led to the launched version of DeepSeek-V2-Chat.
They decided for 2-staged RL, due to the fact that they found that RL on thinking information had “unique attributes” different from RL on basic data. For example, RL on reasoning could improve over more training steps. [31]
The 2 V2-Lite designs were smaller sized, and trained similarly, though DeepSeek-V2-Lite-Chat just underwent SFT, not RL. They trained the Lite variation to assist “more research and development on MLA and DeepSeekMoE”. [31]
Architecturally, the V2 designs were significantly modified from the DeepSeek LLM series. They changed the standard attention system by a low-rank approximation called multi-head latent attention (MLA), and used the mix of professionals (MoE) alternative previously released in January. [28]
The Financial Times reported that it was less expensive 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 further for 6T tokens, then context-extended to 128K context length. This produced the Base designs.
DeepSeek-Coder and DeepSeek-Math were utilized to produce 20K code-related and 30K math-related direction information, then integrated with a guideline dataset of 300M tokens. This was utilized for SFT.
2. RL with GRPO. The reward for math issues was computed by comparing with the ground-truth label. The benefit for code issues was created by a reward design trained to predict whether a program would pass the unit tests.
DeepSeek-V2.5 was released in September and updated in December 2024. It was made by integrating DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. [36]
V3
In December 2024, they launched a base design DeepSeek-V3-Base and a chat design DeepSeek-V3. The design architecture is basically the very same as V2. They were trained as follows: [37]
1. Pretraining on 14.8 T tokens of a multilingual corpus, primarily English and Chinese. It consisted of 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, utilizing YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 dates on 1.5 M samples of thinking (math, programming, logic) and non-reasoning (imaginative writing, roleplay, easy concern answering) information. Reasoning data was created by “expert designs”. Non-reasoning data was created by DeepSeek-V2.5 and inspected by people. – The “skilled designs” were trained by beginning with an unspecified base design, then SFT on both information, and artificial data produced by an internal DeepSeek-R1 design. The system prompt asked the R1 to show and confirm throughout thinking. Then the specialist models were RL using an undefined benefit function.
– Each specialist design was trained to generate simply synthetic thinking data in one particular domain (math, shows, reasoning).
– Expert models were used, instead of R1 itself, given that the output from R1 itself suffered “overthinking, bad formatting, and extreme length”.
4. Model-based benefit models were made by beginning with a SFT checkpoint of V3, then finetuning on human choice information containing both last reward and chain-of-thought resulting in the final benefit. The reward model produced benefit signals for both questions with unbiased however free-form responses, and questions without unbiased answers (such as imaginative writing).
5. A SFT checkpoint of V3 was trained by GRPO utilizing both reward models and rule-based benefit. The rule-based reward was calculated for math problems with a last answer (put in a box), and for programming problems by system tests. This produced DeepSeek-V3.
The DeepSeek group carried out extensive low-level engineering to attain performance. 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 standard 32-bit, requiring special GEMM regimens to collect properly. They utilized a custom 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 communication, such as committing 20 streaming multiprocessors out of 132 per H800 for just inter-GPU communication. They lowered communication by rearranging (every 10 minutes) the exact machine each professional was on in order to prevent certain devices being queried regularly than the others, including auxiliary load-balancing losses to the training loss function, and other load-balancing strategies. [37]
After training, it was released on H800 clusters. The H800 cards within a cluster are connected by NVLink, and the clusters are linked by InfiniBand. [37]
Benchmark tests show that DeepSeek-V3 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 became available through DeepSeek’s API, along with through a chat interface after visiting. [42] [43] [note 3] It was trained for logical inference, mathematical thinking, and real-time analytical. DeepSeek claimed that it surpassed performance of OpenAI o1 on standards such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal specified when it used 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 released DeepSeek-R1 and DeepSeek-R1-Zero. [46] Both were initialized from DeepSeek-V3-Base, and share its architecture. The business also released some “DeepSeek-R1-Distill” designs, which are not initialized on V3-Base, but rather are initialized from other pretrained open-weight designs, including LLaMA and Qwen, then fine-tuned on synthetic data generated by R1. [47]
A conversation between User and Assistant. The user asks a question, and the Assistant resolves it. The assistant first considers the reasoning process in the mind and after that supplies the user with the response. The reasoning procedure and response are enclosed within and tags, respectively, i.e., reasoning process here respond to here. User:. Assistant:
DeepSeek-R1-Zero was trained specifically using GRPO RL without SFT. Unlike previous variations, they utilized no model-based benefit. All reward functions were rule-based, “mainly” of 2 types (other types were not defined): accuracy benefits and format benefits. Accuracy benefit was checking whether a boxed response is right (for math) or whether a code passes tests (for programming). Format reward was checking whether the design puts its thinking trace within … [47]
As R1-Zero has concerns with readability and blending languages, R1 was trained to deal with these problems and further improve reasoning: [47]
1. SFT DeepSeek-V3-Base on “thousands” of “cold-start” data all with the standard format of|special_token|| special_token|summary >.
2. Apply the same RL process as R1-Zero, but likewise with a “language consistency benefit” to encourage it to respond monolingually. This produced an internal design not launched.
3. Synthesize 600K thinking information from the internal model, with rejection tasting (i.e. if the created thinking had an incorrect last response, then it is eliminated). Synthesize 200K non-reasoning data (writing, factual QA, self-cognition, translation) using DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K synthetic data 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 models 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 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 as the highest-rated complimentary app on the iOS App Store in the United States; its chatbot reportedly answers questions, fixes logic issues and composes computer system programs on par with other chatbots on the market, according to benchmark tests utilized by American AI companies. [3]
DeepSeek-V3 utilizes considerably fewer resources compared to its peers; for example, whereas the world’s leading AI companies train their chatbots with supercomputers utilizing as lots of as 16,000 graphics processing systems (GPUs), if not more, DeepSeek claims to require just about 2,000 GPUs, specifically the H800 series chip from Nvidia. [37] It was trained in around 55 days at a cost of US$ 5.58 million, [37] which is approximately one tenth of what United States tech huge Meta spent building its newest AI technology. [3]
DeepSeek’s competitive efficiency at reasonably minimal expense has actually been acknowledged as possibly challenging the worldwide 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 supposedly “on par with” among OpenAI’s newest designs when utilized for jobs such as mathematics, coding, and natural language reasoning; [51] echoing other commentators, American Silicon Valley endeavor capitalist Marc Andreessen also described R1 as “AI’s Sputnik minute”. [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 praised DeepSeek as a national property. [53] [54] On 20 January 2025, China’s Premier Li Qiang welcomed Liang Wenfeng to his symposium with professionals and asked him to offer viewpoints and tips on a draft for remarks of the yearly 2024 government work report. [55]
DeepSeek’s optimization of limited resources has highlighted prospective limits of United States sanctions on China’s AI advancement, which consist of export constraints on innovative AI chips to China [18] [56] The success of the business’s AI designs as a result “sparked market turmoil” [57] and triggered shares in significant international innovation business to plunge on 27 January 2025: Nvidia’s stock fell by as much as 17-18%, [58] as did the stock of competing Broadcom. Other tech companies likewise sank, consisting of Microsoft (down 2.5%), Google’s owner Alphabet (down over 4%), and Dutch chip devices maker ASML (down over 7%). [51] An international selloff of technology stocks on Nasdaq, prompted by the release of the R1 design, had actually resulted in record losses of about $593 billion in the market capitalizations of AI and computer system hardware companies; [59] by 28 January 2025, a total of $1 trillion of value was rubbed out American stocks. [50]
Leading figures in the American AI sector had combined reactions to DeepSeek’s success and efficiency. [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 excellent”. [61] [62] American President Donald Trump, who revealed 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 skepticism of the app’s performance or of the sustainability of its success. [60] [66] [67] Various business, consisting of Amazon Web Services, Toyota, and Stripe, are seeking to utilize the model in their program. [68]
On 27 January 2025, DeepSeek restricted its brand-new user registration to contact number from mainland China, email addresses, or Google account logins, following a “massive” cyberattack disrupted the appropriate performance of its servers. [69] [70]
Some sources have actually observed that the main application programs user interface (API) variation 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 respond to questions about the 1989 Tiananmen Square demonstrations and massacre, persecution of Uyghurs, contrasts in between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI may initially create a response, but then erases it soon afterwards and changes it with a message such as: “Sorry, that’s beyond my existing scope. Let’s talk about something else.” [72] The incorporated censorship systems and restrictions can just be removed to a restricted extent in the open-source variation of the R1 design. If the “core socialist values” defined by the Chinese Internet regulative authorities are discussed, or the political status of Taiwan is raised, discussions are terminated. [74] When evaluated by NBC News, DeepSeek’s R1 explained Taiwan as “an inalienable part of China’s territory,” and stated: “We firmly oppose any type of ‘Taiwan self-reliance’ separatist activities and are committed to accomplishing the total reunification of the motherland through serene ways.” [75] In January 2025, Western scientists were able to trick DeepSeek into giving certain answers to a few of these topics by asking for in its answer to swap specific letters for similar-looking numbers. [73]
Security and personal privacy
Some specialists fear that the federal government of China could utilize the AI system for foreign impact operations, spreading out disinformation, monitoring and the advancement of cyberweapons. [76] [77] [78] DeepSeek’s personal privacy terms and conditions say “We store the details we gather in safe servers found in individuals’s Republic of China … We might collect your text or audio input, prompt, uploaded files, feedback, chat history, or other content that you provide to our design and Services”. Although the information storage and collection policy follows ChatGPT’s privacy policy, [79] a Wired short article reports this as security concerns. [80] In response, the Italian data defense authority is looking for additional info on DeepSeek’s collection and use of individual information, and the United States National Security Council announced that it had begun a national security review. [81] [82] Taiwan’s government prohibited using DeepSeek at federal government ministries on security grounds and South Korea’s Personal Information Protection Commission opened a questions into DeepSeek’s usage of individual details. [83]
Expert system market in China.
Notes
^ a b c The number of heads does not equal the variety of KV heads, due to GQA.
^ Inexplicably, the model called DeepSeek-Coder-V2 Chat in the paper was released as DeepSeek-Coder-V2-Instruct in HuggingFace.
^ At that time, the R1-Lite-Preview required choosing “Deep Think allowed”, and every user could utilize it just 50 times a day.
References
^ Gibney, Elizabeth (23 January 2025). “China’s cheap, open AI model DeepSeek thrills scientists”. Nature. doi:10.1038/ d41586-025-00229-6. ISSN 1476-4687. PMID 39849139.
^ a b Vincent, James (28 January 2025). “The DeepSeek panic 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 Taking On Silicon Valley Giants”. The New York City Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Cosgrove, Emma (27 January 2025). “DeepSeek’s less expensive models and weaker chips bring into question trillions in AI facilities spending”. Business Insider.
^ Mallick, Subhrojit (16 January 2024). “Biden admin’s cap on GPU exports might hit India’s AI ambitions”. The Economic Times. Retrieved 29 January 2025.
^ Saran, Cliff (10 December 2024). “Nvidia examination signals broadening of US and China chip war|Computer Weekly”. Computer Weekly. Retrieved 27 January 2025.
^ Sherman, Natalie (9 December 2024). “Nvidia targeted by China in new chip war probe”. BBC. Retrieved 27 January 2025.
^ a b c Metz, Cade (27 January 2025). “What is DeepSeek? And How Is It Upending A.I.?”. The New York 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 triggering Nvidia and other stocks to slump?”. CBS News.
^ Zahn, Max (27 January 2025). “Nvidia, Microsoft shares tumble as China-based AI app DeepSeek hammers tech giants”. ABC News. Retrieved 27 January 2025.
^ Roose, Kevin (28 January 2025). “Why DeepSeek Could Change What Silicon Valley Believe About A.I.” The New York Times. ISSN 0362-4331. Retrieved 28 January 2025.
^ a b Romero, Luis E. (28 January 2025). “ChatGPT, DeepSeek, Or Llama? Meta’s LeCun Says Open-Source Is The Key”. Forbes.
^ Chen, Caiwei (24 January 2025). “How a leading Chinese AI design overcame US sanctions”. MIT Technology Review. Archived from the original on 25 January 2025. Retrieved 25 January 2025.
^ a b c d Ottinger, Lily (9 December 2024). “Deepseek: From Hedge Fund to Frontier Model Maker”. ChinaTalk. Archived from the original on 28 December 2024. Retrieved 28 December 2024.
^ Leswing, Kif (23 February 2023). “Meet the $10,000 Nvidia chip powering the race for A.I.” CNBC. Retrieved 30 January 2025.
^ Yu, Xu (17 April 2023).” [Exclusive] Chinese Quant Hedge Fund High-Flyer Won’t Use AGI to Trade Stocks, MD Says”. Yicai Global. Archived from the original on 31 December 2023. Retrieved 28 December 2024.
^ a b c d e Jiang, Ben; Perezi, Bien (1 January 2025). “Meet DeepSeek: the Chinese start-up that is changing how AI models are trained”. South China Morning Post. Archived from the initial on 22 January 2025. Retrieved 1 January 2025.
^ a b McMorrow, Ryan; Olcott, Eleanor (9 June 2024). “The Chinese quant fund-turned-AI leader”. Financial Times. Archived from the 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 primary · 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 main”. huggingface.co. 26 December 2024. Retrieved 28 January 2025.
^ Jiang, Ben (27 December 2024). “Chinese start-up DeepSeek’s brand-new AI model surpasses 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 initial on 27 December 2024. Retrieved 28 December 2024.
^ Wiggers, Kyle (26 December 2024). “DeepSeek’s brand-new AI model appears to be one of the very best ‘open’ oppositions yet”. TechCrunch. Archived from the initial on 2 January 2025. Retrieved 31 December 2024.
^ “Deepseek Log in page”. DeepSeek. Retrieved 30 January 2025.
^ “News|DeepSeek-R1-Lite Release 2024/11/20: DeepSeek-R1-Lite-Preview is now live: 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 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, 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 initial on 8 December 2024. Retrieved 25 January 2025.
^ a b c Milmo, Dan; Hawkins, Amy; Booth, Robert; Kollewe, Julia (28 January 2025). “‘ Sputnik moment’: $1tn rubbed out US stocks after Chinese firm reveals AI chatbot” – through The Guardian.
^ a b c d Hoskins, Peter; Rahman-Jones, Imran (27 January 2025). “Nvidia shares sink as Chinese AI app spooks markets”. BBC. Retrieved 28 January 2025.
^ Goldman, David (27 January 2025). “What is DeepSeek, the Chinese AI startup that shook the tech world?|CNN Business”. CNN. Retrieved 29 January 2025.
^ “DeepSeek 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 company’s AI design breakthrough highlights limitations of US sanctions”. Tom’s Hardware. Archived from the initial on 28 December 2024. Retrieved 28 December 2024.
^ “DeepSeek updates – Chinese AI chatbot sparks US market turmoil, cleaning $500bn off Nvidia”. BBC News. Retrieved 27 January 2025.
^ Nazareth, Rita (26 January 2025). “Stock Rout Gets Ugly as Nvidia Extends Loss to 17%: Markets Wrap”. Bloomberg. Retrieved 27 January 2025.
^ Carew, Sinéad; Cooper, Amanda; Banerjee, Ankur (27 January 2025). “DeepSeek stimulates international 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 excellent”: “We need to take the developments out of China very, really seriously””. Windows Central. Retrieved 28 January 2025.
^ Nazzaro, Miranda (28 January 2025). “OpenAI’s Sam Altman calls DeepSeek design ‘impressive'”. The Hill. Retrieved 28 January 2025.
^ Dou, Eva; Gregg, Aaron; Zakrzewski, Cat; Tiku, Nitasha; Najmabadi, Shannon (28 January 2025). “Trump calls China’s DeepSeek AI app a ‘wake-up call’ after tech stocks slide”. The Washington Post. Retrieved 28 January 2025.
^ Habeshian, Sareen (28 January 2025). “Johnson bashes China on AI, Trump calls DeepSeek advancement “positive””. Axios.
^ Karaian, Jason; Rennison, Joe (27 January 2025). “China’s A.I. Advances Spook Big Tech Investors on Wall Street” – by means of NYTimes.com.
^ Sharma, Manoj (6 January 2025). “Musk dismisses, Altman applauds: What leaders say on DeepSeek’s disruption”. Fortune India. Retrieved 28 January 2025.
^ “Elon Musk ‘concerns’ DeepSeek’s claims, suggests massive Nvidia GPU facilities”. Financialexpress. 28 January 2025. Retrieved 28 January 2025.
^ Kim, Eugene. “Big AWS customers, consisting of Stripe and Toyota, are hounding the cloud giant for access to DeepSeek AI designs”. Business Insider.
^ Kerr, Dara (27 January 2025). “DeepSeek hit with ‘large-scale’ cyber-attack after AI chatbot tops app shops”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek temporarily limited new sign-ups, mentioning ‘massive harmful attacks'”. Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has actually sparked a $1 trillion panic – and it doesn’t appreciate complimentary speech”. The Daily Telegraph. ISSN 0307-1235. Retrieved 27 January 2025.
^ a b Steinschaden, Jakob (27 January 2025). “DeepSeek: This is what live censorship appears like in the Chinese AI chatbot”. Trending Topics. Retrieved 27 January 2025.
^ a b Lu, Donna (28 January 2025). “We tried DeepSeek. It worked well, up until 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 increase of turmoil”. 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 moment'”. NBC News. Retrieved 27 January 2025.
^ Kimery, Anthony (26 January 2025). “China’s DeepSeek AI poses formidable cyber, data personal privacy threats”. Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). “Experts urge 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 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 data security”. Reuters. 28 January 2025. Retrieved 28 January 2025.
^ Shalal, Andrea; Shepardson, David (28 January 2025). “White House assesses effect of China AI app DeepSeek on national security, authorities states”. Reuters. Retrieved 28 January 2025.