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.
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.
- 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
- 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
- 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