Overview

  • Founded Date December 18, 1943
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Company Description

This Stage used 3 Reward Models

DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese synthetic intelligence business that establishes open-source large language designs (LLMs). Based in Hangzhou, Zhejiang, it is owned and funded by Chinese hedge fund High-Flyer, whose co-founder, Liang Wenfeng, developed the company in 2023 and serves as its CEO.

The DeepSeek-R1 model supplies actions comparable to other modern 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 an equivalent LLM. [2] [3] [4] DeepSeek’s AI models were established in the middle of United States sanctions on India and China for Nvidia chips, [5] which were intended to limit the capability of these two countries to develop sophisticated AI systems. [6] [7]

On 10 January 2025, DeepSeek released its very first free chatbot app, based on the DeepSeek-R1 design, for iOS and Android; by 27 January, DeepSeek-R1 had gone beyond ChatGPT as the most-downloaded free app on the iOS App Store in the United States, [8] causing Nvidia’s share cost to come by 18%. [9] [10] DeepSeek’s success against bigger and more established competitors has been referred to as “overthrowing AI”, [8] making up “the very first chance at what is emerging as a worldwide AI space race”, [11] and ushering in “a brand-new era of AI brinkmanship”. [12]

DeepSeek makes its generative synthetic intelligence algorithms, models, and training details open-source, allowing its code to be easily available for use, adjustment, watching, and developing documents for constructing functions. [13] The business supposedly strongly recruits young AI researchers from top Chinese universities, [8] and employs from outside the computer technology field to diversify its designs’ knowledge and abilities. [3]

In February 2016, High-Flyer was co-founded by AI enthusiast Liang Wenfeng, who had been trading since the 2007-2008 monetary crisis while attending Zhejiang University. [14] By 2019, he developed 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 expert system chatbot open source, suggesting its code is easily available for usage, adjustment, and viewing. This consists of approval to gain access to and use the source code, along with design files, for building functions. [13]

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

In April 2023, High-Flyer began an artificial basic intelligence laboratory committed to research establishing AI tools separate from High-Flyer’s financial business. [17] [18] In May 2023, with High-Flyer as one of the financiers, the lab became its own company, DeepSeek. [15] [19] [18] Venture capital firms hesitated in supplying funding as it was unlikely that it would be able to create an exit in a short time period. [15]

After releasing DeepSeek-V2 in May 2024, which offered strong efficiency for a low price, DeepSeek ended up being understood as the driver for China’s AI model price war. It was quickly dubbed the “Pinduoduo of AI”, and other major tech giants such as ByteDance, Tencent, Baidu, and Alibaba began to cut the rate of their AI models to take on the business. Despite the low price charged by DeepSeek, it was lucrative compared to its rivals that were losing money. [20]

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

DeepSeek’s hiring preferences target technical capabilities rather than work experience, leading to a lot of new hires being either recent university graduates or developers whose AI careers are less established. [18] [3] Likewise, the business recruits people without any computer science background to help its technology comprehend other subjects and knowledge areas, consisting of having the ability to generate poetry and carry out well on the infamously hard Chinese college admissions exams (Gaokao). [3]

Development and release history

DeepSeek LLM

On 2 November 2023, DeepSeek released its very first series of design, DeepSeek-Coder, which is available free of charge to both scientists 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 usage” for the design itself. [21]

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

1. Pretraining: 1.8 T tokens (87% source code, 10% code-related English (GitHub markdown and Stack Exchange), and 3% code-unrelated Chinese).
2. Long-context pretraining: 200B tokens. This extends the context length from 4K to 16K. This produced the Base models.
3. Supervised finetuning (SFT): 2B tokens of guideline data. This produced the Instruct models.

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

On 29 November 2023, DeepSeek released the DeepSeek-LLM series of designs, with 7B and 67B parameters in both Base and Chat types (no Instruct was launched). It was established to compete with other LLMs available at the time. The paper declared benchmark outcomes greater than most 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 model itself. [27]

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

The Chat variations of the 2 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 released 2 DeepSeek-MoE designs (Base, Chat), each of 16B parameters (2.7 B triggered per token, 4K context length). The training was basically the like DeepSeek-LLM 7B, and was trained on a part of its training dataset. They claimed similar efficiency with a 16B MoE as a 7B non-MoE. In architecture, it is a variant of the basic sparsely-gated MoE, with “shared professionals” that are constantly queried, and “routed professionals” that might not be. They found this to assist with professional balancing. In basic MoE, some experts can become extremely relied on, while other professionals may be hardly ever used, squandering parameters. Attempting to stabilize the experts so that they are similarly used then causes experts to duplicate the exact same capability. They proposed the shared professionals to discover core capacities that are often utilized, and let the routed professionals to learn the peripheral capabilities that are rarely used. [28]

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

1. Initialize with a 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 mathematics issues and their tool-use-integrated step-by-step services. This produced the Instruct model.
Reinforcement knowing (RL): The benefit model was a process reward model (PRM) trained from Base according to the Math-Shepherd approach. [30] This benefit model was then utilized to train Instruct using group relative policy optimization (GRPO) on a dataset of 144K math concerns “associated to GSM8K and MATH”. The reward model was constantly upgraded during training to prevent benefit hacking. This led to the RL design.

V2

In May 2024, they launched the DeepSeek-V2 series. The series includes 4 models, 2 base models (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 using YaRN. [32] This led to 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 released.
4. RL using GRPO in two phases. The first stage was trained to resolve mathematics and coding problems. This stage utilized 1 reward model, trained on compiler feedback (for coding) and ground-truth labels (for math). The second phase was trained to be valuable, safe, and follow rules. This phase used 3 reward designs. The helpfulness and safety benefit models were trained on human preference information. The rule-based reward design was manually configured. All skilled benefit designs were initialized from DeepSeek-V2-Chat (SFT). This led to the released variation of DeepSeek-V2-Chat.

They went with 2-staged RL, due to the fact that they discovered that RL on thinking information had “distinct qualities” various from RL on basic data. For instance, RL on reasoning might enhance over more training steps. [31]

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

Architecturally, the V2 designs were substantially modified from the DeepSeek LLM series. They changed the standard attention mechanism by a low-rank approximation called multi-head latent attention (MLA), and utilized the mix 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 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 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 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 produce 20K code-related and 30K math-related guideline information, then combined with an instruction dataset of 300M tokens. This was utilized for SFT.
2. RL with GRPO. The benefit for math issues was calculated by comparing to the ground-truth label. The benefit for code problems was produced by a reward model trained to anticipate 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 design DeepSeek-V3-Base and a chat design DeepSeek-V3. The model architecture is essentially the same as V2. They were trained as follows: [37]

1. Pretraining on 14.8 T tokens of a multilingual corpus, mainly English and Chinese. It included a greater ratio of math and programs than the pretraining dataset of V2.
2. Extend context length twice, from 4K to 32K and after that to 128K, using YaRN. [32] This produced DeepSeek-V3-Base.
3. SFT for 2 epochs on 1.5 M samples of thinking (math, shows, reasoning) and non-reasoning (creative writing, roleplay, basic question answering) information. Reasoning information was generated by “expert designs”. Non-reasoning data was produced by DeepSeek-V2.5 and examined by humans. – The “skilled models” were trained by beginning with an unspecified base model, then SFT on both data, and synthetic information generated by an internal DeepSeek-R1 model. The system timely asked the R1 to reflect and validate throughout thinking. Then the professional models were RL utilizing an undefined benefit function.
– Each specialist design was trained to create just artificial reasoning information in one specific domain (mathematics, shows, reasoning).
– Expert designs were used, rather of R1 itself, because the output from R1 itself suffered “overthinking, bad format, and excessive length”.

4. Model-based benefit models were made by beginning with a SFT checkpoint of V3, then finetuning on human choice information consisting of both final reward and chain-of-thought resulting in the last reward. The reward model produced benefit signals for both concerns with objective but free-form responses, and questions without unbiased answers (such as innovative writing).
5. A SFT checkpoint of V3 was trained by GRPO utilizing both reward designs and rule-based reward. The rule-based benefit was calculated for math issues with a last answer (put in a box), and for programs issues by unit tests. This produced DeepSeek-V3.

The DeepSeek team carried out extensive low-level engineering to achieve effectiveness. They used mixed-precision math. Much of the forward pass was performed in 8-bit drifting point numbers (5E2M: 5-bit exponent and 2-bit mantissa) rather than the basic 32-bit, requiring special GEMM regimens to build up precisely. They used a custom-made 12-bit float (E5M6) for only the inputs to the linear layers after the attention modules. Optimizer states were in 16-bit (BF16). They minimized the communication latency by overlapping extensively computation and communication, such as devoting 20 streaming multiprocessors out of 132 per H800 for just inter-GPU communication. They lowered interaction by rearranging (every 10 minutes) the exact device each professional was on in order to avoid certain makers 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 released 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 surpassed 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 through DeepSeek’s API, as well as 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 analytical. DeepSeek claimed that it surpassed performance of OpenAI o1 on benchmarks 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 model reached a solution quicker 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 also launched some “DeepSeek-R1-Distill” models, which are not initialized on V3-Base, however instead are initialized from other pretrained open-weight designs, including LLaMA and Qwen, then fine-tuned on artificial information generated by R1. [47]

A discussion in between User and Assistant. The user asks a concern, and the Assistant fixes it. The assistant first thinks of the reasoning procedure in the mind and then provides the user with the answer. The thinking process and answer are confined within and tags, respectively, i.e., thinking process here address here. User:. Assistant:

DeepSeek-R1-Zero was trained exclusively utilizing GRPO RL without SFT. Unlike previous versions, they used no model-based reward. All reward functions were rule-based, “mainly” of 2 types (other types were not specified): accuracy rewards and format benefits. Accuracy reward was inspecting whether a boxed answer is proper (for math) or whether a code passes tests (for programming). Format reward 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 further 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, but likewise with a “language consistency reward” to motivate it to react monolingually. This produced an internal design not released.
3. Synthesize 600K thinking information from the internal design, with rejection sampling (i.e. if the produced reasoning had a wrong final response, then it is removed). Synthesize 200K non-reasoning data (writing, accurate QA, self-cognition, translation) utilizing DeepSeek-V3.
4. SFT DeepSeek-V3-Base on the 800K synthetic information for 2 epochs.
5. GRPO RL with rule-based benefit (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 data manufactured from DeepSeek-R1, in a similar way as action 3 above. They were not trained with RL. [47]

Assessment and responses

DeepSeek launched its AI Assistant, which utilizes the V3 design as a chatbot app for Apple IOS and Android. By 27 January 2025 the app had surpassed ChatGPT as the highest-rated complimentary app on the iOS App Store in the United States; its chatbot reportedly responds to questions, solves logic problems and writes computer programs on par with other chatbots on the market, according to benchmark tests utilized by American AI companies. [3]

DeepSeek-V3 uses considerably less resources compared to its peers; for instance, whereas the world’s leading AI companies train their chatbots with supercomputers utilizing as many as 16,000 graphics processing systems (GPUs), if not more, DeepSeek claims to require only about 2,000 GPUs, specifically 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 giant Meta invested building its latest AI technology. [3]

DeepSeek’s competitive efficiency at reasonably very little cost has actually been recognized as possibly challenging the global supremacy 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 minute” for American AI. [49] [50] The performance of its R1 model was apparently “on par with” among OpenAI’s newest models when utilized for jobs such as mathematics, coding, and natural language reasoning; [51] echoing other analysts, American Silicon Valley venture capitalist Marc Andreessen similarly described R1 as “AI’s Sputnik minute”. [51]

DeepSeek’s founder, 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 widely applauded DeepSeek as a nationwide possession. [53] [54] On 20 January 2025, China’s Premier Li Qiang invited Liang Wenfeng to his symposium with experts and asked him to offer viewpoints and ideas on a draft for comments of the annual 2024 government work report. [55]

DeepSeek’s optimization of minimal resources has actually highlighted potential limits of United States sanctions on China’s AI development, which include export constraints on advanced AI chips to China [18] [56] The success of the company’s AI models as a result “triggered market turmoil” [57] and caused shares in major worldwide 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 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 tape losses of about $593 billion in the market capitalizations of AI and hardware business; [59] by 28 January 2025, an overall of $1 trillion of worth was cleaned off American stocks. [50]

Leading figures in the American AI sector had mixed responses 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 establish American AI infrastructure-both called DeepSeek “extremely impressive”. [61] [62] American President Donald Trump, who announced The Stargate Project, called DeepSeek a wake-up call [63] and a positive development. [64] [50] [51] [65] Other leaders in the field, consisting of Scale AI CEO Alexandr Wang, Anthropic cofounder and CEO Dario Amodei, and Elon Musk expressed apprehension of the app’s efficiency or of the sustainability of its success. [60] [66] [67] Various business, including Amazon Web Services, Toyota, and Stripe, are seeking to utilize the model in their program. [68]

On 27 January 2025, DeepSeek limited its new user registration to phone numbers from mainland China, e-mail addresses, or Google account logins, following a “large-scale” cyberattack interrupted the appropriate performance of its servers. [69] [70]

Some sources have observed that the official application shows user interface (API) version of R1, which ranges from servers located in China, utilizes censorship mechanisms for subjects that are thought about politically sensitive for the federal government of China. For example, the model declines to respond to 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 might initially produce a response, however then deletes it soon later on and changes it with a message such as: “Sorry, that’s beyond my present scope. Let’s discuss something else.” [72] The integrated censorship systems and constraints can only be gotten rid of to a limited extent in the open-source variation of the R1 model. If the “core socialist worths” defined by the Chinese Internet regulative authorities are discussed, or the political status of Taiwan is raised, conversations are ended. [74] When checked by NBC News, DeepSeek’s R1 explained Taiwan as “an inalienable part of China’s territory,” and specified: “We securely oppose any kind of ‘Taiwan self-reliance’ separatist activities and are devoted 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 particular responses to a few of these topics by requesting in its answer to swap specific letters for similar-looking numbers. [73]

Security and privacy

Some specialists fear that the federal government of China could utilize the AI system for foreign influence operations, spreading out disinformation, monitoring and the advancement of cyberweapons. [76] [77] [78] DeepSeek’s personal privacy conditions say “We save the info we gather in secure 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 offer to our design and Services”. Although the data storage and collection policy is constant with ChatGPT’s privacy policy, [79] a Wired post reports this as security concerns. [80] In action, the Italian data defense authority is looking for extra information on DeepSeek’s collection and usage of individual data, and the United States National Security Council announced that it had begun a national security review. [81] [82] Taiwan’s government banned the usage of DeepSeek at federal government ministries on security grounds and South Korea’s Personal Information Protection Commission opened an inquiry into DeepSeek’s usage of personal details. [83]

Artificial intelligence industry in China.

Notes

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