하지만 곧 ‘벤치마크’가 목적이 아니라 ‘근본적인 도전 과제’를 해결하겠다는 방향으로 전환했고, 이 결정이 결실을 맺어 현재 DeepSeek LLM, DeepSeekMoE, DeepSeekMath, DeepSeek-VL, DeepSeek-V2, DeepSeek-Coder-V2, DeepSeek-Prover-V1.5 등 다양한 용도에 활용할 수 있는 최고 수준의 모델들을 빠르게 연이어 출시했습니다. 현재 출시한 모델들 중 가장 인기있다고 할 수 있는 DeepSeek-Coder-V2는 코딩 작업에서 최고 수준의 성능과 비용 경쟁력을 보여주고 있고, Ollama와 함께 실행할 수 있어서 인디 개발자나 엔지니어들에게 아주 매력적인 옵션입니다. 우리나라의 LLM 스타트업들도, 알게 모르게 그저 받아들이고만 있는 통념이 있다면 그에 도전하면서, 독특한 고유의 기술을 계속해서 쌓고 글로벌 AI 생태계에 크게 기여할 수 있는 기업들이 더 많이 등장하기를 기대합니다. 특히, DeepSeek만의 독자적인 MoE 아키텍처, 그리고 어텐션 메커니즘의 변형 MLA (Multi-Head Latent Attention)를 고안해서 LLM을 더 다양하게, 비용 효율적인 구조로 만들어서 좋은 성능을 보여주도록 만든 점이 아주 흥미로웠습니다. DeepSeek-Coder-V2 모델을 기준으로 볼 때, Artificial Analysis의 분석에 따르면 이 모델은 최상급의 품질 대비 비용 경쟁력을 보여줍니다. 다른 오픈소스 모델은 압도하는 품질 대비 비용 경쟁력이라고 봐야 할 거 같고, 빅테크와 거대 스타트업들에 밀리지 않습니다. 처음에는 경쟁 모델보다 우수한 벤치마크 기록을 달성하려는 목적에서 출발, 다른 기업과 비슷하게 다소 평범한(?) 모델을 만들었는데요. 다만, DeepSeek-Coder-V2 모델이 Latency라든가 Speed 관점에서는 다른 모델 대비 열위로 나타나고 있어서, 해당하는 유즈케이스의 특성을 고려해서 그에 부합하는 모델을 골라야 합니다.

The advantages extend beyond just speed. DeepSeek-V2.5’s structure contains key innovations, similar to Multi-Head Latent Attention (MLA), which significantly reduces the KV cache, thereby improving inference speed with out compromising on model performance. The number of operations in vanilla attention is quadratic within the sequence length, and the memory will increase linearly with the number of tokens. To create their coaching dataset, the researchers gathered lots of of 1000's of excessive-faculty and undergraduate-stage mathematical competitors problems from the internet, with a concentrate on algebra, number theory, combinatorics, geometry, and statistics. High-Flyer's investment and analysis group had 160 members as of 2021 which embrace Olympiad Gold medalists, web giant experts and senior researchers. By breaking down the obstacles of closed-source fashions, DeepSeek-Coder-V2 might result in more accessible and highly effective instruments for developers and researchers working with code. In an interview with TechTalks, Huajian Xin, lead writer of the paper, mentioned that the primary motivation behind DeepSeek-Prover was to advance formal mathematics. AlphaGeometry additionally makes use of a geometry-specific language, whereas DeepSeek-Prover leverages Lean’s complete library, which covers various areas of arithmetic.

While it responds to a prompt, use a command like btop to test if the GPU is being used efficiently. Producing research like this takes a ton of labor - buying a subscription would go a good distance toward a deep, significant understanding of AI developments in China as they occur in real time. "It's fairly shocking to construct an AI model and depart the backdoor wide open from a safety perspective," says impartial security researcher Jeremiah Fowler, who was not concerned within the Wiz research but makes a speciality of discovering uncovered databases. "Usually when we find this sort of publicity, it’s in some uncared for service that takes us hours to seek out-hours of scanning," says Nir Ohfeld, the head of vulnerability research at Wiz. AI labs corresponding to OpenAI and Meta AI have additionally used lean in their research. If in case you have played with LLM outputs, you recognize it can be challenging to validate structured responses. In a head-to-head comparability with GPT-3.5, DeepSeek LLM 67B Chat emerges because the frontrunner in Chinese language proficiency. DeepSeek LLM 67B Chat had already demonstrated vital performance, approaching that of GPT-4. As a result of an unsecured database, free deepseek customers' chat historical past was accessible through the Internet.

In October 2023, High-Flyer introduced it had suspended its co-founder and senior executive Xu Jin from work on account of his "improper handling of a household matter" and having "a adverse impression on the company's popularity", following a social media accusation put up and a subsequent divorce court docket case filed by Xu Jin's wife relating to Xu's extramarital affair. At the end of 2021, High-Flyer put out a public assertion on WeChat apologizing for its losses in belongings as a consequence of poor efficiency. In addition the company acknowledged it had expanded its assets too rapidly resulting in similar buying and selling methods that made operations harder. By this 12 months all of High-Flyer’s methods have been using AI which drew comparisons to Renaissance Technologies. In 2016, High-Flyer experimented with a multi-factor price-volume primarily based model to take stock positions, began testing in trading the next 12 months after which extra broadly adopted machine studying-based strategies. It was accepted as a professional Foreign Institutional Investor one year later.

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