Kediska Cabbirka Daaqadda Macnaha YaRN

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay nidaamka codaynta ee YaRN ay wax ka beddesho si loo kordhiyo dhererka macnaha guud?
   • Ku-xidhka Booska Wareega (Rotary Position Embeddings)
   • Ku-xidhnaanta sinusoidal oo dhammaystiran
   • Miisaska goobaynta ee la bartay
   • Eexda toosan ee ALiBi
2. Sidee ayay YaRN u daawaysaa soo noqnoqoshada sare iyo cabbirada RoPE-ga hooseeya?
   • Labaduba si isku mid ah ayay u dhexaysaa
   • Waxay gabi ahaanba meesha ka saartaa cabbirada soo noqnoqda
   • Waxa kaliya oo ay wax ka beddeshaa cabbirada soo noqnoqda
   • Waxay ka tagtaa cabbirada soo noqnoqoshada sare inta badan si sax ah waxayna isku xirtaa kuwa soo noqnoqonaya
3. How should YaRN's reported data and fine-tuning efficiency be interpreted?
   • As proof that no YaRN model ever needs training
   • As results for its evaluated model-extension setups, rather than a fixed guarantee for every model
   • As a universal promise that every dataset size works equally well
   • As removal of the need for GPUs or inference computation
4. Marka laga soo tago dib-u-cusboonaynta soo noqnoqoshada, waa maxay hagaajinta dheeraadka ah ee YaRN ay khusayso si ay u ilaaliso feejignaanta fog?
   • Kordhinta waxbarashada
   • Erayada ka weyn
   • Habaynta heerkulka feejignaanta (softmax scaling).
   • Goynta si tartiib ah
5. What motivates evaluating a RoPE model before using a context far beyond its original training length?
   • The model necessarily becomes perfectly accurate at longer lengths
   • The context extension automatically reduces memory to zero
   • A larger window proves every passage is equally easy to retrieve
   • Positional and attention behavior may degrade outside the conditions it learned
6. Mawqifka toosan ee dhexdhexaadka ah ayaa dhibaya awooda YaRN ay doonayso inay ilaaliso?
   • Xiriiro maxalli ah oo wanaagsan oo ka dhexeeya calaamadaha dhow
   • Cabbirka erayada qaabka
   • Xawaaraha calaamadeeyaha
   • Tirada lakabyada
7. What happens in the transition region between YaRN's high- and low-frequency treatments?
   • Every dimension is assigned a random vocabulary token
   • All transition dimensions are deleted from attention
   • The scheme blends original and interpolated frequency treatment across the relevant dimensions
   • One identical scaling rule ignores frequency differences entirely
8. What does a checkpoint's advertised longer YaRN context limit fail to establish by itself?
   • Whether the positional scheme can accept any coordinates at all
   • Reliable performance for every intended task and position within that window
   • The spelling of the model's name
   • A requirement that every inference request retrain all weights