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 ai-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 benefits prolong past simply speed. DeepSeek-V2.5’s architecture consists of key improvements, comparable to Multi-Head Latent Attention (MLA), which significantly reduces the KV cache, thereby enhancing inference velocity without compromising on mannequin efficiency. The variety of operations in vanilla attention is quadratic within the sequence size, and the reminiscence will increase linearly with the number of tokens. To create their training dataset, the researchers gathered a whole lot of thousands of high-school and undergraduate-level mathematical competition problems from the web, with a concentrate on algebra, number idea, combinatorics, geometry, and statistics. High-Flyer's funding and analysis crew had 160 members as of 2021 which include Olympiad Gold medalists, web big consultants and senior researchers. By breaking down the limitations of closed-source fashions, DeepSeek-Coder-V2 could lead to more accessible and highly effective instruments for builders and researchers working with code. In an interview with TechTalks, Huajian Xin, lead writer of the paper, stated that the main motivation behind DeepSeek-Prover was to advance formal arithmetic. AlphaGeometry additionally makes use of a geometry-specific language, while DeepSeek-Prover leverages Lean’s comprehensive library, which covers numerous areas of mathematics.

While it responds to a prompt, use a command like btop to verify if the GPU is getting used successfully. Producing research like this takes a ton of work - purchasing a subscription would go a great distance toward a deep, meaningful understanding of AI developments in China as they happen in real time. "It's pretty shocking to construct an AI mannequin and depart the backdoor extensive open from a security perspective," says unbiased security researcher Jeremiah Fowler, who was not concerned in the Wiz research however makes a speciality of discovering uncovered databases. "Usually when we discover this sort of publicity, it’s in some neglected service that takes us hours to find-hours of scanning," says Nir Ohfeld, the pinnacle of vulnerability research at Wiz. AI labs corresponding to OpenAI and Meta AI have additionally used lean in their research. When you've got performed with LLM outputs, you already know it can be difficult 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 important efficiency, approaching that of GPT-4. As a consequence of an unsecured database, DeepSeek users' chat historical past was accessible via the Internet.

In October 2023, High-Flyer announced it had suspended its co-founder and senior executive Xu Jin from work on account of his "improper handling of a family matter" and having "a damaging influence on the corporate's reputation", following a social media accusation publish and a subsequent divorce courtroom case filed by Xu Jin's wife relating to Xu's extramarital affair. At the tip of 2021, High-Flyer put out a public assertion on WeChat apologizing for its losses in property as a result of poor efficiency. In addition the corporate acknowledged it had expanded its property too shortly resulting in similar buying and selling strategies that made operations harder. By this yr all of High-Flyer’s methods were using AI which drew comparisons to Renaissance Technologies. In 2016, High-Flyer experimented with a multi-factor worth-quantity primarily based model to take inventory positions, began testing in buying and selling the following year and then more broadly adopted machine learning-based mostly methods. It was approved as a professional Foreign Institutional Investor one yr later.

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