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GUIDE teknik
Modèle de potage
Dagg model dafay wàññi reso neuronal bi ci dindi ay poid wala ay structure yu mat te duñu def lu bari ci génnam.
Déglul tegtal biiIA-nettali · ~2 min
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Résumé
It cuts size, memory, and compute cost while aiming to keep accuracy nearly intact.
Plongeur bu xóot
Reseau neuronal yiñ tàggat dañuy faral di ëpp ay parametre: lëkkaloo yu bari dañuy yor ay poid yu tuuti yuy indi jafe-jafe ci wax luy waaja am. Dagg dafay xàmmee ba noppi dindi mbir yooyu, bàyyi xeetu garab gu gëna woyof. Dagg bu jaxasoowul dafay nul poid yi ci benn-benn, génne matris yu bari yuñ mëna comprimé bu baax waaye soxla hardware wala bibliothèque yu yam ngir gaaw. Dagg buñ yamale dafay dindi ay yunit yu mat - neuron, boppu bàyyi xel, chaine, wala diisaay - di joxe xeetu dense bu gëna ndaw buy daw ci hardware bu gëna gaaw. Benn ci rëset yiñ gëna xam mooy bouclage iteratif bi: tàggat, dagg parametre yi gëna néew solo ci yenn kritër (dafay faral di am magnitude poids), ba noppi defar ko bu baax ngir am njubte gi ñàkk, baamtu ba dayo bi wala gaawaay bi nga bëgga yegg. Dagg ñaari mbir ci anam wu natureel ak kantite ak distilaasioŋ ci biir tuyo yi ñuy dugal.
Doxal ci àdduna dëgg
Komprime ab modelu làkk bu yaatu ngir dox ci benn GPU konsomatër ci barabu benn cluster serwër.
Sewloo xeetu gis-gis bi suko defee mu mëna ànd ak memory telefon bu xarañ bi wala kamera biñ samp ci biir.
Di dindi boppu yiy gëna bàyyi xel ci Transformatër bu amul benn wàññeekaay buñu mëna natt ci kalite bi.
Wàññi energie inference ak latency ngir sarwis yu bari trafik ngir wàññi njëgu cloud bi.
Ëlëgu modelu dagg
Pruning is increasingly applied to large language models, where structured methods can remove supported attention heads, neurons, or layers to fit models onto smaller GPUs and edge devices. Some hardware and kernels accelerate specific supported sparsity patterns, such as NVIDIA 2:4 structured sparsity; arbitrary unstructured zeros do not automatically run faster. Expect pruning to be combined with quantization and distillation in compression pipelines measured against concrete latency, energy, memory, and quality targets.
Teg ko ci jëf
Lu tax mu am solo
- Njëgg ak budget. Dogal yi architecture di jël dañuy indi njariñ ak njëgu liggéey bi ay at ci ginaaw.
- dogal yu gëna leer. Njàngalem xarala yi dafay jàppale ekip yi ñu tànn li gën, te baña yam ci li gëna bees daal.
- Xool kalite. Tanneef yu gëna baax ci wàllu ingeñër dina wàññi jafe-jafe yi ci wàllu wóor ci liggéey bi.
Moytul
- Optimize benn benchmark mën na nëbb ñakk kattan yu gëna yaatu ci sistem bi.
- Njëg li ñuy fay ci infrastructure yi ak ci toppatoo dañuy faral di suufeel.
- Bu sistem yi di gëna xawa jafee xam, jafe-jafe yi am ci wàllu kaaraange ak seetlu mën nañu gëna bari.
Jéego yi ci topp
- Mandargal latency, kalite, ak njëg yi laata ngay jëfandikoo.
- Benchmark ci biir sargal ak done yu dëggu.
- Jumtukaay bi di saytu njuumte yi, derive bi ak njeextalu jëfandikukat bi.
- Waajal rollback ak yooni tontu ci jafe-jafe yi laata ngay eskale.
Weyal di banneexu
Bulletin bu amul fayda
Jàngal xibaar AI bu bes
Ñatti jaar-jaari IA yuñ firndeel bis bu nekk ci ayu-bis bi, ñu bind leen ci Àngle bu leer. Gratis ba fàww, amul yëgle.
Benn email bis bu nekk ci ayu-bis bi. Benn klik nga dindi abonemaa bi. Dunu musa jaay wala séddoo sa adres.
Nattal sa bopp
Take the Model Pruning quiz
Feedback ci saasi ci bépp tontu, ak sertifikaa buñ mëna séddoo ak ID buñ mëna saytu soo jàllee ci njàng mi.
Jàppale njàngum IA te doo fay. AI Understanding mbootaay la buy def te Yàlla tax — amul yëgle, amul paywall, musul am. Defal ab sarax
Laaj yi ñuy faral di laaj
What is Model Pruning?
Dagg model dafay wàññi reso neuronal bi ci dindi ay poid wala ay structure yu mat te duñu def lu bari ci génnam. Dafay wàññi dayo bi, mémoire bi, ak njëgu ordinatër bi, boole ci baña yàq njubte gi.
What does model pruning remove from a trained network?
Dagg dafay jëfandikoo parametrisation bu ëpp ci dagg poids wala unité yu néew doole ngir wàññi model bi boole ci baña yàq li gëna bari ci njubteem.
Lan mooy wuutale dagg buñ defar ak dagg buñ defarul?
Daggukaay buñ yamale dafay dindi bépp komposant, ba noppi génne ay model yu gëna ndaw yu gëna gaaw ci hardware buñ miin, fekk daggukaayu jaxasoowul dafay dindi poid yi benn-benn, di defar sparsity.
Lan moo waral dagg garab gu jaxasoo du gaawaale model bi ci hardware bu gëna bari?
Zero yu tasaaroo duñu tekki ci saasi ci xayma yu néew; hardware dense baa ngi leen di liggéey fileek jëfandikoo wuñu kernel wala accelerateur yu yam.
Lan mooy rëset bu ñuy faral di def ngir dagg garab?
Daggug iteratif dafay wuutale dindi parametre yu amul solo ak defar bu baax ngir am njub, ndànk-ndànk dem ba ci dayo wala gaawaay biñ bëgga am.
Luy xalaatu tiketu loteri bi wax?
Hypothèse bi dafa wane ni subréseau bu ndaw buñu tann bu baax ('ticket winning'), buñu tàggatee ci initialisation bu njëkk bi, mën na yegg ci njubteg reso dense bu mat sëkk.
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