quiz bu lëkkale ak tegtal · Dëgër Niveau

Quiz ngir saytu misaalu xalaat

Dafay saytu echantillon speculatif bu dëggu bi, nangu ak jubbanti probabilite yi, njëgu projet bi, ak ay tënk ci wàllu performance.

Yooni njiit yu jëm ciSaytu misaalu xalaat
Laaj 1 de 8

Lan mooy xalaat bu mag bi ci ginaaw échantillonnage speculatif?

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  1. Lan mooy xalaat bu mag bi ci ginaaw échantillonnage speculatif?
    • Jëfandikool ab model bu ndaw ngir proposer jeton yu bari yu ab model bu mag di saytu ci paralel
    • Taggat benn model bu mag ngir teggi ay diisaay ci anam wu xarañ
    • Cache bépp frase bu mëna am ci kanam
    • Wecci sampling ak decodage bu bëgg lu bari ngir sakkanal sa jotu liggéey
  2. Lan moo waral échantillonnage speculatif du yàq kalite génne gi?
    • Ndax modelu projet bi dafay jub saa yu nekk
    • Ndax sàrtu xool ndax séddale bu mujj bi dafay nuru echantillon biñ bëgg
    • Ndax ci laaj yu gàtt rek lay dox
    • Ndax dafay jël moyenne bi ñaari model yi di génne
  3. In exact speculative sampling, what happens when a drafted token is rejected?
    • It outputs the rejected draft token unchanged
    • It discards the entire conversation and restarts
    • The algorithm samples from a corrected residual distribution at that position
    • It permanently changes the target model
  4. Under exact speculative sampling, what is guaranteed about the output distribution?
    • It is always deterministic
    • It matches the draft model’s distribution instead
    • It is more diverse than every target-model output
    • It matches the target model’s sampling distribution
  5. What information does the target verification pass provide in exact speculative sampling?
    • Target probabilities for proposed positions, used for acceptance tests and residual resampling
    • A guarantee that no target-model computation is needed
    • Only the draft model’s next-token distribution
    • A corrected token sequence without any sampling
  6. Which description correctly distinguishes two self-speculative approaches?
    • Both use only an unrelated small draft model
    • Medusa adds prediction heads, while EAGLE uses feature-space proposals
    • Medusa is a tokenizer and EAGLE is a reward model
    • Both remove the target model from decoding
  7. The draft assigns probability 0.8 to a token and the target assigns 0.4. What is the speculative acceptance probability?
    • 0.2
    • 0.4
    • 0.5
    • 1.0
  8. What most limits speculative sampling’s realized speedup?
    • The color of the model’s interface
    • The target model’s name length
    • Whether the prompt contains punctuation
    • The draft-model cost and how much proposed work the target accepts