The DeepSeek app immediately zoomed to the highest of the Apple app retailer, where it attracted big numbers of customers who had been clearly unfazed by the fact that the terms and circumstances and the privacy coverage they wanted to just accept had been in Chinese. DeepSeek-Coder-V2 모델은 컴파일러와 테스트 케이스의 피드백을 활용하는 GRPO (Group Relative Policy Optimization), 코더를 파인튜닝하는 학습된 리워드 모델 등을 포함해서 ‘정교한 강화학습’ 기법을 활용합니다. ‘코드 편집’ 능력에서는 DeepSeek-Coder-V2 0724 모델이 최신의 GPT-4o 모델과 동등하고 Claude-3.5-Sonnet의 77.4%에만 살짝 뒤지는 72.9%를 기록했습니다. 트랜스포머에서는 ‘어텐션 메커니즘’을 사용해서 모델이 입력 텍스트에서 가장 ‘유의미한’ – 관련성이 높은 – 부분에 집중할 수 있게 하죠. 236B 모델은 210억 개의 활성 파라미터를 포함하는 DeepSeek의 MoE 기법을 활용해서, 큰 사이즈에도 불구하고 모델이 빠르고 효율적입니다. 소스 코드 60%, 수학 코퍼스 (말뭉치) 10%, 자연어 30%의 비중으로 학습했는데, 약 1조 2천억 개의 코드 토큰은 깃허브와 CommonCrawl로부터 수집했다고 합니다. DeepSeek-Coder-V2는 이전 버전 모델에 비교해서 6조 개의 토큰을 추가해서 트레이닝 데이터를 대폭 확충, 총 10조 2천억 개의 토큰으로 학습했습니다. DeepSeek-Coder-V2는 총 338개의 프로그래밍 언어를 지원합니다. DeepSeek-Coder-V2는 컨텍스트 길이를 16,000개에서 128,000개로 확장, 훨씬 더 크고 복잡한 프로젝트도 작업할 수 있습니다 – 즉, 더 광범위한 코드 베이스를 더 잘 이해하고 관리할 수 있습니다.
우리나라의 LLM 스타트업들도, ديب سيك 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. 예를 들어 중간에 누락된 코드가 있는 경우, 이 모델은 주변의 코드를 기반으로 어떤 내용이 빈 곳에 들어가야 하는지 예측할 수 있습니다. DeepSeek 연구진이 고안한 이런 독자적이고 혁신적인 접근법들을 결합해서, DeepSeek-V2가 다른 오픈소스 모델들을 앞서는 높은 성능과 효율성을 달성할 수 있게 되었습니다. 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. The fashions are roughly based on Facebook’s LLaMa household of models, though they’ve changed the cosine studying fee scheduler with a multi-step learning price scheduler. Interestingly, I have been hearing about some extra new fashions which can be coming soon. What appears possible is that gains from pure scaling of pre-training seem to have stopped, which implies that we have managed to include as a lot information into the models per dimension as we made them larger and threw more knowledge at them than we have been able to up to now. Models are pre-educated utilizing 1.8T tokens and a 4K window measurement on this step.
In lots of functions, we may additional constrain the structure utilizing a JSON schema, which specifies the kind of every subject in a JSON object and is adopted as a potential output format for GPT-4 within the OpenAI API. Since May 2024, now we have been witnessing the event and success of DeepSeek-V2 and DeepSeek-Coder-V2 fashions. This situation has led to blended reactions, with some analysts suggesting that the market’s response could also be an overreaction, given the continued excessive demand for AI expertise, which is able to still require substantial infrastructure. For engineering-related duties, while DeepSeek-V3 performs barely below Claude-Sonnet-3.5, it still outpaces all other models by a big margin, demonstrating its competitiveness throughout numerous technical benchmarks. In each eval the individual duties done can seem human degree, however in any real world job they’re nonetheless fairly far behind. The CodeUpdateArena benchmark represents an essential step forward in assessing the capabilities of LLMs within the code generation area, and the insights from this analysis may also help drive the event of extra robust and adaptable fashions that may keep tempo with the rapidly evolving software program landscape.
The strategy to interpret both discussions should be grounded in the truth that the DeepSeek V3 model is extraordinarily good on a per-FLOP comparability to peer fashions (seemingly even some closed API models, extra on this under). But, like many fashions, it confronted challenges in computational efficiency and scalability. This means they efficiently overcame the previous challenges in computational effectivity! In January 2024, this resulted within the creation of extra superior and environment friendly fashions like DeepSeekMoE, which featured a complicated Mixture-of-Experts structure, and a brand new model of their Coder, DeepSeek-Coder-v1.5. More data: DeepSeek-V2: A robust, Economical, and Efficient Mixture-of-Experts Language Model (DeepSeek, GitHub). As well as the company stated it had expanded its assets too quickly leading to similar buying and selling strategies that made operations harder. Rich folks can choose to spend more money on medical services with a view to receive higher care. However, with 22B parameters and a non-production license, it requires fairly a little bit of VRAM and might solely be used for analysis and testing purposes, so it might not be the very best match for each day local utilization. Welcome to Import AI, a e-newsletter about AI research. Based on a qualitative evaluation of fifteen case research presented at a 2022 conference, this analysis examines tendencies involving unethical partnerships, policies, and practices in contemporary global health.