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.
Mubvunzo 1 ye 8
Ndeipi pfungwa huru kumashure kwekufungidzira sampling?
Tarisa mubvunzo wese8 mibvunzo
Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.
- 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
- 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
- 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