Kediska Meesha Wareega ee Isku-xidhka

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Sidee buu RoPE ugu duraa macluumaadka booska beddelka?
   • Iyadoo la beddelayo lammaane cabbirro ah oo ku jira weydiinta iyo fiilooyinka muhiimka ah iyadoo loo eegayo xagal ku tiirsan booska
   • Adigoo ku daraya vector go'an ee ku-xidhka gelinta
   • Adigoo dib u habaynaya calaamadaha si aan kala sooc lahayn
   • Adigoo ku dhejinaya lambarka booska qoraal ahaan
2. Ka dib marka la isticmaalo RoPE, dhibcaha dareenka ee u dhexeeya laba calaamadood waxay ku xiran tahay ugu horreyn:
   • Mawqifkooda saxda ah kaliya
   • Masaafada u dhaxaysa (m laga jaray n)
   • Cabbirka ereyada
   • Cabbirka dufcadda
3. What training-efficiency comparison did the YaRN paper report for context extension?
   • Exactly the same data and steps as all prior methods
   • No fine-tuning or training data of any kind
   • About 10x fewer tokens and 2.5x fewer training steps than prior methods
   • A fixed 100x increase in output tokens
4. Waa maxay sababta lammaaneyaasha cabbirka kala duwan ay ugu wareegaan inta jeer ee RoPE?
   • Si loo badbaadiyo xusuusta adigoo ka boodaya cabbirrada qaarkood
   • Si aan kala sooc lahayn wax soo saarka moodeelka
   • Sababtoo ah dhammaan lammaanuhu waa inay isticmaalaan xagal isku mid ah
   • Si aad u qabsato nidaamka deegaanka ee wanaagsan iyo booska fogaanta labadaba
5. What is the goal of techniques such as Position Interpolation and YaRN for RoPE-based models?
   • Extend the usable context length beyond the original training window using additional scaling/fine-tuning methods
   • Guarantee accurate recall at any sequence length with no evaluation
   • Remove position information from attention
   • Replace all token embeddings with absolute learned vectors
6. Does standard RoPE require learned absolute-position vectors to inject position into query and key representations?
   • Yes; it adds one learned vector for every possible sequence position
   • No; it applies position-dependent rotations to paired query/key dimensions
   • It removes query and key vectors entirely
   • It only adds position to value vectors
7. What does standard RoPE alone guarantee about a model’s context length?
   • Unlimited accurate context at every position
   • A fixed context of exactly 128 tokens
   • It does not by itself guarantee reliable use beyond the trained context length
   • Removal of the attention mechanism
8. What extended context length did the Position Interpolation paper report for its LLaMA evaluations?
   • Exactly 1,000 tokens with no position changes
   • Unlimited context with no fine-tuning
   • Up to 2 million tokens for all models
   • Up to 32,768 tokens with minimal fine-tuning