The Hidden Gem Of Deepseek Chatgpt

‘장기적인 관점에서 현재의 생성형 AI 기술을 바탕으로 AGI로 가는 길을 찾아보겠다’는 꿈이 엿보이는 듯합니다. 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. DeepSeek 모델은 처음 2023년 하반기에 출시된 후에 빠르게 AI 커뮤니티의 많은 관심을 받으면서 유명세를 탄 편이라고 할 수 있는데요. 대부분의 오픈소스 비전-언어 모델이 ‘Instruction Tuning’에 집중하는 것과 달리, 시각-언어데이터를 활용해서 Pretraining (사전 훈련)에 더 많은 자원을 투입하고, 고해상도/저해상도 이미지를 처리하는 두 개의 비전 인코더를 사용하는 하이브리드 비전 인코더 (Hybrid Vision Encoder) 구조를 도입해서 성능과 효율성의 차별화를 꾀했습니다. 특히 DeepSeek-V2는 더 적은 메모리를 사용하면서도 더 빠르게 정보를 처리하는 또 하나의 혁신적 기법, MLA (Multi-Head Latent Attention)을 도입했습니다. DeepSeek-V2에서 도입한 MLA라는 구조는 이 어텐션 메커니즘을 변형해서 KV 캐시를 아주 작게 압축할 수 있게 한 거고, 그 결과 모델이 정확성을 유지하면서도 정보를 훨씬 빠르게, 더 적은 메모리를 가지고 처리할 수 있게 되는 거죠. 이렇게 하면, 모델이 데이터의 다양한 측면을 좀 더 효과적으로 처리할 수 있어서, 대규모 작업의 효율성, 확장성이 개선되죠. 트랜스포머에서는 ‘어텐션 메커니즘’을 사용해서 모델이 입력 텍스트에서 가장 ‘유의미한’ - 관련성이 높은 - 부분에 집중할 수 있게 하죠.

특히, DeepSeek만의 혁신적인 MoE 기법, 그리고 MLA (Multi-Head Latent Attention) 구조를 통해서 높은 성능과 효율을 동시에 잡아, 향후 주시할 만한 AI 모델 개발의 사례로 인식되고 있습니다. Besides, the model makes use of some new methods akin to Multi-Head Latent Attention (MLA) and an auxiliary-loss-free load balancing methodology to enhance effectivity and lower prices for coaching and deployment. As talked about above, the DeepSeek-V3 makes use of MLA for optimum memory usage and inference performance. Moreover, DeepSeek-V3 can process up to 128,000 tokens in a single context, and this long-context understanding offers it a aggressive edge in areas like authorized document evaluate and educational research. Huawei will now be restricted to the logic chips that its domestic logic chip manufacturing companion, SMIC, can produce, as well as either legally acquired HBM2 or smuggled supplies of HBM3e. The slowing sales of H20s appeared to suggest that native opponents have been becoming extra engaging than Nvidia’s degraded chips for the Chinese market. The model is built on NVIDIA H800 chips, a lower-efficiency but extra cost-effective different to H100 chips that has been designed for restricted markets like China.

US export controls have restricted China’s entry to superior NVIDIA AI chips, with an aim to contain its AI progress. And one of the info about COCOM, which was the Cold War period export controls multilateral arrangement - one of the details that was for a long time labeled however has since been declassified is that it actually was born because the financial adjunct of NATO. It needs to be noted that traditional fashions predict one word at a time. AI firm’s world competitiveness by limiting their chip gross sales abroad, however will take some time and sturdy enforcement to be effective, given that it has a 120-day comment interval and difficult enforcement. DeepSeek-AI has offered multiple methods for users to take advantage of DeepSeek-V2.5. DeepSeek-AI continues to refine and expand its AI fashions, so DeepSeek-V2.5 represents a big step forward. Since its inception, DeepSeek-AI has been identified for producing highly effective models tailor-made to fulfill the rising wants of builders and non-builders alike. Chinese LLM builders are more likely to rapidly optimize DeepSeek’s innovations and deploy them at a pace that poses a severe challenge to U.S. This also exhibits how open-supply AI could continue to problem closed model developers like OpenAI and Anthropic.

In the case of limitations, the DeepSeek-V3 may need significant computational assets. DeepSeek AI-V3 is trained on 14.8 trillion tokens which includes vast, high-quality datasets to supply broader understanding of language and job-specific capabilities. These fashions should not simply extra environment friendly-they are additionally paving the best way for broader AI adoption throughout industries. This mixture permits DeepSeek-V2.5 to cater to a broader viewers whereas delivering enhanced performance throughout various use instances. DeepSeek-V2.5 builds on the success of its predecessors by integrating the best options of DeepSeekV2-Chat, which was optimized for conversational duties, and DeepSeek-Coder-V2-Instruct, identified for its prowess in producing and understanding code. This means the model has been optimized to comply with instructions extra precisely and provide more related and coherent responses. Similarly, within the HumanEval Python test, the model improved its rating from 84.5 to 89. These metrics are a testomony to the significant developments typically-goal reasoning, coding abilities, and human-aligned responses. In essence, MoE models are like a group of specialist fashions working together to answer a query. In relation to arithmetic and coding, the model outperformed its rivals in benchmarks like MATH-500 and LiveCodeBench. The addition of the mannequin comes at the identical time as DeepSeek's being scrutinized for how it trained its models.

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