DeepSeek V3 і R1 Тест міркування

Відповіді та пояснення з’являються під час виконання тесту.

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. Яка ефективна техніка пов’язана з навчанням DeepSeek-V3?
   • Прихована увага кількох головок і допоміжне балансування навантаження без втрат
   • Друк моделі на папері
   • Видалення всіх шарів уваги
   • Навчання на одному прикладі
6. Which types of tasks did the DeepSeek-R1 paper evaluate?
   • Тільки класифікація зображень
   • 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