Ọgbọ anabatara yana Grammar
Ọgbọ amachibidoro na-amanye ụdị asụsụ iwepụta mmepụta nke na-adaba n'usoro akọwapụtara mgbe niile, dị ka JSON, SQL, ma ọ bụ nkwupụta oge niile.
Nchịkọta
It matters because it eliminates an entire class of parsing failures, making LLMs reliable enough to wire into real software pipelines.
Ime miri emi
Ụdị asụsụ nkịtị na-esetịpụ akara na-esote n'efu, yabụ ọ nwere ike iwepụta JSON na-adịghị mma, uru enum na-ezighi ezi, ma ọ bụ braket na-ezighi ezi. Ọgbọ amachibidoro na-agbanwe usoro nlele n'onwe ya: n'ọnọdụ ọ bụla, sistemụ ahụ na-agbakọ akara akara ka enyere atụmatụ ma ọ bụ ụtọ asụsụ, wee kpuchie ohere nke akara ngosi iwu na-akwadoghị ka ọ bụrụ efu tupu ịlele. A na-egosipụtakarị iwu ndị a dị ka ụtọ asụsụ na-enweghị ihe ọ bụla (a na-achịkọtakarị ya n'ụdị GBNF nke llama.cpp na-eji), nkwupụta oge niile, ma ọ bụ atụmatụ JSON. Ọbá akwụkwọ dị ka ndepụta, Nduzi, na XGrammar, gbakwunyere OpenAI's arụpụtara mmepụta na 'JSON mode,' mejuputa nke a. N'ihi na a na-akwachapụ ụzọ iwu na-akwadoghị, ihe nlereanya ahụ enweghị ike ịwepụta eriri na-adịghị atụgharị, ebe ọ ka na-ahọrọ n'efu n'etiti ndị na-aga n'ihu.
Nghọta nka nka
Isi aghụghọ bụ igwe nwere njedebe steeti token. A na-achịkọta ụtọ asụsụ ma ọ bụ regex ka ọ bụrụ steeti, yana maka steeti ọ bụla akara nkpuchi agbagoro agbagoro nke akara okwu na-eme ka mmepụta ahụ dị irè. Mgbe ihe nlereanya ahụ ewepụtala logit ya, akara ngosi iwu na-akwadoghị na-edobe na njedebe na-adịghị mma, yabụ softmax na-ekenye ha ohere efu. Ọganihu igwe na-egosi akara ngosi ọ bụla anabatara. Tokenizer mismatches (otu akara ngosi na-agafe oke ụtọ ụtọ asụsụ) bụ akụkụ siri ike, nke a na-edozi site na ịkọwapụta okwu megide akpaghị aka tupu oge eruo.
Mmetụta atụmatụ
Ọsọ na ọnụ ọgụgụ
Usoro ọrụ asụsụ nwere ike ịga ngwa ngwa n'achụghị nkwụsi ike.
Nweta na iru
Ọ na-agbasawanye ohere n'ofe asụsụ na ụdị nzikọrịta ozi.
Mkpebi doro anya
Ndị otu nwere ike itinyekwu oge na ikpe ebe akpaaka na-ejikwa nkwughachi.
Ọdịnihu nke Ọgbọ Ndabere na Grammar
Na-atụ anya ndozi mmachi ka ọ bụrụ ihe ndabara, ihe dị nso-efu-n'elu n'ime igwe inference dị ka vLLM na TensorRT-LLM kama ịbụ ọba akwụkwọ bolt-na. Nchọpụta na-aga n'ihu na mmachi ndị bara ụba, ụtọ ụtọ asụsụ zuru oke na-enwe mmetụta, ụdị koodu enyochara, yana mgbochi ndị na-amanye eziokwu gbasara ọmụmụ, ọ bụghị naanị syntax. Ijikọ nke ọma na ndị nnọchi anya yana ịkpọ ngwá ọrụ ga-ahapụ ụdị ka ha wepụta arụmụka ọrụ ntụkwasị obi. Ihe ịma aka a na-emeghe bụ idobe izi ezi dị elu, ebe ọ bụ na grammar na-akpachi anya nwere ike mgbe ụfọdụ wepụ ihe nlereanya na azịza ya kacha mma.
Mmejuputa n'ezie n'ụwa
Na-amanye LLM ka ọ bupụta JSON nke dabara na atụmatụ API nke mere na koodu mgbada adịghị enweta njehie nzacha.
Ịmepụta SQL nke ekwenyere na ọ ga-aba uru n'ụzọ kwekọrọ ekwekọ megide ụtọ asụsụ nchekwa data tupu e gbuo ya.
Na-amachibido mmepụta nkewa klas ka ọ bụrụ otu n'ime aha aha ndị edobere anya site na iji regex ma ọ bụ enum.
Na-ewepụta arụmụka-oku ọrụ maka ndị na-eji ngwá ọrụ na-adakọ mgbe niile ụdị oke ihe achọrọ
Ihe ize ndụ & okporo ụzọ nche
Eziokwu ndị e chepụtara echepụta nwere ike jiri nwayọ tinye akụkọ, nkwado nkwado, ma ọ bụ nsonaazụ nyocha.
Mmetụta ngwa ngwa nwere ike ịmepụta nsonaazụ na-ekwekọghị ekwekọ n'ofe arịrịọ ndị yiri ya.
Enwere ike ikpughe data ederede nwere mmetụta ma ọ bụrụ na njikwa ohere adịghị ike.
Map mmejuputa
Kọwaa usoro mmepụta, ụda, na ụkpụrụ ịdịmma tupu ibugharị.
Weghachite nzaghachi site na isi mmalite ntụkwasị obi mgbe ọ bụla izi ezi dị mkpa.
Debe ebe nleba anya mmadụ maka mpụta dị elu.
Sochie ụkpụrụ ọdịda ma na-azụghachi mkpali ma ọ bụ usoro ọrụ mgbe niile.
Nọgide na-eme nchọpụta
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Ntuziaka na-esote
Ndozi amachibidoro
Ajụjụ a na-ajụkarị
What is Constrained and Grammar-Guided Generation?
Ọgbọ amachibidoro na-amanye ụdị asụsụ iwepụta mmepụta nke na-adaba n'usoro akọwapụtara mgbe niile, dị ka JSON, SQL, ma ọ bụ nkwupụta oge niile. Ọ dị mkpa n'ihi na ọ na-ewepụ klas dum nke ntule ọdịda, na-eme ka LLMs bụrụ ndị a pụrụ ịdabere na ya nke ọma iji waya n'ime pipeline software.
Kedu ihe ọgbọ amachibidoro na-agbanwe n'ezie n'oge ọgbọ ederede?
Ọgbọ amachibidoro na-etinye aka n'oge ngbanwe, na-ewepụ ohere nke token ga-emebi usoro achọrọ tupu ịlele.
Kedu n'ime ihe ndị a bụ ụzọ a na-ejikarị egosipụta iwu maka ọgbọ na-eduzi ụtọ asụsụ?
A na-akọwakarị mmachi dị ka ụtọ asụsụ na-enweghị ihe ọ bụla (dịka, GBNF), nkwupụta oge niile, ma ọ bụ atụmatụ JSON.
Kedu ka esi egbochi ịhọpụta akara ngosi iwu na-akwadoghị?
Masking na-esetịpụ akara ngosi iwu na-akwadoghị na enweghị njedebe na-adịghị mma, yabụ mgbe softmax gasịrị, ha na-enweta ohere efu na enweghị ike ịlele ya.
Gịnị bụ isi ọrụaka siri ike na mmejuputa iwu-ọkwa token mmachi?
Otu akara nwere ike gbasaa n'ofe ụtọ asụsụ, yabụ, a ga-edocharịrị nke ọma n'ụkpụrụ okwu ahụ megide automaton iji jikwaa ndakọrịta ndị a.
Kedu nke bụ uru kpọmkwem nke ọgbọ amachibidoro maka usoro mmepụta?
Site n'ịrụ ụlọ, ihe a na-emepụta na-adaba na nhazi ahụ, ya mere, ndị na-eme nchọpụta nke ala adịghị amanye ederede na-adịghị mma. Ọ naghị ekwe nkwa izi ezi.