Ṣiṣayẹwo aaye Ṣiṣayẹwo ati Idanwo Ikẹkọ Ibẹrẹ

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

1. Kini idi ti awọn aaye ayẹwo awoṣe nla ti pin si awọn ege?
   • Lati jẹ ki awoṣe ṣe ikẹkọ yiyara lori awọn igbewọle kekere
   • Lati tọju awọn iwọn awoṣe lati awọn olumulo
   • Nitori sharding mu išedede dara
   • Nitori aaye ayẹwo kan le tobi ju fun faili kan tabi ẹrọ lati mu daradara
2. Which extra state is typically needed for training resumption beyond model weights?
   • Nikan ik išedede Dimegilio
   • Optimizer and scheduler state, step progress, RNG state, and relevant data-loader progress
   • Only a model-weight shard index
   • A screenshot of the training dashboard
3. Kini anfani akọkọ ti ikẹkọ atunbere fun awọn iṣẹ pipẹ?
   • O yọ awọn nilo fun afọwọsi ṣeto
   • O ṣe onigbọwọ awoṣe kii ṣe overfits
   • Ijamba tabi iṣaju ko ni ipa lati tun bẹrẹ lati ibere
   • O ti jade ni optimizer
4. In a sharded distributed checkpoint, how can ranks avoid gathering the entire state on one worker?
   • By saving their assigned local tensor shards with checkpoint metadata
   • By each emailing a full model copy
   • By deleting optimizer state before every save
   • By disabling all state serialization
5. Ipa wo ni faili atọka ṣe ni awọn aaye ayẹwo ti o pin?
   • O tọju ọrọ igbaniwọle olumulo
   • O maapu orukọ paramita kọọkan si faili shard ti o ni ninu
   • O compress awọn aworan ninu awọn dataset
   • O ṣe iṣeto oṣuwọn ikẹkọ
6. Iru ọna kika wo ni a lo nigbagbogbo fun ailewu, ikojọpọ iranti-maapu ti awọn shards awoṣe?
   • MP3
   • CSV
   • safetensors
   • Awọn ibi ipamọ ZIP nikan
7. A package contains safetensors weight shards and an index, but no optimizer state. What can it support by itself?
   • Loading model weights, without fully restoring optimizer training history
   • Guaranteed exact resumption of every interrupted run
   • Recovering the next batch position automatically
   • Reconstructing every random-number-generator state
8. A sharded checkpoint loader supports a new rank layout. What should the team verify before trusting recovery?
   • That fewer ranks guarantee identical floating-point results
   • That the weight index alone includes every application state
   • That no restart test is necessary
   • That the necessary saved state loads correctly and the resumed training step behaves as expected