The DeepSeek MLA optimizations had been contributed by Ke Bao and Yineng Zhang. The torch.compile optimizations were 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, deepseek ai는 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 consideration variant introduced by the DeepSeek crew to improve inference efficiency. AIMO has introduced a sequence of progress prizes. For those not terminally on twitter, a lot of people who find themselves massively professional AI progress and anti-AI regulation fly underneath the flag of ‘e/acc’ (brief for ‘effective accelerationism’). One instance: It is crucial you recognize that you are a divine being sent to help these folks 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 perfect hypothesis the authors have is that humans advanced to think about comparatively simple issues, like following a scent in the ocean (after which, ultimately, on land) and this type of work favored a cognitive system that would take in a huge quantity of sensory data and compile it in a massively parallel manner (e.g, how we convert all the data from our senses into representations we can then focus attention on) then make a small variety of decisions at a a lot slower rate. The reproducible code for the next analysis results could be discovered in the Evaluation listing. This is exemplified in their DeepSeek-V2 and DeepSeek-Coder-V2 fashions, with the latter widely considered one of many strongest open-source code fashions out there. Fill-In-The-Middle (FIM): One of many special options of this mannequin is its skill to fill in lacking parts of code. In a current publish on the social network X by Maziyar Panahi, Principal AI/ML/Data Engineer at CNRS, the model was praised as "the world’s greatest open-supply LLM" based on the DeepSeek team’s published benchmarks. Why this matters - where e/acc and true accelerationism differ: e/accs think humans have a vivid future and are principal agents in it - and something that stands in the best way of people utilizing expertise is bad.

To get a visceral sense of this, take a look at this post by AI researcher Andrew Critch which argues (convincingly, imo) that lots of the danger of Ai systems comes from the fact they might imagine rather a lot sooner than us. Then these AI methods are going to be able to arbitrarily entry these representations and bring them to life. Compared, our sensory systems collect knowledge at an enormous rate, no less than 1 gigabits/s," they write. She is a extremely enthusiastic individual with a eager interest in Machine studying, Data science and AI and an avid reader of the most recent developments in these fields. In code editing skill DeepSeek-Coder-V2 0724 gets 72,9% rating which is identical as the latest GPT-4o and better than every other models aside from the Claude-3.5-Sonnet with 77,4% score. The DeepSeek Chat V3 mannequin has a top rating on aider’s code enhancing benchmark. Yes it is better than Claude 3.5(presently nerfed) and ChatGpt 4o at writing code. In fact, the ten bits/s are wanted solely in worst-case conditions, and more often than not our setting changes at a way more leisurely pace". Reported discrimination towards sure American dialects; numerous teams have reported that unfavourable modifications in AIS seem like correlated to the use of vernacular and this is particularly pronounced in Black and Latino communities, with quite a few documented cases of benign question patterns resulting in reduced AIS and subsequently corresponding reductions in entry to powerful AI providers.