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DeepSeek 모델은 처음 2023년 하반기에 출시된 후에 빠르게 AI 커뮤니티의 많은 관심을 받으면서 유명세를 탄 편이라고 할 수 있는데요. AI 커뮤니티의 관심은 - 어찌보면 당연하게도 - Llama나 Mistral 같은 모델에 집중될 수 밖에 없지만, DeepSeek이라는 스타트업 자체, 이 회사의 연구 방향과 출시하는 모델의 흐름은 한 번 살펴볼 만한 중요한 대상이라고 생각합니다. 중국 AI 스타트업 DeepSeek이 GPT-4를 넘어서는 오픈소스 AI 모델을 개발해 많은 관심을 받고 있습니다. ‘DeepSeek’은 오늘 이야기할 생성형 AI 모델 패밀리의 이름이자 이 모델을 만들고 있는 스타트업의 이름이기도 합니다. 역시 중국의 스타트업인 이 DeepSeek의 기술 혁신은 실리콘 밸리에서도 주목을 받고 있습니다. 이 소형 모델은 GPT-4의 수학적 추론 능력에 근접하는 성능을 보여줬을 뿐 아니라 또 다른, 우리에게도 널리 알려진 중국의 모델, Qwen-72B보다도 뛰어난 성능을 보여주었습니다. Moonshot AI 같은 중국의 생성형 AI 유니콘을 이전에 튜링 포스트 코리아에서도 소개한 적이 있는데요. 시장의 규모, 경제적/산업적 환경, 정치적 안정성 측면에서 우리나라와는 많은 차이가 있기는 하지만, 과연 우리나라의 생성형 AI 생태계가 어떤 도전을 해야 할지에 대한 하나의 시금석이 될 수도 있다고 생각합니다.

특히 DeepSeek-V2는 더 적은 메모리를 사용하면서도 더 빠르게 정보를 처리하는 또 하나의 혁신적 기법, MLA (Multi-Head Latent Attention)을 도입했습니다. DeepSeek-V2 introduces Multi-Head Latent Attention (MLA), a modified attention mechanism that compresses the KV cache right into a a lot smaller type. DeepSeek-V2 is a state-of-the-art language model that makes use of a Transformer structure mixed with an innovative MoE system and a specialised consideration mechanism called Multi-Head Latent Attention (MLA). Remember, these are recommendations, and the actual performance will rely upon a number of factors, together with the particular job, model implementation, and different system processes. Although particular technological instructions have repeatedly evolved, the mixture of fashions, knowledge, and computational power remains constant. Shared knowledgeable isolation: Shared experts are specific experts which are always activated, no matter what the router decides. OpenAI and ByteDance are even exploring potential analysis collaborations with the startup. At the time, the financial institution additionally highlighted that the magnitude of each potential information center capability it walked away from and the choice to pull again on land acquisition (which supports core lengthy-time period capacity growth) indicates the lack of a serious demand signal that Microsoft was initially responding to and that it believed the shift in Microsoft's appetite for capability is tied to OpenAI, which recent press experiences appear to verify.

In consequence, Nvidia's inventory experienced a significant decline on Monday, as anxious traders anxious that demand for Nvidia's most advanced chips-which also have the best profit margins-would drop if companies realized they might develop high-efficiency AI fashions with cheaper, less superior chips. MSFT thinks AI supply demand should be more inline by the end of their present FYI provide will grow extra in line with demand going forward relatively than being briefly supply. Through RL, it naturally realized to allocate more "thinking time" to more durable issues. It also scored 84.1% on the GSM8K mathematics dataset with out wonderful-tuning, exhibiting remarkable prowess in solving mathematical problems. Mathematical reasoning is a big problem for language fashions because of the complicated and structured nature of arithmetic. Handling long contexts: DeepSeek-Coder-V2 extends the context length from 16,000 to 128,000 tokens, permitting it to work with a lot bigger and more advanced tasks. DeepSeek-V2: How does it work? We've got explored DeepSeek’s method to the development of advanced models. On this wave, our place to begin is not to take advantage of the opportunity to make a fast profit, but reasonably to achieve the technical frontier and drive the event of the complete ecosystem … Combination of these improvements helps DeepSeek-V2 obtain particular options that make it even more competitive amongst different open fashions than earlier variations.

It dealt with duties like artistic writing and summarization, generating clear, well-structured responses even for prolonged inputs. Managing extremely long text inputs up to 128,000 tokens. 1,170 B of code tokens have been taken from GitHub and CommonCrawl. The performance of DeepSeek-Coder-V2 on math and code benchmarks. Powered by the groundbreaking DeepSeek-V3 mannequin with over 600B parameters, this state-of-the-art AI leads international standards and matches high-tier international models throughout multiple benchmarks. This leads to raised alignment with human preferences in coding tasks. The preferred, DeepSeek-Coder-V2, stays at the highest in coding duties and might be run with Ollama, making it particularly enticing for indie developers and coders. That decision was certainly fruitful, and now the open-source household of models, together with DeepSeek Coder, DeepSeek LLM, DeepSeekMoE, DeepSeek-Coder-V1.5, DeepSeekMath, Free DeepSeek Chat-VL, DeepSeek-V2, DeepSeek-Coder-V2, and DeepSeek-Prover-V1.5, might be utilized for many purposes and is democratizing the usage of generative fashions. DeepSeek-Coder-V2, costing 20-50x times less than other fashions, represents a big upgrade over the original DeepSeek-Coder, with more extensive coaching information, larger and extra efficient fashions, enhanced context dealing with, and advanced strategies like Fill-In-The-Middle and Reinforcement Learning. Testing DeepSeek-Coder-V2 on various benchmarks reveals that DeepSeek-Coder-V2 outperforms most fashions, including Chinese rivals. ????Up to 67 billion parameters, astonishing in numerous benchmarks.

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