Mfano wa Maswali ya Kuhesabu

Majibu na maelezo huonekana unapojibu maswali.

1. Kuhesabu mfano kunapunguza nini?
   • Usahihi wa nambari unaotumika kuhifadhi uzani na kuwezesha
   • Idadi ya tabaka kwenye mtandao
   • Seti ya data ya mafunzo ambayo modeli ilijengwa juu yake
   • Lugha ambayo mwanamitindo anaweza kuelewa
2. Ignoring scale metadata and other runtime tensors, what is the weight-payload ratio from sixteen bits to four bits per weight?
   • The payload is unchanged
   • It is one hundredth of the original payload
   • The four-bit payload is one quarter of the sixteen-bit payload
   • The payload becomes four times larger
3. Je, ni upande gani kuu wa quantization ya kiwango cha chini cha fujo?
   • Mfano husahau lugha yake ya mafunzo
   • Upotevu unaowezekana wa usahihi kadri thamani zinavyobanwa kwenye ndoo chache
   • Inaweza tu kukimbia kwenye kompyuta kubwa
   • Inafuta tabaka kabisa
4. Je, mafunzo ya kufahamu quantization yanatofautiana vipi na upimaji wa baada ya mafunzo?
   • Haihitaji data hata kidogo
   • Inafanya kazi kwenye picha tu
   • Inaongeza idadi ya bits kwa muda
   • Inaiga mzunguko wakati wa mafunzo ili mtindo ujifunze kuvumilia usahihi wa chini
5. Ni mbinu gani hutumia ujanibishaji wa biti 4 kufanya usanifu mzuri wa miundo mikubwa inayoweza kumudu kwenye GPU moja?
   • Kuacha
   • Urekebishaji wa kundi
   • QLoRA
   • Uwekaji alama
6. Kwa nini kuongeza kwa kila kituo au kwa kila kikundi husaidia miundo iliyopimwa?
   • Inapeana vipengele tofauti vya mizani kwa vipande vya matrix ya uzani ili kuhifadhi usahihi
   • Huondoa hitaji la uzani wowote
   • Inafanya mfano kuwa mkubwa
   • Inabadilisha maandishi kuwa sauti
7. A four-bit model’s raw weights fit in available VRAM, but serving fails out of memory. What could explain this?
   • Every four-bit weight expands permanently to a gigabyte
   • Quantization guarantees no runtime allocations
   • Only the model’s filename consumes the remaining memory
   • Caches, scales, unquantized tensors, or workspace need additional memory
8. A team quantizes to INT8 but sees no inference speedup. What should it check?
   • Whether the training data are alphabetized
   • Whether the workload uses efficient supported kernels on its hardware
   • Whether lower bit width always guarantees faster execution
   • Whether the model must have changed languages