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.
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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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