Kedis ku xidhan hagaha · Adag Heerka

Kediska Muunadaha Male-awaalka ah ee Xaqiijinta

Wuxuu tijaabiyaa shaybaar malo-awaal ah oo sax ah, aqbalid iyo sixid ixtimaalka, kharashyada qabyada ah, iyo caqabadaha sheegashada waxqabadkeeda.

Wadooyinka haga ee la xidhiidhaXaqiijinta Muunada Male-awaalka ah
Su'aal 1 ee 8

Waa maxay fikradda udub-dhexaadka ah ee ka dambeysa muunad mala awaal ah?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Waa maxay fikradda udub-dhexaadka ah ee ka dambeysa muunad mala awaal ah?
    • Isticmaal qaab yar oo qabyo ah si aad u soo jeediso calaamado badan oo nashqada weyni si siman u xaqiijiso
    • Tababar hal nooc oo weyn si aad si firfircoon uga boodo lakabyada
    • Horey u kaydi jumlad kasta oo suurtagal ah
    • Ku beddel muunad-qaadista cod-dejin hunguri ah si aad wakhtiga u badbaadiso
  2. Waa maxay sababta shaybaarka mala-awaalka ahi aanu hoos u dhigin tayada wax-soo-saarka?
    • Sababtoo ah qaabka qabyada ah had iyo jeer waa sax
    • Sababtoo ah xeerka hubinta wuxuu ka dhigayaa qaybinta kama dambaysta ah mid la mid ah muunanaynta bartilmaameedka saafiga ah
    • Sababtoo ah waxay kaliya ku shaqeysaa dardargelin gaaban
    • Sababtoo ah waxay celcelis ahaan labada moodal wax soo saarkooda
  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