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DeepSeek 모델은 처음 2023년 하반기에 출시된 후에 빠르게 AI 커뮤니티의 많은 관심을 받으면서 유명세를 탄 편이라고 할 수 있는데요. 허깅페이스 기준으로 지금까지 DeepSeek이 출시한 모델이 48개인데, 2023년 DeepSeek과 비슷한 시기에 설립된 미스트랄AI가 총 15개의 모델을 내놓았고, 2019년에 설립된 독일의 알레프 알파가 6개 모델을 내놓았거든요. 처음에는 Llama 2를 기반으로 다양한 벤치마크에서 주요 모델들을 고르게 앞서나가겠다는 목표로 모델을 개발, 개선하기 시작했습니다. 이렇게 한 번 고르게 높은 성능을 보이는 모델로 기반을 만들어놓은 후, 아주 빠르게 새로운 모델, 개선된 버전을 내놓기 시작했습니다. AI 학계와 업계를 선도하는 미국의 그늘에 가려 아주 큰 관심을 받지는 못하고 있는 것으로 보이지만, 분명한 것은 생성형 AI의 혁신에 중국도 강력한 연구와 스타트업 생태계를 바탕으로 그 역할을 계속해서 확대하고 있고, 특히 중국의 연구자, 개발자, 그리고 스타트업들은 ‘나름의’ 어려운 환경에도 불구하고, ‘모방하는 중국’이라는 통념에 도전하고 있다는 겁니다. DeepSeek의 오픈소스 모델 DeepSeek-V2, 그리고 DeepSeek-Coder-V2 모델은 독자적인 ‘어텐션 메커니즘’과 ‘MoE 기법’을 개발, 활용해서 LLM의 성능을 효율적으로 향상시킨 결과물로 평가받고 있고, 특히 DeepSeek-Coder-V2는 현재 기준 가장 강력한 오픈소스 코딩 모델 중 하나로 알려져 있습니다. 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.

The 7B model makes use of Multi-Head attention (MHA) while the 67B model makes use of Grouped-Query Attention (GQA). Ethical issues and limitations: While DeepSeek-V2.5 represents a significant technological advancement, it also raises necessary moral questions. To run regionally, DeepSeek-V2.5 requires BF16 format setup with 80GB GPUs, with optimal performance achieved utilizing 8 GPUs. LLM v0.6.6 helps DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Although the export controls have been first introduced in 2022, they solely started to have an actual effect in October 2023, and the most recent generation of Nvidia chips has solely not too long ago begun to ship to knowledge centers. Hasn’t the United States limited the variety of Nvidia chips bought to China? The licensing restrictions mirror a growing consciousness of the potential misuse of AI applied sciences. The open-source nature of DeepSeek-V2.5 could speed up innovation and democratize entry to advanced AI applied sciences. DeepSeek-V2.5 was launched on September 6, 2024, and is accessible on Hugging Face with each internet and API entry. DeepSeek V3 is monumental in measurement: 671 billion parameters, or 685 billion on AI dev platform Hugging Face.

Meta announced in mid-January that it could spend as much as $65 billion this yr on AI development. It’s been just a half of a year and DeepSeek AI startup already considerably enhanced their fashions. Its efficiency in benchmarks and third-party evaluations positions it as a robust competitor to proprietary fashions. It might pressure proprietary AI firms to innovate additional or rethink their closed-source approaches. That is all nice to hear, although that doesn’t mean the massive companies out there aren’t massively increasing their datacenter funding in the meantime. There are plenty of frameworks for constructing AI pipelines, but when I wish to combine production-ready finish-to-end search pipelines into my utility, Haystack is my go-to. Why this matters - where e/acc and true accelerationism differ: e/accs think people have a vivid future and are principal brokers in it - and something that stands in the way in which of people utilizing expertise is bad. I think I'll make some little project and doc it on the month-to-month or weekly devlogs till I get a job. But we can make you might have experiences that approximate this.

Aider can connect to nearly any LLM. Aider lets you pair program with LLMs to edit code in your local git repository Start a brand new undertaking or work with an present git repo. The mannequin is optimized for each large-scale inference and small-batch native deployment, enhancing its versatility. The mannequin is optimized for writing, instruction-following, and coding tasks, introducing operate calling capabilities for external device interplay. The model’s mixture of basic language processing and coding capabilities sets a new standard for open-source LLMs. Breakthrough in open-source AI: DeepSeek, a Chinese AI firm, has launched DeepSeek-V2.5, a powerful new open-source language mannequin that combines basic language processing and superior coding capabilities. In K. Inui, J. Jiang, V. Ng, and X. Wan, editors, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the ninth International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5883-5889, Hong Kong, China, Nov. 2019. Association for Computational Linguistics.

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