Quiz decodage spekulatif

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

1. How does speculative decoding speed up generation from a large model?
   • Benn modelu projet bu ndaw dafay xalaat ay token yu model bu mag bi di xool ci paralel
   • kompresse model bi ci genn-wàllu dayoom ba fàww
   • Di dindi jumtukaayu bàyyi xel
   • Taggat model bi ci yeneen done
2. For a large model at low batch size, why can one-token autoregressive decoding be slow?
   • Each token requires internet access
   • Repeated reads of large model weights can make decoding memory-bandwidth bound
   • The model permanently stores a full copy of every possible output
   • The tokenizer performs the model’s matrix multiplications
3. In greedy speculative decoding, what happens at the first token where draft and target predictions disagree?
   • The whole conversation is thrown away and restarted from scratch
   • Xeetu projet bi dañu koy tàggataat ci saasi
   • The matching prefix is kept, the target token is used at the mismatch, and later draft tokens are discarded
   • Kalite biy génne dafay yàqu ba fàww
4. Under exact speculative sampling, how does the output distribution compare with sampling from the target model alone?
   • It is always lower quality but faster
   • It is identical only when the draft model is larger
   • It becomes deterministic regardless of target sampling
   • It is identical in distribution
5. Lan mooy gëna mëna wax ci gaawaayu dekodage speculatif bi?
   • Njëg li ñuy nangu ci xalaati modelu projet bi
   • Kulòoru GPU bi
   • Guddaayig tur model bi
   • Waxtu ci bis bi
6. Lan la Medusa ak EAGLE bokk ci seeni jegewaale?
   • Dañu soxla jumtukaayi quantum
   • Dañu baña soxla beneen xeetu projet bu nekk ci boppam
   • Dañuy efaase model biñ bëgga
   • Ci nataal kese lañuy liggéey
7. What should a serving team measure before claiming speculative decoding will speed up its workload?
   • Only the target model’s parameter count
   • Only whether the draft produces fluent text
   • Draft cost, target verification cost, accepted tokens and end-to-end latency under representative traffic
   • The number of tokenizer files
8. In exact speculative sampling, what happens when a proposed token is rejected?
   • The whole conversation is discarded
   • The target model is permanently changed
   • The rejected token is kept without verification
   • A correction step samples from the distribution needed to preserve the target model’s output distribution