Maswali ya Kusimbua ya Kukisia

Majibu na maelezo huonekana unapojibu maswali.

1. How does speculative decoding speed up generation from a large model?
   • Muundo mdogo wa rasimu hukisia ishara kadhaa ambazo muundo mkubwa huthibitisha kwa sambamba
   • Kukandamiza mfano hadi nusu ya ukubwa wake kwa kudumu
   • Kuondoa utaratibu wa tahadhari
   • Kufundisha mfano juu ya data zaidi
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
   • Mfano wa rasimu hufunzwa tena mara moja
   • The matching prefix is kept, the target token is used at the mismatch, and later draft tokens are discarded
   • Ubora wa pato huharibika kabisa
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. Ni nini huamua moja kwa moja kasi kutoka kwa usimbaji wa kubahatisha?
   • Kiwango cha kukubalika cha makadirio ya muundo wa rasimu
   • Rangi ya GPU
   • Urefu wa jina la mfano
   • Wakati wa siku
6. Je, Medusa na EAGLE wanafanana nini kuhusu mbinu?
   • Wanahitaji vifaa vya quantum
   • Wanaondoa hitaji la muundo tofauti wa rasimu iliyojitegemea
   • Wanafuta mfano wa lengo
   • Wanafanya kazi kwenye picha tu
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