Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Ti eleto Pruning ati Layer Sisọ adanwo

Checks why structured pruning speeds inference, how layer importance is estimated, and how fine-tuning or distillation recovers quality.

Jẹmọ itọsọna awọn ọnaTi eleto Pruning Ati Layer silẹ
Ibeere 1 ti 8

Èé ṣe tí ìsokọ́ra tí a ṣètò ṣe ń mú kí a yára gbéra jáde nígbà tí ìrẹ̀lẹ̀ tí a kò ṣètò kò sábà máa ń ṣe?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Èé ṣe tí ìsokọ́ra tí a ṣètò ṣe ń mú kí a yára gbéra jáde nígbà tí ìrẹ̀lẹ̀ tí a kò ṣètò kò sábà máa ń ṣe?
    • Igi gige ti a ṣeto yọkuro awọn bulọọki isọpọ ti o dinku awọn apọn
    • Unstructured pruning mu ki awọn awoṣe iwọn
    • Igi gige ti a ṣeto ṣe afikun awọn ipele diẹ sii
    • Unstructured pruning nikan ṣiṣẹ lori CPUs
  2. Kí ni 'Layer dropping' ni o tọ ti Ayirapada?
    • Idinku oṣuwọn ẹkọ fun Layer
    • Nparẹ gbogbo awọn bulọọki ẹrọ iyipada lati dinku ijinle awoṣe
    • Fifi dropout si gbogbo Layer
    • Atunse awọn fẹlẹfẹlẹ laileto
  3. Eyi ti ifihan commonly tọkasi wipe a transformer Layer le ti wa ni kuro lailewu?
    • Layer ni awọn paramita pupọ julọ
    • Layer jẹ akọkọ ninu nẹtiwọki
    • Iṣawọle Layer ati iṣelọpọ awọn ipinlẹ ti o farapamọ jọra pupọ
    • Layer nlo iranti julọ
  4. Igbesẹ wo ni igbagbogbo ṣe atunṣe deede lẹhin pruning ti a ṣeto?
    • Npo iwọn ipele nikan
    • Tun-diwọn si 16 die-die
    • Fifi awọn olori akiyesi diẹ sii
    • A kukuru yika ti itanran-yiyi tabi imo distillation
  5. Bawo ni awọn ori akiyesi ẹni kọọkan nigbagbogbo ni ipo fun pruning?
    • Nipa ilosoke ninu pipadanu nigbati ori ba boju (ifamọ)
    • Nipa tito alfabeti
    • Nipa ipo wọn ni awọn fokabulari
    • Nipa awọn nọmba ti àmi ti won ti ri
  6. Kini lilo iwulo pataki ti pruning eleto ni idapo pẹlu distillation?
    • Ṣiṣẹda data ikẹkọ laifọwọyi
    • Wiwa idile ti o kere, awọn awoṣe yiyara lati aaye ayẹwo nla kan
    • Ìsekóòdù awoṣe òṣuwọn
    • Npo si ipari window ọrọ-ọrọ
  7. Why can a smaller student recover some function of a pruned model during distillation?
    • It restores the deleted weights in place
    • It changes the test labels
    • It is trained to match useful outputs or behavior of the larger teacher
    • It removes the need for validation
  8. If a transformer layer’s input and output have high cosine similarity, what does that suggest under the guide’s pruning heuristic?
    • The layer is guaranteed to be useless for every task
    • The model must add another layer
    • The output has no features
    • The layer changed the representation relatively little and may be a candidate for dropping