This Stage used 3 Reward Models

DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese synthetic 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 functions as its CEO.

The DeepSeek-R1 model offers reactions equivalent 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 a similar LLM. [2] [3] [4] DeepSeek’s AI designs 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 establish innovative AI systems. [6] [7]

On 10 January 2025, DeepSeek released its first totally free chatbot app, based upon the DeepSeek-R1 design, for iOS and Android; by 27 January, DeepSeek-R1 had actually surpassed ChatGPT as the most-downloaded totally free app on the iOS App Store in the United States, [8] triggering Nvidia’s share cost to stop by 18%. [9] [10] DeepSeek’s success versus larger and more recognized competitors has been referred to as “overthrowing AI”, [8] making up “the first chance at what is emerging as a global AI space race”, [11] and ushering in “a new period of AI brinkmanship”. [12]

DeepSeek makes its generative expert system algorithms, designs, and training details open-source, permitting its code to be freely readily available for use, modification, viewing, and developing files for constructing functions. [13] The business reportedly strongly hires young AI scientists from top Chinese universities, [8] and works with from outside the computer 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 since the 2007-2008 financial crisis while participating in Zhejiang University. [14] By 2019, he established High-Flyer as a hedge fund concentrated on developing and utilizing AI trading algorithms. By 2021, High-Flyer specifically utilized AI in trading. [15] DeepSeek has made its generative synthetic intelligence chatbot open source, indicating its code is freely readily available for usage, modification, and watching. This consists of approval to gain access to and utilize the source code, along with design files, for building purposes. [13]

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

In April 2023, High-Flyer began a synthetic basic intelligence laboratory devoted to research study establishing AI tools separate from High-Flyer’s monetary business. [17] [18] In May 2023, with High-Flyer as one of the financiers, the laboratory became its own company, DeepSeek. [15] [19] [18] Venture capital companies were reluctant in offering funding as it was unlikely that it would be able to generate an exit in a brief time period. [15]

After launching DeepSeek-V2 in May 2024, which used strong performance for a low cost, DeepSeek ended up being called the driver for China’s AI model price war. It was quickly dubbed the “Pinduoduo of AI”, and other significant tech giants such as ByteDance, Tencent, Baidu, and Alibaba began to cut the rate of their AI models to complete with the company. Despite the low rate charged by DeepSeek, it was successful compared to its competitors that were losing money. [20]

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

DeepSeek’s working with choices target technical abilities instead of work experience, resulting in a lot of new hires being either recent university graduates or developers whose AI careers are less established. [18] [3] Likewise, the company hires individuals without any computer technology background to help its innovation comprehend other subjects and knowledge areas, including having the ability to generate poetry and perform well on the notoriously tough Chinese college admissions exams (Gaokao). [3]

Development and release history

DeepSeek LLM

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

They are of the very same architecture as DeepSeek LLM detailed listed 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 models.
3. Supervised finetuning (SFT): 2B tokens of direction information. This produced the Instruct models.

They were trained on clusters of A100 and H800 Nvidia GPUs, connected by InfiniBand, NVLink, NVSwitch. [22]

On 29 November 2023, DeepSeek launched the DeepSeek-LLM series of models, with 7B and 67B parameters in both Base and Chat forms (no Instruct was released). It was established to complete with other LLMs offered at the time. The paper declared benchmark outcomes higher than most 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 design itself. [27]

The architecture was essentially the exact same as those of the Llama series. They used the pre-norm decoder-only Transformer with RMSNorm as the normalization, SwiGLU in the feedforward layers, rotary positional embedding (RoPE), and grouped-query attention (GQA). Both had vocabulary size 102,400 (byte-level BPE) and context length of 4096. They trained on 2 trillion tokens of English and Chinese text obtained by deduplicating the Common Crawl. [26]

The Chat variations of the two Base models was likewise launched concurrently, acquired 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 parameters (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 declared 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 professionals” that are constantly queried, and “routed specialists” that might not be. They discovered this to help with professional balancing. In basic MoE, some professionals can become extremely depended on, while other professionals might be seldom used, squandering parameters. Attempting to stabilize the professionals so that they are similarly utilized then triggers experts to replicate the very same capacity. They proposed the shared specialists to learn core capacities that are frequently used, and let the routed experts to discover the peripheral capacities that are hardly ever 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 mathematics issues and their tool-use-integrated step-by-step services. This produced the Instruct model.
Reinforcement learning (RL): The reward model was a process benefit model (PRM) trained from Base according to the Math-Shepherd method. [30] This reward design was then used to train Instruct utilizing group relative policy optimization (GRPO) on a dataset of 144K math questions “associated to GSM8K and MATH”. The reward design was constantly upgraded throughout training to avoid benefit hacking. This resulted in the RL design.

V2

In May 2024, they launched the DeepSeek-V2 series. The series consists of 4 designs, 2 base models (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (-Chat). The 2 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 resulted in DeepSeek-V2.
3. SFT with 1.2 M circumstances for helpfulness and 0.3 M for safety. This resulted in DeepSeek-V2-Chat (SFT) which was not released.
4. RL utilizing GRPO in 2 phases. The very first phase was trained to solve math and coding problems. 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 helpful, safe, and follow rules. This stage used 3 reward designs. The helpfulness and security benefit models were trained on human choice information. The rule-based benefit design was by hand configured. All trained reward models were initialized from DeepSeek-V2-Chat (SFT). This led to the released version of DeepSeek-V2-Chat.

They decided for 2-staged RL, due to the fact that they discovered that RL on reasoning information had “distinct attributes” different from RL on basic information. For instance, RL on reasoning could enhance over more training actions. [31]

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

Architecturally, the V2 models were considerably modified from the DeepSeek LLM series. They changed the standard attention system by a low-rank approximation called multi-head latent attention (MLA), and utilized the mixture of experts (MoE) alternative formerly released in January. [28]

The Financial Times reported that it was more affordable than its peers with a cost of 2 RMB for each million output tokens. The University of Waterloo Tiger Lab’s leaderboard ranked DeepSeek-V2 seventh on its LLM ranking. [19]

In June 2024, they launched 4 designs in the DeepSeek-Coder-V2 series: V2-Base, V2-Lite-Base, V2-Instruct, V2-Lite-Instruct. They were trained as follows: [35] [note 2]

1. The Base designs were initialized from corresponding intermediate checkpoints after pretraining on 4.2 T tokens (not the version at the end of pretraining), then pretrained even more for 6T tokens, then context-extended to 128K context length. This produced the Base designs.
DeepSeek-Coder and DeepSeek-Math were utilized to generate 20K code-related and 30K math-related direction data, then combined with a direction dataset of 300M tokens. This was utilized for SFT.
2. RL with GRPO. The benefit for mathematics issues was calculated by comparing with the ground-truth label. The benefit for code problems was generated by a reward model trained to predict 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 launched a base model DeepSeek-V3-Base and a chat design DeepSeek-V3. The model architecture is basically the like V2. They were trained as follows: [37]

1. Pretraining on 14.8 T tokens of a multilingual corpus, primarily English and Chinese. It contained a greater ratio of math and programs than the pretraining dataset of V2.
2. Extend context length two times, from 4K to 32K and after that to 128K, utilizing 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, basic concern answering) data. Reasoning information was generated by “professional models”. Non-reasoning information was created by DeepSeek-V2.5 and examined by humans. – The “professional models” were trained by beginning with an undefined base model, then SFT on both data, and synthetic data produced by an internal DeepSeek-R1 model. The system timely asked the R1 to reflect and validate during thinking. Then the professional models were RL utilizing an undefined benefit function.
– Each specialist design was trained to create just synthetic thinking 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 format, and excessive length”.

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

The DeepSeek group carried out extensive low-level engineering to accomplish efficiency. They used mixed-precision math. Much of the forward pass was carried out in 8-bit floating point numbers (5E2M: 5-bit and 2-bit mantissa) rather than the standard 32-bit, needing unique GEMM regimens to accumulate accurately. They used a custom-made 12-bit float (E5M6) for only the inputs to the direct layers after the attention modules. Optimizer states remained in 16-bit (BF16). They minimized the communication latency by overlapping extensively calculation and communication, such as dedicating 20 streaming multiprocessors out of 132 per H800 for only inter-GPU interaction. They lowered interaction by rearranging (every 10 minutes) the exact device each expert was on in order to avoid certain machines being queried more frequently than the others, adding auxiliary load-balancing losses to the training loss function, and other load-balancing methods. [37]

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

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

R1

On 20 November 2024, DeepSeek-R1-Lite-Preview ended up being accessible via DeepSeek’s API, in addition to via a chat user interface after visiting. [42] [43] [note 3] It was trained for logical reasoning, mathematical thinking, and real-time analytical. DeepSeek declared that it exceeded efficiency of OpenAI o1 on criteria such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal mentioned when it used 15 issues from the 2024 edition of AIME, the o1 design reached a solution faster than DeepSeek-R1-Lite-Preview. [45]

On 20 January 2025, DeepSeek launched DeepSeek-R1 and DeepSeek-R1-Zero. [46] Both were initialized from DeepSeek-V3-Base, and share its architecture. The company also launched some “DeepSeek-R1-Distill” models, which are not initialized on V3-Base, but instead are initialized from other pretrained open-weight designs, consisting of LLaMA and Qwen, then fine-tuned on synthetic data generated by R1. [47]

A discussion between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks of the thinking process in the mind and after that provides the user with the answer. The reasoning procedure and response 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 utilized no model-based reward. All reward functions were rule-based, “generally” of 2 types (other types were not defined): accuracy benefits and format rewards. Accuracy benefit was examining whether a boxed answer is right (for math) or whether a code passes tests (for programming). Format benefit was inspecting whether the design puts its thinking trace within … [47]

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

1. SFT DeepSeek-V3-Base on “thousands” of “cold-start” data all with the standard format of|special_token|| special_token|summary >.
2. Apply the very same RL procedure as R1-Zero, however likewise with a “language consistency benefit” to encourage it to respond monolingually. This produced an internal design not launched.
3. Synthesize 600K reasoning data from the internal design, with rejection sampling (i.e. if the created thinking had an incorrect final response, then it is removed). Synthesize 200K non-reasoning data (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 reasoning tasks) and model-based benefit (for non-reasoning tasks, helpfulness, and harmlessness). This produced DeepSeek-R1.

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

Assessment and responses

DeepSeek launched its AI Assistant, which uses the V3 model as a chatbot app for Apple IOS and Android. By 27 January 2025 the app had gone beyond ChatGPT as the highest-rated complimentary app on the iOS App Store in the United States; its chatbot apparently addresses questions, resolves reasoning issues and composes computer system programs on par with other chatbots on the market, according to benchmark tests utilized by American AI business. [3]

DeepSeek-V3 uses substantially fewer resources compared to its peers; for instance, whereas the world’s leading AI business train their chatbots with supercomputers using as lots of as 16,000 graphics processing units (GPUs), if not more, DeepSeek declares to require 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 approximately one tenth of what United States tech giant Meta spent constructing its newest AI technology. [3]

DeepSeek’s competitive performance at fairly minimal cost has been recognized as potentially challenging the global dominance of American AI designs. [48] Various publications and news media, such as The Hill and The Guardian, explained the release of its chatbot as a “Sputnik moment” for American AI. [49] [50] The efficiency of its R1 design was reportedly “on par with” among OpenAI’s most current designs when used for tasks such as mathematics, coding, and natural language reasoning; [51] echoing other analysts, American Silicon Valley investor Marc Andreessen similarly explained 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 nationwide possession. [53] [54] On 20 January 2025, China’s Premier Li Qiang welcomed Liang Wenfeng to his symposium with professionals and asked him to supply opinions and ideas on a draft for comments of the annual 2024 federal government work report. [55]

DeepSeek’s optimization of restricted resources has highlighted possible limitations of United States sanctions on China’s AI advancement, which include export constraints on sophisticated AI chips to China [18] [56] The success of the business’s AI designs as a result “triggered market turmoil” [57] and triggered shares in major worldwide innovation companies to plunge on 27 January 2025: Nvidia’s stock fell by as much as 17-18%, [58] as did the stock of rival Broadcom. Other tech companies also sank, including Microsoft (down 2.5%), Google’s owner Alphabet (down over 4%), and Dutch chip equipment maker ASML (down over 7%). [51] A global selloff of innovation stocks on Nasdaq, triggered by the release of the R1 model, had actually led to tape losses of about $593 billion in the market capitalizations of AI and hardware companies; [59] by 28 January 2025, a total of $1 trillion of value was wiped off American stocks. [50]

Leading figures in the American AI sector had blended reactions to DeepSeek’s success and efficiency. [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 “super 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, including 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 seeking to use the model in their program. [68]

On 27 January 2025, DeepSeek limited its brand-new user registration to contact number from mainland China, e-mail addresses, or Google account logins, following a “massive” cyberattack interfered with the appropriate functioning of its servers. [69] [70]

Some sources have actually observed that the main application shows interface (API) version of R1, which runs from servers found in China, uses censorship systems for topics that are thought about politically delicate for the government of China. For example, the model declines to respond to concerns about the 1989 Tiananmen Square demonstrations and massacre, persecution of Uyghurs, comparisons between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI might at first generate an answer, but then deletes it shortly 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 restrictions can just be eliminated to a minimal level in the open-source variation of the R1 design. 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 terminated. [74] When checked by NBC News, DeepSeek’s R1 described Taiwan as “an inalienable part of China’s territory,” and mentioned: “We firmly oppose any form of ‘Taiwan independence’ separatist activities and are dedicated to achieving the complete reunification of the motherland through tranquil means.” [75] In January 2025, Western researchers had the ability to trick DeepSeek into providing specific responses to a few of these subjects by requesting in its response to swap certain 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 disinformation, security and the advancement of cyberweapons. [76] [77] [78] DeepSeek’s personal privacy terms state “We save the details we gather in secure servers located in the People’s Republic of China … We may gather your text or audio input, prompt, uploaded files, feedback, chat history, or other material that you offer to our model and Services”. Although the data storage and collection policy follows ChatGPT’s personal privacy policy, [79] a Wired article reports this as security concerns. [80] In response, the Italian information security authority is looking for additional info on DeepSeek’s collection and usage of personal data, and the United States National Security Council announced that it had begun a nationwide security evaluation. [81] [82] Taiwan’s government banned making use of DeepSeek at federal government ministries on security premises and South Korea’s Personal Information Protection Commission opened an inquiry into DeepSeek’s use of personal info. [83]

Artificial intelligence market in China.

Notes

^ a b c The number of heads does not equal the variety of KV heads, due to GQA.
^ Inexplicably, the design 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 selecting “Deep Think allowed”, and every user could utilize it only 50 times a day.
References

^ Gibney, Elizabeth (23 January 2025). “China’s inexpensive, open AI model 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 prepared 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 cast doubt on 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 investigation signals broadening of US and China chip war|Computer Weekly”. Computer Weekly. Retrieved 27 January 2025.
^ Sherman, Natalie (9 December 2024). “Nvidia targeted by China in brand-new chip war probe”. BBC. Retrieved 27 January 2025.
^ a b c Metz, Cade (27 January 2025). “What is DeepSeek? And How Is It Upending A.I.?”. The New York Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Field, Hayden (27 January 2025). “China’s DeepSeek AI dismisses ChatGPT on App Store: Here’s what you need to know”. CNBC.
^ Picchi, Aimee (27 January 2025). “What is DeepSeek, and why is it triggering Nvidia and other stocks to slump?”. CBS News.
^ Zahn, Max (27 January 2025). “Nvidia, Microsoft shares 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 top Chinese AI design conquered 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 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 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 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 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, 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 brand-new AI design 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 original on 27 December 2024. Retrieved 28 December 2024.
^ Wiggers, Kyle (26 December 2024). “DeepSeek’s brand-new AI model appears to be among the best ‘open’ challengers 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 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 efficiency”. 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 initial on 27 December 2024. Retrieved 28 December 2024.
^ “Release DeepSeek-R1 · deepseek-ai/DeepSeek-R1@23807ce”. GitHub. Archived from the original on 21 January 2025. Retrieved 21 January 2025.
^ a b c d DeepSeek-AI; Guo, Daya; Yang, Dejian; Zhang, Haowei; Song, Junxiao; Zhang, Ruoyu; Xu, Runxin; Zhu, Qihao; Ma, Shirong (22 January 2025), DeepSeek-R1: Incentivizing Reasoning Capability in LLMs by means of 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 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 presents 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 states”. Reuters. Retrieved 30 January 2025.
^ 澎湃新闻 (22 January 2025). “量化巨头幻方创始人梁文锋参加总理座谈会并发言 ， 他还创办了” AI界拼多多””. finance.sina.com.cn. Retrieved 31 January 2025.
^ Shilov, Anton (27 December 2024). “Chinese AI company’s AI design advancement 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 stimulates US market chaos, wiping $500bn off Nvidia”. BBC News. Retrieved 27 January 2025.
^ Nazareth, Rita (26 January 2025). “Stock Rout Gets Ugly as Nvidia Extends Loss to 17%: Markets Wrap”. Bloomberg. Retrieved 27 January 2025.
^ Carew, Sinéad; Cooper, Amanda; Banerjee, Ankur (27 January 2025). “DeepSeek sparks worldwide 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 touts DeepSeek’s open-source AI as “super outstanding”: “We must 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 ‘outstanding'”. The Hill. Retrieved 28 January 2025.
^ Dou, Eva; Gregg, Aaron; Zakrzewski, Cat; Tiku, Nitasha; Najmabadi, Shannon (28 January 2025). “Trump calls China’s DeepSeek AI app a ‘wake-up call’ after tech stocks slide”. The Washington Post. Retrieved 28 January 2025.
^ Habeshian, Sareen (28 January 2025). “Johnson slams China on AI, Trump calls DeepSeek advancement “favorable””. Axios.
^ Karaian, Jason; Rennison, Joe (27 January 2025). “China’s A.I. Advances Spook Big Tech Investors on Wall Street” – via 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, recommends huge Nvidia GPU infrastructure”. Financialexpress. 28 January 2025. Retrieved 28 January 2025.
^ Kim, Eugene. “Big AWS customers, including Stripe and Toyota, are pestering the cloud giant for access to DeepSeek AI models”. Business Insider.
^ Kerr, Dara (27 January 2025). “DeepSeek hit with ‘large-scale’ cyber-attack after AI chatbot tops app stores”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek briefly limited brand-new sign-ups, mentioning ‘massive destructive attacks'”. Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has triggered a $1 trillion panic – and it doesn’t appreciate complimentary speech”. The Daily Telegraph. ISSN 0307-1235. Retrieved 27 January 2025.
^ a b Steinschaden, Jakob (27 January 2025). “DeepSeek: This is what live censorship looks 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, until we asked it about Tiananmen Square and Taiwan”. The Guardian. ISSN 0261-3077. Retrieved 30 January 2025.
^ “The Guardian view on an international AI race: geopolitics, development and the rise of 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, giving the AI race its ‘Sputnik minute'”. NBC News. Retrieved 27 January 2025.
^ Kimery, Anthony (26 January 2025). “China’s DeepSeek AI poses formidable cyber, information personal privacy threats”. Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). “Experts urge care over usage of Chinese AI DeepSeek”. The Guardian. ISSN 0261-3077. Retrieved 28 January 2025.
^ Hornby, Rael (28 January 2025). “DeepSeek’s success has 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 inquires from DeepSeek on information security”. Reuters. 28 January 2025. Retrieved 28 January 2025.
^ Shalal, Andrea; Shepardson, David (28 January 2025). “White House examines effect of China AI app DeepSeek on nationwide security, official states”. Reuters. Retrieved 28 January 2025.