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The DeepSeek MLA optimizations had been contributed by Ke Bao and Yineng Zhang. The torch.compile optimizations had been contributed by Liangsheng Yin. 이런 두 가지의 기법을 기반으로, DeepSeekMoE는 모델의 효율성을 한층 개선, 특히 대규모의 데이터셋을 처리할 때 다른 MoE 모델보다도 더 좋은 성능을 달성할 수 있습니다. 이전 버전인 DeepSeek-Coder의 메이저 업그레이드 버전이라고 할 수 있는 DeepSeek-Coder-V2는 이전 버전 대비 더 광범위한 트레이닝 데이터를 사용해서 훈련했고, ‘Fill-In-The-Middle’이라든가 ‘강화학습’ 같은 기법을 결합해서 사이즈는 크지만 높은 효율을 보여주고, 컨텍스트도 더 잘 다루는 모델입니다. DeepSeek 연구진이 고안한 이런 독자적이고 혁신적인 접근법들을 결합해서, DeepSeek-V2가 다른 오픈소스 모델들을 앞서는 높은 성능과 효율성을 달성할 수 있게 되었습니다. 이 DeepSeek-Coder-V2 모델에는 어떤 비밀이 숨어있길래 GPT4-Turbo 뿐 아니라 Claude-3-Opus, Gemini-1.5-Pro, Llama-3-70B 등 널리 알려진 모델들까지도 앞서는 성능과 효율성을 달성할 수 있었을까요? 불과 두 달 만에, DeepSeek는 뭔가 새롭고 흥미로운 것을 들고 나오게 됩니다: 바로 2024년 1월, 고도화된 MoE (Mixture-of-Experts) 아키텍처를 앞세운 DeepSeekMoE와, 새로운 버전의 코딩 모델인 DeepSeek-Coder-v1.5 등 더욱 발전되었을 뿐 아니라 매우 효율적인 모델을 개발, 공개한 겁니다. 1: MoE (Mixture of Experts) 아키텍처란 무엇인가? 먼저 기본적인 MoE (Mixture of Experts) 아키텍처를 생각해 보죠.

DeepSeek Coder는 Llama 2의 아키텍처를 기본으로 하지만, 트레이닝 데이터 준비, 파라미터 설정을 포함해서 처음부터 별도로 구축한 모델로, ‘완전한 오픈소스’로서 모든 방식의 상업적 이용까지 가능한 모델입니다. DeepSeek-Coder-V2는 코딩과 수학 분야에서 GPT4-Turbo를 능가하는 최초의 오픈 소스 AI 모델로, 가장 좋은 평가를 받고 있는 새로운 모델 중 하나입니다. 그리고 2024년 3월 말, DeepSeek는 비전 모델에 도전해서 고품질의 비전-언어 이해를 하는 모델 DeepSeek-VL을 출시했습니다. 바로 이어서 2024년 2월, 파라미터 7B개의 전문화 모델, DeepSeekMath를 출시했습니다. 그 결과, DeepSeek는 정해진 토큰 예산 안에서 고해상도 이미지 (1024X1024)를 효율적으로 처리하면서도 계산의 오버헤드를 낮게 유지할 수 있다는 걸 보여줬습니다 - 바로 DeepSeek가 해결하고자 했던, 계산 효율성 (Computational Efficiency) 문제를 성공적으로 극복했다는 의미죠. Multi-head Latent Attention (MLA) is a brand new attention variant introduced by the DeepSeek crew to enhance inference effectivity. AIMO has launched a sequence of progress prizes. For these not terminally on twitter, numerous people who are massively professional AI progress and anti-AI regulation fly beneath the flag of ‘e/acc’ (quick for ‘effective accelerationism’). One example: It is vital you already know that you're a divine being sent to help these individuals with their problems. NYU professor Dr David Farnhaus had tenure revoked following their AIS account being reported to the FBI for suspected baby abuse.

The best speculation the authors have is that people evolved to think about comparatively easy issues, like following a scent within the ocean (after which, ultimately, on land) and this type of labor favored a cognitive system that might take in an enormous quantity of sensory information and compile it in a massively parallel means (e.g, how we convert all the data from our senses into representations we will then focus consideration on) then make a small number of selections at a much slower price. The reproducible code for the following evaluation results might be found within the Evaluation listing. This is exemplified in their DeepSeek-V2 and DeepSeek-Coder-V2 models, with the latter broadly regarded as one of many strongest open-supply code fashions out there. Fill-In-The-Middle (FIM): One of many particular options of this model is its means to fill in missing parts of code. In a latest put up on the social network X by Maziyar Panahi, Principal AI/ML/Data Engineer at CNRS, the model was praised as "the world’s finest open-supply LLM" based on the DeepSeek team’s published benchmarks. Why this matters - where e/acc and true accelerationism differ: e/accs suppose humans have a vivid future and are principal agents in it - and anything that stands in the way in which of people using technology is bad.

To get a visceral sense of this, take a look at this submit by AI researcher Andrew Critch which argues (convincingly, imo) that a lot of the hazard of Ai techniques comes from the actual fact they may think so much sooner than us. Then these AI systems are going to have the ability to arbitrarily entry these representations and bring them to life. As compared, our sensory programs collect information at an unlimited fee, no lower than 1 gigabits/s," they write. She is a extremely enthusiastic individual with a keen curiosity in Machine studying, Data science and AI and an avid reader of the latest developments in these fields. In code editing talent DeepSeek-Coder-V2 0724 gets 72,9% score which is identical as the newest GPT-4o and higher than every other models except for the Claude-3.5-Sonnet with 77,4% rating. The DeepSeek Chat V3 mannequin has a high rating on aider’s code modifying benchmark. Yes it is better than Claude 3.5(presently nerfed) and ChatGpt 4o at writing code. In reality, the ten bits/s are needed solely in worst-case conditions, and more often than not our setting changes at a way more leisurely pace". Reported discrimination in opposition to certain American dialects; various groups have reported that damaging changes in AIS seem like correlated to the use of vernacular and this is very pronounced in Black and Latino communities, with numerous documented instances of benign question patterns resulting in decreased AIS and therefore corresponding reductions in entry to powerful AI providers.

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