Awoṣe Quantization adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Didiwọn awoṣe kan dinku kini?
   • Itọye nọmba ti a lo lati tọju awọn iwuwo ati awọn iṣẹ ṣiṣe
   • Awọn nọmba ti fẹlẹfẹlẹ ni awọn nẹtiwọki
   • Awọn dataset ikẹkọ awoṣe ti a ṣe lori
   • Ede ti awoṣe le loye
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. Kí ni akọkọ downside ti ibinu kekere-bit quantization?
   • Awoṣe gbagbe ede ikẹkọ rẹ
   • O pọju isonu ti deede bi awọn iye ti wa ni squeezed sinu díẹ garawa
   • O le nikan ṣiṣẹ lori supercomputers
   • O npa awọn ipele rẹ kuro patapata
4. Báwo ni ìdánilẹ́kọ̀ọ́ tí a mọ̀ nípa dídán mọ́ ṣe yàtọ̀ sí dídidiwọ̀n ìdánilẹ́kọ̀ọ́ lẹ́yìn-ọ̀-rẹyìn?
   • Ko nilo data rara
   • O ṣiṣẹ nikan lori awọn aworan
   • O mu ki awọn nọmba ti die-die lori akoko
   • O ṣe simulates yikaka lakoko ikẹkọ nitorinaa awoṣe kọ ẹkọ lati fi aaye gba konge kekere
5. Ilana wo lo nlo titobi 4-bit lati ṣe atunṣe awọn awoṣe nla ti o dara ni ifarada lori GPU kan?
   • Jade kuro
   • Ipese deede
   • QLoRA
   • Àmi
6. Kini idi ti ikanni-ikanni tabi igbelowọn ẹgbẹ-ẹgbẹ ṣe iranlọwọ awọn awoṣe titobi?
   • O ṣe ipinnu awọn ifosiwewe iwọn lọtọ si awọn ege matrix iwuwo lati ṣetọju pipe
   • O yọ awọn nilo fun eyikeyi òṣuwọn
   • O mu ki awoṣe naa tobi
   • O ṣe iyipada ọrọ sinu ohun
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