Rsat Arquitectos
Add a review FollowOverview
-
Founded Date March 22, 1991
-
Posted Jobs 0
-
Viewed 8
Company Description
This Stage Utilized 3 Reward Models
DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese expert system company that establishes open-source big language models (LLMs). Based in Hangzhou, Zhejiang, it is owned and moneyed by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, established the business in 2023 and functions as its CEO.
The DeepSeek-R1 design supplies responses comparable to other modern big language models, 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 amid United States sanctions on India and China for Nvidia chips, [5] which were planned to limit the capability of these two countries to develop advanced AI systems. [6] [7]
On 10 January 2025, DeepSeek released its very first complimentary chatbot app, based upon the DeepSeek-R1 model, for iOS and Android; by 27 January, DeepSeek-R1 had actually surpassed ChatGPT as the most-downloaded totally free app on the iOS App Store in the United States, [8] triggering Nvidia’s share cost to visit 18%. [9] [10] DeepSeek’s success against bigger and more established competitors has actually been referred to as “upending AI”, [8] constituting “the very first shot at what is emerging as an international AI space race”, [11] and introducing “a new period of AI brinkmanship”. [12]
DeepSeek makes its generative synthetic intelligence algorithms, designs, and training details open-source, permitting its code to be freely offered for use, adjustment, watching, and creating documents for building purposes. [13] The company supposedly vigorously recruits young AI researchers from leading Chinese universities, [8] and works with from outside the computer system 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 attending 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 solely utilized AI in trading. [15] DeepSeek has made its generative synthetic intelligence chatbot open source, meaning its code is freely offered for use, modification, and viewing. This includes permission to access and utilize the source code, in addition to design files, for constructing purposes. [13]
According to 36Kr, Liang had built up a store of 10,000 Nvidia A100 GPUs, which are utilized to train AI [16], before the United States federal government enforced AI chip limitations on China. [15]
In April 2023, High-Flyer began a synthetic general intelligence lab devoted to research establishing AI tools separate from High-Flyer’s financial company. [17] [18] In May 2023, with High-Flyer as one of the investors, the laboratory became its own business, DeepSeek. [15] [19] [18] Equity capital firms were hesitant in supplying funding as it was unlikely that it would have the ability to create an exit in a short amount of time. [15]
After releasing DeepSeek-V2 in May 2024, which offered strong efficiency for a low cost, DeepSeek became called the catalyst for China’s AI design price war. It was rapidly dubbed the “Pinduoduo of AI“, and other significant tech giants such as ByteDance, Tencent, Baidu, and Alibaba started to cut the cost of their AI models to compete with the company. Despite the low price charged by DeepSeek, it paid compared to its rivals that were losing money. [20]
DeepSeek is focused on research and has no in-depth strategies for commercialization; [20] this also permits its technology to avoid the most strict arrangements of China’s AI policies, such as requiring consumer-facing technology to abide by the government’s controls on details. [3]
DeepSeek’s employing preferences target technical capabilities rather than work experience, resulting in a lot of new hires being either recent university graduates or designers whose AI professions are less developed. [18] [3] Likewise, the business hires people without any computer system science background to help its innovation understand other topics and knowledge locations, consisting of being able to produce poetry and perform well on the infamously difficult Chinese college admissions examinations (Gaokao). [3]
Development and release history
DeepSeek LLM
On 2 November 2023, DeepSeek launched its very first series of design, DeepSeek-Coder, which is readily available for complimentary to both researchers and industrial users. The code for the model was made open-source under the MIT license, with an extra license arrangement (“DeepSeek license”) regarding “open and responsible downstream use” for the model itself. [21]
They are of the exact same architecture as DeepSeek LLM detailed listed 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 designs.
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 designs, with 7B and 67B criteria in both Base and Chat forms (no Instruct was released). It was established to complete with other LLMs available at the time. The paper declared benchmark results greater than most open source LLMs at the time, particularly 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 basically the 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 acquired by deduplicating the Common Crawl. [26]
The Chat variations of the two Base designs was also launched simultaneously, gotten by training Base by supervised finetuning (SFT) followed by direct policy optimization (DPO). [26]
On 9 January 2024, they launched 2 DeepSeek-MoE models (Base, Chat), each of 16B parameters (2.7 B triggered 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 equivalent performance with a 16B MoE as a 7B non-MoE. In architecture, it is a variation of the standard sparsely-gated MoE, with “shared specialists” that are constantly queried, and “routed experts” that may not be. They discovered this to aid with professional balancing. In basic MoE, some experts can end up being overly depended on, while other experts might be hardly ever utilized, squandering criteria. Attempting to balance the experts so that they are similarly used then causes experts to reproduce the exact same capacity. They proposed the shared professionals to find out core capabilities that are often used, and let the routed specialists to discover the peripheral capabilities that are rarely utilized. [28]
In April 2024, they launched 3 DeepSeek-Math designs specialized for doing mathematics: 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 design.
3. Train an instruction-following design by SFT Base with 776K math issues and their tool-use-integrated detailed options. This produced the Instruct model.
Reinforcement learning (RL): The benefit model was a process reward design (PRM) trained from Base according to the Math-Shepherd method. [30] This reward model was then used to train Instruct using group relative policy optimization (GRPO) on a dataset of 144K math questions “associated to GSM8K and MATH”. The reward model was continuously upgraded during training to prevent reward hacking. This led to the RL design.
V2
In May 2024, they launched the DeepSeek-V2 series. The series consists of 4 models, 2 base designs (DeepSeek-V2, DeepSeek-V2-Lite) and 2 chatbots (-Chat). The 2 larger 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 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 phases. The very first stage was trained to fix math and coding issues. This stage utilized 1 benefit design, trained on compiler feedback (for coding) and ground-truth labels (for mathematics). The 2nd phase was trained to be practical, safe, and follow guidelines. This stage utilized 3 reward designs. The helpfulness and safety reward designs were trained on human preference data. The rule-based reward design was by hand programmed. All qualified benefit designs were initialized from DeepSeek-V2-Chat (SFT). This led to the released version of DeepSeek-V2-Chat.
They went with 2-staged RL, due to the fact that they discovered that RL on thinking information had “distinct characteristics” different from RL on general information. For example, RL on reasoning could improve over more training steps. [31]
The two V2-Lite models were smaller sized, and skilled similarly, though DeepSeek-V2-Lite-Chat just went through SFT, not RL. They trained the Lite variation to assist “additional research and development on MLA and DeepSeekMoE”. [31]
Architecturally, the V2 designs were substantially modified from the DeepSeek LLM series. They changed the basic attention mechanism by a low-rank approximation called multi-head hidden attention (MLA), and used the mixture of specialists (MoE) variant formerly published in January. [28]
The Financial Times reported that it was cheaper than its peers with a price 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 models in the DeepSeek-Coder-V2 series: V2-Base, V2-Lite-Base, V2-Instruct, V2-Lite-Instruct. They were trained as follows: [35] [note 2]
1. The Base designs were initialized from corresponding intermediate checkpoints after pretraining on 4.2 T tokens (not the variation at the end of pretraining), then pretrained further for 6T tokens, then context-extended to 128K context length. This produced the Base models.
DeepSeek-Coder and DeepSeek-Math were utilized to generate 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 reward for math issues was computed by comparing with the ground-truth label. The reward for code problems was generated by a reward design trained to anticipate whether a program would pass the system 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 released a base design DeepSeek-V3-Base and a chat model DeepSeek-V3. The design architecture is essentially the exact same as V2. They were trained as follows: [37]
1. Pretraining on 14.8 T tokens of a multilingual corpus, mostly English and Chinese. It contained a greater ratio of math and programming than the pretraining dataset of V2.
2. Extend context length twice, from 4K to 32K and then to 128K, using YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 dates on 1.5 M samples of reasoning (math, programs, logic) and non-reasoning (innovative writing, roleplay, basic question answering) data. Reasoning data was produced by “expert models”. Non-reasoning information was generated by DeepSeek-V2.5 and examined by human beings. – The “expert designs” were trained by starting with an undefined base design, then SFT on both information, and artificial information generated by an internal DeepSeek-R1 design. The system timely asked the R1 to reflect and validate during thinking. Then the expert models were RL using an undefined benefit function.
– Each expert design was trained to generate just artificial reasoning data in one particular domain (math, shows, reasoning).
– Expert designs were utilized, rather of R1 itself, since the output from R1 itself suffered “overthinking, poor format, and extreme length”.
4. Model-based reward models were made by beginning with a SFT checkpoint of V3, then finetuning on human preference data consisting of both last benefit and chain-of-thought leading to the last reward. The reward model produced benefit signals for both questions with unbiased but free-form responses, and concerns without objective responses (such as innovative writing).
5. A SFT checkpoint of V3 was trained by GRPO using both reward models and rule-based benefit. The rule-based benefit was calculated for math issues with a last response (put in a box), and for programming issues by unit tests. This produced DeepSeek-V3.
The DeepSeek group carried out substantial low-level engineering to attain effectiveness. They utilized mixed-precision arithmetic. Much of the forward pass was carried out in 8-bit drifting point numbers (5E2M: 5-bit exponent and 2-bit mantissa) rather than the standard 32-bit, needing special GEMM routines to accumulate properly. They used a customized 12-bit float (E5M6) for only the inputs to the direct layers after the attention modules. Optimizer states were in 16-bit (BF16). They minimized the communication latency by overlapping thoroughly computation and interaction, such as committing 20 streaming multiprocessors out of 132 per H800 for only inter-GPU interaction. They reduced communication by rearranging (every 10 minutes) the precise maker each specialist was on in order to avoid particular devices being queried regularly 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 linked by NVLink, and the clusters are connected 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 ended up being accessible via DeepSeek’s API, in addition to by means of a chat user interface after logging in. [42] [43] [note 3] It was trained for sensible reasoning, mathematical reasoning, and real-time problem-solving. DeepSeek declared that it surpassed efficiency of OpenAI o1 on criteria such as American Invitational Mathematics Examination (AIME) and MATH. [44] However, The Wall Street Journal specified when it utilized 15 issues from the 2024 edition of AIME, the o1 design reached a service 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 business likewise released some “DeepSeek-R1-Distill” designs, which are not initialized on V3-Base, however rather are initialized from other pretrained open-weight models, including LLaMA and Qwen, then fine-tuned on synthetic information generated by R1. [47]
A conversation between User and Assistant. The user asks a question, and the Assistant resolves it. The assistant initially thinks of the thinking procedure in the mind and after that supplies the user with the response. The reasoning process and answer are enclosed within and tags, respectively, i.e., thinking process here answer here. User:. Assistant:
DeepSeek-R1-Zero was trained solely using GRPO RL without SFT. Unlike previous variations, they utilized no model-based reward. All reward functions were rule-based, “mainly” of two types (other types were not defined): accuracy rewards and format benefits. Accuracy reward was checking whether a boxed response is proper (for mathematics) or whether a code passes tests (for programs). Format benefit was examining whether the design puts its thinking trace within … [47]
As R1-Zero has issues with readability and mixing languages, R1 was trained to deal with these concerns and more improve reasoning: [47]
1. SFT DeepSeek-V3-Base on “thousands” of “cold-start” information all with the basic format of|special_token|| special_token|summary >.
2. Apply the exact same RL process as R1-Zero, but also with a “language consistency benefit” to encourage it to respond monolingually. This produced an internal design not released.
3. Synthesize 600K reasoning data from the internal design, with rejection sampling (i.e. if the created thinking had a wrong last answer, then it is eliminated). Synthesize 200K non-reasoning data (writing, factual QA, self-cognition, translation) utilizing DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K artificial data for 2 epochs.
5. GRPO RL with rule-based reward (for thinking tasks) 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 responses
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 surpassed ChatGPT as the highest-rated complimentary app on the iOS App Store in the United States; its chatbot apparently answers questions, fixes reasoning issues and writes computer programs on par with other chatbots on the marketplace, according to benchmark tests used by American AI business. [3]
DeepSeek-V3 uses substantially less resources compared to its peers; for instance, whereas the world’s leading AI business train their chatbots with supercomputers utilizing as lots of as 16,000 graphics processing units (GPUs), if not more, DeepSeek claims to have actually required just about 2,000 GPUs, particularly the H800 series chip from Nvidia. [37] It was trained in around 55 days at an expense of US$ 5.58 million, [37] which is roughly one tenth of what United States tech huge Meta invested constructing its latest AI technology. [3]
DeepSeek’s competitive performance at relatively very little expense has been recognized as possibly challenging the international supremacy of American AI designs. [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 design was apparently “on par with” one of OpenAI’s most current designs when utilized 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 moment”. [51]
DeepSeek’s creator, Liang Wenfeng has been compared to Open AI CEO Sam Altman, with CNN calling him the Sam Altman of China and an evangelist for AI. [52] Chinese state media commonly praised DeepSeek as a nationwide possession. [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 ideas on a draft for remarks of the yearly 2024 federal government work report. [55]
DeepSeek’s optimization of restricted resources has highlighted prospective limitations of United States sanctions on China’s AI development, that include export restrictions 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 global technology 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 firms likewise sank, consisting of Microsoft (down 2.5%), Google’s owner Alphabet (down over 4%), and Dutch chip equipment maker ASML (down over 7%). [51] A worldwide selloff of technology stocks on Nasdaq, prompted by the release of the R1 model, had led to record losses of about $593 billion in the market capitalizations of AI and hardware business; [59] by 28 January 2025, an overall of $1 trillion of worth was cleaned off 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 associated with the United States government-backed “Stargate Project” to develop American AI infrastructure-both called DeepSeek “super remarkable”. [61] [62] American President Donald Trump, who revealed The Stargate Project, called DeepSeek a wake-up call [63] and a favorable advancement. [64] [50] [51] [65] Other leaders in the field, consisting of Scale AI CEO Alexandr Wang, Anthropic cofounder and CEO Dario Amodei, and Elon Musk revealed skepticism of the app’s efficiency or of the sustainability of its success. [60] [66] [67] Various business, consisting of Amazon Web Services, Toyota, and Stripe, are seeking to use the model in their program. [68]
On 27 January 2025, DeepSeek restricted its new user registration to phone numbers from mainland China, email addresses, or Google account logins, following a “massive” cyberattack interrupted the correct functioning of its servers. [69] [70]
Some sources have observed that the main application programs user interface (API) variation of R1, which ranges from servers found in China, uses censorship mechanisms for subjects that are considered politically sensitive for the government of China. For example, the design refuses to address questions about the 1989 Tiananmen Square protests and massacre, persecution of Uyghurs, contrasts between Xi Jinping and Winnie the Pooh, or human rights in China. [71] [72] [73] The AI might initially produce a response, however then erases it shortly later on and changes it with a message such as: “Sorry, that’s beyond my existing scope. Let’s discuss something else.” [72] The incorporated censorship mechanisms and constraints can just be removed to a limited degree in the open-source variation of the R1 model. If the “core socialist worths” specified by the Chinese Internet regulative authorities are touched upon, or the political status of Taiwan is raised, discussions are terminated. [74] When evaluated by NBC News, DeepSeek’s R1 explained Taiwan as “an inalienable part of China’s territory,” and mentioned: “We strongly oppose any kind of ‘Taiwan independence’ separatist activities and are dedicated to accomplishing the total reunification of the motherland through tranquil means.” [75] In January 2025, Western researchers had the ability to fool DeepSeek into providing particular answers to a few of these subjects by asking for in its answer to switch specific letters for similar-looking numbers. [73]
Security and personal privacy
Some experts fear that the federal government of China might use the AI system for foreign impact operations, spreading out disinformation, monitoring and the development of cyberweapons. [76] [77] [78] DeepSeek’s privacy conditions say “We save the info we gather in safe servers found 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 information storage and collection policy follows ChatGPT’s personal privacy policy, [79] a Wired short article reports this as security concerns. [80] In action, the Italian data defense authority is looking for extra info on DeepSeek’s collection and use of individual information, and the United States National Security Council announced that it had actually begun a nationwide security review. [81] [82] Taiwan’s government prohibited making use of DeepSeek at federal government ministries on security grounds and South Korea’s Personal Information Protection Commission opened a query into DeepSeek’s usage of personal information. [83]
Expert system industry in China.
Notes
^ a b c The variety of heads does not equivalent the variety of KV heads, due to GQA.
^ Inexplicably, the 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 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 design DeepSeek thrills researchers”. Nature. doi:10.1038/ d41586-025-00229-6. ISSN 1476-4687. PMID 39849139.
^ a b Vincent, James (28 January 2025). “The DeepSeek panic reveals an AI world all set 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 designs and weaker chips call into concern trillions in AI facilities costs”. Business Insider.
^ Mallick, Subhrojit (16 January 2024). “Biden admin’s cap on GPU exports may strike India’s AI aspirations”. The Economic Times. Retrieved 29 January 2025.
^ Saran, Cliff (10 December 2024). “Nvidia investigation signals expanding of US and China chip war|Computer Weekly”. Computer Weekly. Retrieved 27 January 2025.
^ Sherman, Natalie (9 December 2024). “Nvidia targeted by China in brand-new chip war probe”. BBC. Retrieved 27 January 2025.
^ a b c Metz, Cade (27 January 2025). “What is DeepSeek? And How Is It Upending A.I.?”. The New York City Times. ISSN 0362-4331. Retrieved 27 January 2025.
^ Field, Hayden (27 January 2025). “China’s DeepSeek AI dismisses ChatGPT on App Store: Here’s what you must understand”. CNBC.
^ Picchi, Aimee (27 January 2025). “What is DeepSeek, and why is it causing 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 model 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 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 original on 22 January 2025. Retrieved 1 January 2025.
^ a b McMorrow, Ryan; Olcott, Eleanor (9 June 2024). “The Chinese quant fund-turned-AI pioneer”. Financial Times. Archived from the initial on 17 July 2024. Retrieved 28 December 2024.
^ a b Schneider, Jordan (27 November 2024). “Deepseek: The Quiet Giant Leading China’s AI Race”. ChinaTalk. Retrieved 28 December 2024.
^ “DeepSeek-Coder/LICENSE-MODEL at main · deepseek-ai/DeepSeek-Coder”. GitHub. Archived from the original on 22 January 2025. Retrieved 24 January 2025.
^ a b c Guo, Daya; Zhu, Qihao; Yang, Dejian; Xie, Zhenda; Dong, Kai; Zhang, Wentao; Chen, Guanting; Bi, Xiao; Wu, Y. (26 January 2024), DeepSeek-Coder: When the Large Language Model Meets Programming – The Rise of Code Intelligence, arXiv:2401.14196.
^ “DeepSeek Coder”. deepseekcoder.github.io. Retrieved 27 January 2025.
^ deepseek-ai/DeepSeek-Coder, DeepSeek, 27 January 2025, recovered 27 January 2025.
^ “deepseek-ai/deepseek-coder -5.7 bmqa-base · Hugging Face”. huggingface.co. Retrieved 27 January 2025.
^ a b c d DeepSeek-AI; Bi, Xiao; Chen, Deli; Chen, Guanting; Chen, Shanhuang; Dai, Damai; Deng, Chengqi; Ding, Honghui; Dong, Kai (5 January 2024), DeepSeek LLM: Scaling Open-Source Language Models with Longtermism, arXiv:2401.02954.
^ deepseek-ai/DeepSeek-LLM, DeepSeek, 27 January 2025, obtained 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 outshines Meta, OpenAI items”. South China Morning Post. Archived from the original on 27 December 2024. Retrieved 28 December 2024.
^ Sharma, Shubham (26 December 2024). “DeepSeek-V3, ultra-large open-source AI, exceeds Llama and Qwen on launch”. VentureBeat. Archived from the initial on 27 December 2024. Retrieved 28 December 2024.
^ Wiggers, Kyle (26 December 2024). “DeepSeek’s new AI model seems among the best ‘open’ 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: letting loose 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 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 through Reinforcement Learning, arXiv:2501.12948.
^ “Chinese AI startup DeepSeek overtakes ChatGPT on Apple App Store”. Reuters. 27 January 2025. Retrieved 27 January 2025.
^ Wade, David (6 December 2024). “American AI has 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 cleaned off US stocks after Chinese firm unveils AI chatbot” – by means of The Guardian.
^ a b c d Hoskins, Peter; Rahman-Jones, Imran (27 January 2025). “Nvidia shares sink as Chinese AI app spooks markets”. BBC. Retrieved 28 January 2025.
^ Goldman, David (27 January 2025). “What is DeepSeek, the Chinese AI startup that shook the tech world?|CNN Business”. CNN. Retrieved 29 January 2025.
^ “DeepSeek poses an obstacle to Beijing as much as to Silicon Valley”. The Economist. 29 January 2025. ISSN 0013-0613. Retrieved 31 January 2025.
^ Paul, Katie; Nellis, Stephen (30 January 2025). “Chinese state-linked accounts hyped DeepSeek AI launch ahead of US stock rout, Graphika says”. Reuters. Retrieved 30 January 2025.
^ 澎湃新闻 (22 January 2025). “量化巨头幻方创始人梁文锋参加总理座谈会并发言 , 他还创办了” AI界拼多多””. finance.sina.com.cn. Retrieved 31 January 2025.
^ Shilov, Anton (27 December 2024). “Chinese AI business’s AI model breakthrough highlights limits of US sanctions”. Tom’s Hardware. Archived from the initial on 28 December 2024. Retrieved 28 December 2024.
^ “DeepSeek updates – Chinese AI chatbot stimulates 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’ however Staying Skeptical”. Inc. Retrieved 29 January 2025.
^ Okemwa, Kevin (28 January 2025). “Microsoft CEO Satya Nadella promotes DeepSeek’s open-source AI as “incredibly excellent”: “We ought to take the advancements out of China very, very seriously””. Windows Central. Retrieved 28 January 2025.
^ Nazzaro, Miranda (28 January 2025). “OpenAI’s Sam Altman calls DeepSeek design ‘remarkable'”. 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 ‘questions’ DeepSeek’s claims, recommends enormous Nvidia GPU infrastructure”. Financialexpress. 28 January 2025. Retrieved 28 January 2025.
^ Kim, Eugene. “Big AWS clients, including Stripe and Toyota, are pestering the cloud giant for access to DeepSeek AI designs”. 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 temporarily restricted brand-new sign-ups, mentioning ‘massive malicious attacks'”. Business Insider.
^ Field, Matthew; Titcomb, James (27 January 2025). “Chinese AI has actually triggered a $1 trillion panic – and it does not appreciate totally free speech”. The Daily Telegraph. ISSN 0307-1235. Retrieved 27 January 2025.
^ a b Steinschaden, Jakob (27 January 2025). “DeepSeek: This is what live censorship 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, till we asked it about Tiananmen Square and Taiwan”. The Guardian. ISSN 0261-3077. Retrieved 30 January 2025.
^ “The Guardian view on a worldwide AI race: geopolitics, innovation and the increase of chaos”. 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 positions powerful cyber, data personal privacy threats”. Biometric Update. Retrieved 27 January 2025.
^ Booth, Robert; Milmo, Dan (28 January 2025). “Experts prompt 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 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, official states”. Reuters. Retrieved 28 January 2025.