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DeepSeek V3 ve R1 Muhakeme Testi

DeepSeek-V3 ve R1'in MoE mimarisini, rapor edilen eğitim hesaplamasını, RL ardışık düzenini ve orijinal raporlardan damıtılmış sürümleri test edin.

İlgili kılavuz yollarıDeepseek V3 ve R1 Gerekçesi
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What parameter counts did the DeepSeek-V3 technical report give?

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Testi çözdüğünüzde cevaplar ve açıklamalar görünür.

  1. What parameter counts did the DeepSeek-V3 technical report give?
    • 671B total parameters, with 37B activated per token
    • 37B total parameters, all active per token
    • 671B active parameters for every token
    • A dense model with no routed experts
  2. What was notable about DeepSeek-R1’s reasoning training and release?
    • It used no training data
    • The report describes RL-based reasoning and publicly released model weights
    • It was only a closed cloud API
    • It was a rule-based program with no learned model
  3. What compute figure did the DeepSeek-V3 report give for its full training run?
    • 2.788 thousand CPU-hours
    • 2.788 million H100 GPU-hours for R1 only
    • 2.788 million H800 GPU-hours
    • No accelerator-hours were reported
  4. What training signal helped DeepSeek-R1 develop reasoning behaviors?
    • Only manually written rules with no learned model
    • Image-label classification
    • Random rewards unrelated to answers
    • Reinforcement learning rewards for correct, verifiable outcomes
  5. DeepSeek-V3'ün eğitimiyle ilişkili verimlilik tekniklerinden biri nedir?
    • Çok kafalı gizli dikkat ve yardımcı kayıpsız yük dengeleme
    • Modeli kağıda yazdırma
    • Tüm dikkat katmanlarının kaldırılması
    • Tek bir örnek üzerinde eğitim
  6. Which types of tasks did the DeepSeek-R1 paper evaluate?
    • Yalnızca görüntü sınıflandırması
    • Mathematics, coding competitions, and STEM tasks
    • Only speech transcription
    • Only spreadsheet formatting
  7. What supervised-learning step does the DeepSeek-R1 pipeline use before its first RL stage?
    • A period of training with no text or prompts
    • A final model-compression step only
    • Cold-start supervised fine-tuning on curated reasoning examples
    • A computer-vision pretraining stage
  8. Which model families were used as bases for the released R1-distilled models?
    • BERT and CLIP
    • Tacotron and WavLM
    • NeRF and Instant-NGP
    • Qwen and Llama