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 big language designs (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 works as its CEO.

The DeepSeek-R1 design provides actions similar to other modern big 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 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 ability of these 2 nations to develop advanced AI systems. [6] [7]

On 10 January 2025, DeepSeek launched its first totally free chatbot app, based on the DeepSeek-R1 model, for iOS and Android; by 27 January, DeepSeek-R1 had surpassed ChatGPT as the most-downloaded totally free app on the iOS App Store in the United States, [8] triggering Nvidia’s share price to come by 18%. [9] [10] DeepSeek’s success versus bigger and more established competitors has been described as “upending AI“, [8] making up “the first chance at what is emerging as a global AI area race”, [11] and ushering in “a brand-new period of AI brinkmanship”. [12]

DeepSeek makes its generative artificial intelligence algorithms, designs, and training information open-source, permitting its code to be freely readily available for use, adjustment, viewing, and creating files for developing functions. [13] The business reportedly vigorously hires young AI researchers from top Chinese universities, [8] and hires from outside the computer technology field to diversify its models’ understanding and abilities. [3]

In February 2016, High-Flyer was co-founded by AI lover Liang Wenfeng, who had been trading given that the 2007-2008 monetary crisis while going to Zhejiang University. [14] By 2019, he established High-Flyer as a hedge fund concentrated on developing and using AI trading algorithms. By 2021, High-Flyer solely utilized AI in trading. [15] DeepSeek has made its generative expert system chatbot open source, meaning its code is easily available for usage, adjustment, and viewing. This includes approval to access and use the source code, as well as style files, for constructing purposes. [13]

According to 36Kr, Liang had actually developed a shop 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 dedicated to research developing AI tools different from High-Flyer’s financial service. [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 companies were unwilling in offering financing as it was not likely that it would have the ability to generate an exit in a short duration of time. [15]

After launching DeepSeek-V2 in May 2024, which provided strong efficiency for a low rate, DeepSeek ended up being referred to as the driver for China’s AI model cost war. It was rapidly dubbed the “Pinduoduo of AI“, and other major tech giants such as ByteDance, Tencent, Baidu, and Alibaba started to cut the cost of their AI models to take on the business. Despite the low cost charged by DeepSeek, it was lucrative compared to its competitors that were losing cash. [20]

DeepSeek is focused on research study and has no detailed plans for commercialization; [20] this likewise allows its innovation to prevent the most stringent arrangements of China’s AI policies, such as needing consumer-facing innovation to abide by the government’s controls on information. [3]

DeepSeek’s working with preferences target technical capabilities instead of work experience, resulting in a lot of brand-new hires being either current university graduates or developers whose AI professions are less developed. [18] [3] Likewise, the company hires individuals without any computer technology background to help its innovation understand other subjects and knowledge locations, including being able to generate poetry and carry out well on the notoriously tough Chinese college admissions examinations (Gaokao). [3]

Development and release history

DeepSeek LLM

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

They are of the very same architecture as DeepSeek LLM detailed below. The series includes 8 models, 4 pretrained (Base) and 4 instruction-finetuned (Instruct). They all have 16K context lengths. The training was as follows: [22] [23] [24]

1. Pretraining: 1.8 T tokens (87% source code, 10% code-related English (GitHub markdown and Stack Exchange), and 3% code-unrelated Chinese).
2. Long-context pretraining: 200B tokens. This extends the context length from 4K to 16K. This produced the Base models.
3. Supervised finetuning (SFT): 2B tokens of instruction 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 released the DeepSeek-LLM series of designs, with 7B and 67B criteria in both Base and Chat types (no Instruct was launched). It was established to contend with other LLMs available at the time. The paper claimed benchmark outcomes greater than a lot of open source LLMs at the time, specifically Llama 2. [26]: area 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 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 gotten by deduplicating the Common Crawl. [26]

The Chat versions of the two Base designs was also launched concurrently, obtained by training Base by monitored finetuning (SFT) followed by direct policy optimization (DPO). [26]

On 9 January 2024, they launched 2 DeepSeek-MoE designs (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 declared comparable efficiency with a 16B MoE as a 7B non-MoE. In architecture, it is a version of the basic sparsely-gated MoE, with “shared experts” that are always queried, and “routed professionals” that might not be. They discovered this to help with expert balancing. In standard MoE, some specialists can end up being excessively depended on, while other professionals may be hardly ever used, wasting parameters. Attempting to balance the specialists so that they are similarly used then triggers experts to replicate the very same capacity. They proposed the shared experts to find out core capabilities that are often utilized, and let the routed experts to discover the peripheral capabilities that are hardly ever utilized. [28]

In April 2024, they released 3 DeepSeek-Math models specialized for doing math: Base, Instruct, RL. It was trained as follows: [29]

1. Initialize with a formerly pretrained DeepSeek-Coder-Base-v1.5 7B.
2. Further pretrain with 500B tokens (6% DeepSeekMath Corpus, 4% AlgebraicStack, 10% arXiv, 20% GitHub code, 10% Common Crawl). This produced the Base model.
3. Train an instruction-following design by SFT Base with 776K math problems and their tool-use-integrated step-by-step services. This produced the Instruct model.
Reinforcement learning (RL): The benefit design was a procedure benefit model (PRM) trained from Base according to the Math-Shepherd approach. [30] This reward model was then utilized to train Instruct using group relative policy optimization (GRPO) on a dataset of 144K math concerns “related to GSM8K and MATH”. The reward design was constantly updated during training to prevent reward hacking. This led to 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 two bigger models were trained as follows: [31]

1. Pretrain on a dataset of 8.1 T tokens, where Chinese tokens are 12% more than English ones.
2. Extend context length from 4K to 128K using YaRN. [32] This resulted in DeepSeek-V2.
3. SFT with 1.2 M instances for helpfulness and 0.3 M for safety. This resulted in DeepSeek-V2-Chat (SFT) which was not launched.
4. RL utilizing GRPO in two stages. The very first stage was trained to resolve mathematics and coding problems. This phase utilized 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 guidelines. This stage used 3 benefit designs. The helpfulness and security reward models were trained on human preference information. The rule-based reward design was manually programmed. All qualified benefit models were initialized from DeepSeek-V2-Chat (SFT). This resulted in the launched version of DeepSeek-V2-Chat.

They selected 2-staged RL, since they found that RL on thinking data had “unique characteristics” various from RL on basic information. For instance, RL on reasoning might enhance over more training steps. [31]

The 2 V2-Lite models were smaller, and experienced similarly, though DeepSeek-V2-Lite-Chat only underwent SFT, not RL. They trained the Lite variation to help “additional research and advancement on MLA and DeepSeekMoE”. [31]

Architecturally, the V2 models were substantially 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 mix of experts (MoE) variant previously published 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 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 create 20K code-related and 30K math-related direction data, then combined with a guideline dataset of 300M tokens. This was utilized for SFT.
2. RL with GRPO. The benefit for mathematics issues was calculated by comparing to the ground-truth label. The benefit for code issues was produced 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 combining DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. [36]

V3

In December 2024, they launched a base model DeepSeek-V3-Base and a chat model DeepSeek-V3. The design architecture is essentially the same as V2. They were trained as follows: [37]

1. Pretraining on 14.8 T tokens of a multilingual corpus, mostly English and Chinese. It included a greater ratio of mathematics and shows 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 dates on 1.5 M samples of reasoning (mathematics, shows, reasoning) and non-reasoning (creative writing, roleplay, basic question answering) information. Reasoning information was produced by “professional designs”. Non-reasoning information was generated by DeepSeek-V2.5 and examined by human beings. – The “professional designs” were trained by starting with an undefined base model, then SFT on both data, and synthetic data created by an internal DeepSeek-R1 design. The system prompt asked the R1 to reflect and confirm throughout thinking. Then the professional models were RL using an undefined reward function.
– Each expert design was trained to create just artificial thinking information in one specific domain (math, programs, reasoning).
– Expert models were utilized, instead of R1 itself, considering that the output from R1 itself suffered “overthinking, poor formatting, and extreme length”.

4. Model-based benefit designs were made by starting with a SFT checkpoint of V3, then finetuning on human preference information containing both final benefit and chain-of-thought resulting in the final benefit. The reward design produced benefit signals for both questions with unbiased 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 reward models and rule-based benefit. The rule-based reward was computed for mathematics issues with a final answer (put in a box), and for programming problems by system tests. This produced DeepSeek-V3.

The DeepSeek team performed extensive low-level engineering to achieve 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 basic 32-bit, requiring special GEMM routines to accumulate accurately. They used a customized 12-bit float (E5M6) for only the inputs to the direct layers after the attention modules. Optimizer states in 16-bit (BF16). They lessened the interaction latency by overlapping extensively computation and interaction, such as committing 20 streaming multiprocessors out of 132 per H800 for only inter-GPU communication. They lowered interaction by rearranging (every 10 minutes) the precise maker each professional was on in order to avoid certain devices being queried regularly than the others, including auxiliary load-balancing losses to the training loss function, and other load-balancing techniques. [37]

After training, it was released on H800 clusters. The H800 cards within a cluster are linked by NVLink, and the clusters are linked 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 became available via DeepSeek’s API, in addition to by means of 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 went beyond performance of OpenAI o1 on standards such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal mentioned when it utilized 15 issues from the 2024 edition of AIME, the o1 model reached a solution 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 launched 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 synthetic information generated by R1. [47]

A discussion in between User and Assistant. The user asks a concern, and the Assistant resolves it. The assistant initially considers the thinking process in the mind and after that supplies the user with the response. The reasoning process and response are confined within and tags, respectively, i.e., thinking process here address here. User:. Assistant:

DeepSeek-R1-Zero was trained exclusively using GRPO RL without SFT. Unlike previous variations, they used no model-based benefit. All reward functions were rule-based, “mainly” of two types (other types were not defined): precision benefits and format benefits. Accuracy reward was inspecting whether a boxed response is right (for mathematics) or whether a code passes tests (for programs). Format benefit was inspecting whether the design puts its thinking trace within … [47]

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

1. SFT DeepSeek-V3-Base on “thousands” of “cold-start” information all with the standard format of|special_token|| special_token|summary >.
2. Apply the exact same RL procedure as R1-Zero, but likewise with a “language consistency reward” to encourage it to respond monolingually. This produced an internal model not launched.
3. Synthesize 600K thinking data from the internal design, with rejection sampling (i.e. if the generated thinking had a wrong last response, then it is removed). Synthesize 200K non-reasoning information (writing, accurate QA, self-cognition, translation) using DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K artificial information for 2 epochs.
5. GRPO RL with rule-based reward (for thinking jobs) and model-based benefit (for non-reasoning jobs, helpfulness, and harmlessness). This produced DeepSeek-R1.

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

Assessment and reactions

DeepSeek released its AI Assistant, which utilizes the V3 model as a chatbot app for Apple IOS and Android. By 27 January 2025 the app had actually gone beyond ChatGPT as the highest-rated complimentary app on the iOS App Store in the United States; its chatbot reportedly answers questions, solves logic problems 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 uses substantially fewer resources compared to its peers; for instance, whereas the world’s leading AI companies train their chatbots with supercomputers utilizing as numerous as 16,000 graphics processing systems (GPUs), if not more, DeepSeek declares to require only about 2,000 GPUs, particularly 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 invested building its latest AI technology. [3]

DeepSeek’s competitive performance at relatively very little expense has actually been acknowledged as possibly challenging the international supremacy 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 minute” for American AI. [49] [50] The efficiency of its R1 model was supposedly “on par with” one of OpenAI’s latest designs 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 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 commonly praised DeepSeek as a nationwide property. [53] [54] On 20 January 2025, China’s Premier Li Qiang welcomed Liang Wenfeng to his symposium with specialists and asked him to provide opinions and ideas on a draft for remarks of the yearly 2024 government work report. [55]

DeepSeek’s optimization of limited resources has highlighted potential limits of United States sanctions on China’s AI advancement, that include export constraints on advanced AI chips to China [18] [56] The success of the business’s AI designs subsequently “triggered market turmoil” [57] and triggered shares in major worldwide innovation business 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 firms likewise sank, including Microsoft (down 2.5%), Google’s owner Alphabet (down over 4%), and Dutch chip devices maker ASML (down over 7%). [51] A global selloff of innovation stocks on Nasdaq, triggered 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 hardware business; [59] by 28 January 2025, a total of $1 trillion of worth was rubbed out American stocks. [50]

Leading figures in the American AI sector had mixed reactions to DeepSeek’s success and performance. [60] Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman-whose companies are associated with the United States government-backed “Stargate Project” to develop American AI infrastructure-both called DeepSeek “incredibly remarkable”. [61] [62] American President Donald Trump, who announced The Stargate Project, called DeepSeek a wake-up call [63] and a positive advancement. [64] [50] [51] [65] Other leaders in the field, including Scale AI CEO Alexandr Wang, Anthropic cofounder and CEO Dario Amodei, and Elon Musk expressed uncertainty 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 telephone number from mainland China, e-mail addresses, or Google account logins, following a “massive” cyberattack disrupted the appropriate functioning of its servers. [69] [70]

Some sources have actually observed that the main application shows interface (API) version of R1, which ranges from servers located in China, uses censorship systems for subjects that are thought about politically sensitive for the federal government of China. For example, the model declines to answer concerns about the 1989 Tiananmen Square protests and massacre, persecution of Uyghurs, contrasts between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI may at first create an answer, but then deletes it soon later on and replaces it with a message such as: “Sorry, that’s beyond my present scope. Let’s talk about something else.” [72] The incorporated censorship systems and constraints can only be eliminated to a restricted degree in the open-source variation of the R1 design. If the “core socialist worths” defined by the Chinese Internet regulatory authorities are discussed, or the political status of Taiwan is raised, conversations are ended. [74] When evaluated by NBC News, DeepSeek’s R1 described Taiwan as “an inalienable part of China’s territory,” and mentioned: “We securely oppose any type of ‘Taiwan independence’ separatist activities and are dedicated to attaining the complete reunification of the motherland through peaceful methods.” [75] In January 2025, Western scientists had the ability to fool DeepSeek into providing particular answers to some of these topics by requesting in its response to switch certain letters for similar-looking numbers. [73]

Security and privacy

Some specialists fear that the 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 terms and conditions say “We save the info we collect in safe servers located in the People’s Republic of China … We may collect your text or audio input, prompt, uploaded files, feedback, chat history, or other material that you provide to our design and Services”. Although the data 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 security authority is looking for additional information on DeepSeek’s collection and use of individual information, and the United States National Security Council announced that it had started a nationwide security review. [81] [82] Taiwan’s federal government prohibited using DeepSeek at federal government ministries on security grounds and South Korea’s Personal Information Protection Commission opened a query into DeepSeek’s use of personal information. [83]

Artificial intelligence market in China.

Notes

^ a b c The variety of heads does not equivalent the variety of KV heads, due to GQA.
^ Inexplicably, the design called DeepSeek-Coder-V2 Chat in the paper was released as DeepSeek-Coder-V2-Instruct in HuggingFace.
^ At that time, the R1-Lite-Preview required selecting “Deep Think allowed”, and every user might use it only 50 times a day.
References

^ Gibney, Elizabeth (23 January 2025). “China’s inexpensive, open AI design DeepSeek thrills researchers”. Nature. doi:10.1038/ d41586-025-00229-6. ISSN 1476-4687. PMID 39849139.
^ a b Vincent, James (28 January 2025). “The DeepSeek panic exposes 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 Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Cosgrove, Emma (27 January 2025). “DeepSeek’s cheaper models and weaker chips call into question trillions in AI infrastructure costs”. Business Insider.
^ Mallick, Subhrojit (16 January 2024). “Biden admin’s cap on GPU exports might hit India’s AI ambitions”. The Economic Times. Retrieved 29 January 2025.
^ Saran, Cliff (10 December 2024). “Nvidia investigation signals broadening of US and China chip war|Computer Weekly”. Computer Weekly. Retrieved 27 January 2025.
^ Sherman, Natalie (9 December 2024). “Nvidia targeted by China in brand-new chip war probe”. BBC. Retrieved 27 January 2025.
^ a b c Metz, Cade (27 January 2025). “What is DeepSeek? And How Is It Upending A.I.?”. The New York 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 understand”. CNBC.
^ Picchi, Aimee (27 January 2025). “What is DeepSeek, and why is it triggering Nvidia and other stocks to drop?”. 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 got rid of 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 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 initial 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 altering 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 original on 17 July 2024. Retrieved 28 December 2024.
^ a b Schneider, Jordan (27 November 2024). “Deepseek: The Quiet Giant Leading China’s AI Race”. ChinaTalk. Retrieved 28 December 2024.
^ “DeepSeek-Coder/LICENSE-MODEL at main · deepseek-ai/DeepSeek-Coder”. GitHub. Archived from the initial 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 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 design exceeds Meta, OpenAI items”. South China Morning Post. Archived from the initial on 27 December 2024. Retrieved 28 December 2024.
^ Sharma, Shubham (26 December 2024). “DeepSeek-V3, ultra-large open-source AI, surpasses 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 seems one of the best ‘open’ oppositions yet”. TechCrunch. Archived from the initial on 2 January 2025. Retrieved 31 December 2024.
^ “Deepseek Log in page”. DeepSeek. Retrieved 30 January 2025.
^ “News|DeepSeek-R1-Lite Release 2024/11/20: DeepSeek-R1-Lite-Preview is now live: 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 very first reasoning model R1-Lite-Preview turns heads, beating OpenAI o1 efficiency”. VentureBeat. Archived from the original on 22 November 2024. Retrieved 28 December 2024.
^ Huang, Raffaele (24 December 2024). “Don’t Look Now, but China’s AI Is Catching Up Fast”. The Wall Street Journal. Archived from the 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 through Reinforcement Learning, arXiv:2501.12948.
^ “Chinese AI start-up 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 moment”. The Hill. Archived from the original on 8 December 2024. Retrieved 25 January 2025.
^ a b c Milmo, Dan; Hawkins, Amy; Booth, Robert; Kollewe, Julia (28 January 2025). “‘ Sputnik minute’: $1tn wiped off US stocks after Chinese company unveils 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 start-up that shook the tech world?|CNN Business”. CNN. Retrieved 29 January 2025.
^ “DeepSeek positions a challenge 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 design development highlights limitations 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 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 sparks worldwide 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 “super outstanding”: “We need to take the advancements out of China extremely, extremely 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 development “positive””. 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 disturbance”. Fortune India. Retrieved 28 January 2025.
^ “Elon Musk ‘concerns’ 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 hounding the cloud giant for access to DeepSeek AI models”. Business Insider.
^ Kerr, Dara (27 January 2025). “DeepSeek struck with ‘massive’ cyber-attack after AI chatbot tops app shops”. The Guardian. Retrieved 28 January 2025.
^ Tweedie, Steven; Altchek, Ana. “DeepSeek briefly restricted brand-new sign-ups, pointing out ‘large-scale harmful attacks'”. Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has actually 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 appears like in the Chinese AI chatbot”. Trending Topics. Retrieved 27 January 2025.
^ a b Lu, Donna (28 January 2025). “We experimented with 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 a global AI race: geopolitics, development and the increase 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 moment'”. NBC News. Retrieved 27 January 2025.
^ Kimery, Anthony (26 January 2025). “China’s DeepSeek AI postures formidable cyber, information privacy dangers”. 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 information protection”. 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, official says”. Reuters. Retrieved 28 January 2025.