Awọn ile-iṣẹ Itọsọna
DeepSeek V3 ati R1 Idi
DeepSeek-V3 and DeepSeek-R1 are open-weight model releases with different technical contributions.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
Lori iwe yi2 min ka
Akopọ
V3 uses a mixture-of-experts architecture; R1 applies reinforcement learning and a multi-stage post-training pipeline to reasoning. Reported GPU-hour figures describe a specific V3 training run, not the full research cost or the cost to train R1.
Jin Dive
The DeepSeek-V3 report describes a 671B-parameter mixture-of-experts model with 37B activated per token. It reports 2.788 million H800 GPU-hours for the full V3 training run; this compute estimate is not the total cost of the research program and does not state the cost of training R1. DeepSeek-R1 builds on DeepSeek-V3-Base. Its paper describes a multi-stage pipeline: cold-start supervised fine-tuning, reasoning-focused reinforcement learning, rejection sampling and supervised fine-tuning, then a second RL stage. The paper evaluates verifiable tasks such as mathematics, coding competitions, and STEM. It also describes smaller distilled models based on Qwen and Llama families.
Real-World imuse
Inspecting the V3 technical report’s active-versus-total parameter description for its MoE architecture
Comparing R1’s reasoning evaluation on mathematics, coding, and STEM tasks
Studying the stages used to build R1 from a V3 base model
Using smaller R1-distilled models based on Qwen and Llama checkpoints for research
Ọjọ iwaju ti DeepSeek V3 ati Idi R1
DeepSeek’s reports contribute open-weight model artifacts and technical descriptions of MoE training and RL-based reasoning. Results should be tied to each model, checkpoint, benchmark, and training stage. Reported compute estimates are narrower than total research, infrastructure, and data costs.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Ilana olutaja. Awọn maapu opopona olutaja ni ipa kini awọn ẹya ti ẹgbẹ rẹ le kọ ni atẹle.
- Iye owo ati isuna. Awọn ofin iṣowo ati awọn aṣayan imuṣiṣẹ ni ipa lori idiyele igba pipẹ ati eewu.
- Ewu ati ailewu. Awọn imoriya ile-iṣẹ ṣe apẹrẹ awọn abawọn ọja, iduro ailewu, ati ṣiṣi.
Ṣọra fun
- Awọn ikede ifilọlẹ le ju iduroṣinṣin lọ ni awọn iṣan-iṣẹ iṣelọpọ gidi.
- Ifowoleri API tabi awọn iyipada eto imulo le fọ awọn arosinu ni alẹ.
- Igbẹkẹle olutaja ẹyọkan ṣe alekun titiipa-inu ati awọn idiyele ijira.
Next awọn igbesẹ
- Ṣe ayẹwo awọn olupese nipa lilo awọn iṣẹ ṣiṣe tirẹ ati awọn ipilẹ data.
- Ṣe atunyẹwo asiri, aabo, ati awọn ofin ofin ṣaaju iṣọpọ.
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- Bojuto awọn akọsilẹ itusilẹ nitoribẹẹ awọn iyipada maapu oju-ọna ma ṣe iyalẹnu awọn ẹgbẹ.
Tesiwaju Ṣiṣawari
Ìwé ìròyìn ọfẹ
Gba ìkànìyàn AI ojoojúmọ́
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Take the DeepSeek V3 and R1 Reasoning quiz
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Awọn ibeere ti a beere nigbagbogbo
Kini idi ti DeepSeek V3 ati R1 Reasoning?
DeepSeek-V3 and DeepSeek-R1 are open-weight model releases with different technical contributions. V3 uses a mixture-of-experts architecture; R1 applies reinforcement learning and a multi-stage post-training pipeline to reasoning. Reported GPU-hour figures describe a specific V3 training run, not the full research cost or the cost to train R1.
What parameter counts did the DeepSeek-V3 technical report give?
The V3 report describes 671B total parameters and 37B active per token in its MoE architecture.
What was notable about DeepSeek-R1’s reasoning training and release?
The R1 paper describes reinforcement-learning-based reasoning and the project released model weights; the paper does not imply every capability is achieved by RL alone.
What compute figure did the DeepSeek-V3 report give for its full training run?
The V3 report lists 2.788M H800 GPU-hours for the full training run; this is not a full research-cost figure or an estimate for R1 training.
What training signal helped DeepSeek-R1 develop reasoning behaviors?
The R1 report describes RL using verifiable reward signals for reasoning tasks, alongside other stages in the full R1 pipeline.
Kini ilana imuṣiṣẹ kan ti o ni nkan ṣe pẹlu ikẹkọ DeepSeek-V3?
V3 ṣe agbekalẹ awọn ilana bii akiyesi wiwakọ ori-pupọ ati ero iwọntunwọnsi ẹru-ipadanu-ọfẹ lati kọ MOE rẹ daradara.
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