Guide-yakabatana mibvunzo · Zvakaoma Nhanho

Yekufungidzira Sampling Verification Quiz

Miedzo chaiyo yekufungidzira sampling, kubvuma uye kururamisa zvingangoitika, kudhirowa mari, uye zvimhingamipinyi pane ayo maitiro ekuita.

Nzira dzekutungamira dzinoenderanaKufungidzira Sampling Verification
Mubvunzo 1 ye 8

Ndeipi pfungwa huru kumashure kwekufungidzira sampling?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Ndeipi pfungwa huru kumashure kwekufungidzira sampling?
    • Shandisa diki dhizaini modhi kuratidza akawanda tokeni ayo hombe modhi inosimbisa mukufambirana
    • Dzidzisa imwe hofori modhi kusvetuka zvidimbu zvine simba
    • Chengetedza mutsara wese unobvira mberi
    • Tsiva sampling nekukara decoding kuchengetedza nguva
  2. Sei kufungidzira sampling kusingaderedze kunaka kwekubuda?
    • Nekuti dhizaini modhi inogara yakarurama
    • Nekuti mutemo wekusimbisa unoita kuti kugovera kwekupedzisira kufanane nekwakachena kutariswa sampling
    • Nekuti inoshanda chete pane pfupi kurudzira
    • Nekuti avhareji yemhando mbiri 'zvakabuda
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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