Raadinta Kediska Go'aaminta

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Maxaa ka sooca madax-dejinta iyo dejinta koodka caadiga ah?
   • Waxay u baahan tahay laba nooc oo si gaar ah u tababaran
   • Ma isticmaasho qaab qabyo ah oo gooni ah - kaliya qaabka la beegsanayo laftiisa
   • Waxay u shaqeysaa oo kaliya hawlaha turjumaada
   • Waxay si joogto ah u cabbiraysaa moodalka
2. Qaabkee tirsiyeedkee ayaa hoosta ka xarriiqaya go'aan-dejinta?
   • Soo degid tartiib ah
   • Fourier transformer
   • K-macnaheedu waa kooxaysi
   • Jacobi (meel go'an) soo noqnoqda
3. Waa maxay labada laamood ee is barbar socda ee ku socda tallaabo kasta?
   • Laan tababar iyo laan imtixaan
   • Cod-bixiye iyo cod-bixiye
   • Laan madax eegid iyo laan xaqiijin ah
   • Qaab qabyo ah iyo qaab bartilmaameed ah
4. Maxaa lagu kaydiyaa ' barkadda n-gram'?
   • Taxanayaal calaamado badan oo macquul ah oo la arkay inta lagu guda jiro isbarbardhigga
   • Taariikhda sheekeysiga isticmaalaha
   • Miisaanka modelka
   • Maaskarada dareenka
5. What supports correctness when lookahead decoding commits a guessed n-gram prefix?
   • Using the same model makes every unverified guess correct
   • Every candidate in the n-gram pool must be committed
   • Verification against the target's greedy predictions or an appropriate distribution-preserving sampling procedure
   • A larger window proves the generated statements are true
6. What resource tradeoff is important when deploying lookahead decoding on already busy hardware?
   • Candidate generation uses no compute or memory
   • Additional parallel work may offset the benefit of fewer sequential passes
   • Every larger window must lower wall-clock latency
   • The target weights are automatically compressed to zero size
7. Why should a lookahead candidate remain provisional until target verification?
   • Parallel iteration guesses can differ from the target's valid continuation
   • Every cached n-gram has already been proven correct by storage alone
   • The lookahead branch permanently changes the target weights
   • Verification is needed only to determine the output filename
8. Does a standard training-free lookahead iteration update the target model's learned weights?
   • Yes; each candidate applies a gradient update to every parameter
   • Yes; verified n-grams become a new training dataset immediately
   • Only the tokenizer's vocabulary is retrained at every step
   • No; it changes candidate token states and verifies outputs while keeping the model weights fixed