Kedis ku xidhan hagaha · Dhexdhexaad Heerka
GPTQ iyo AWQ Quantization Quantization Post-Training
Wuxuu hubiyaa faraqa u dhexeeya GPTQ iyo AWQ, xogta cabbirka, magdhowga curvature, kanaalada firfircoon ee ogaanshaha iyo xusuusta-yar.
Su'aal 1 ee 8
Waa maxay macnaha 'Tirada-tababarka ka dib'?
Horudhac su'aal kasta8 su'aalo
Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.
- Waa maxay macnaha 'Tirada-tababarka ka dib'?
- Cadaadiska miisaanka model horay loo tababaray si loo yareeyo saxnaanta iyada oo aan si buuxda loo tababarin
- Dib-u-tababaridda moodeelka gebi ahaanba saxnaanta hoose
- Ku darida xog tababar oo badan ka dib marka la diro
- Kordhinta tirada cabbirka moodeelka
- Waa maxay macluumaadka uu GPTQ u isticmaalo si uu u kabo khaladaadka soo koobida marka la xisaabinayo?
- Liiska erayada tusaalaha
- Dalabka-labaad (Hessian/curvature) macluumaadka laga soo qaatay kaydinta xogta
- Taariikhda degdega ah ee isticmaalaha
- Sawaxan random
- Waa maxay aragtida muhiimka ah ee dhaqaajisa AWQ (Qorinta Miisaanka Dhaqdhaqaaqa og)?
- Dhammaan miisaanka ayaa si isku mid ah muhiim u ah
- Dhaqdhaqaaqa waa in la qiyaasaa laakiin miisaanku waa in aan la taaban
- Qayb yar oo ka mid ah kanaalada miisaanka ayaa si gaar ah muhiim u ah waana in la ilaaliyo
- Tiro-saariddu waa inay dhacdaa inta lagu jiro tababarka hore oo keliya
- Qiyaastii intee in le'eg ayaa xusuusta ay 4-bit tireed u badbaadisaa marka loo eego miisaanka 16-bit?
- Wax kayd ah malaha
- Ku saabsan xusuusta labanlaab
- Dhab ahaan kala badh xusuusta
- Qiyaastii hal rubuc xusuusta ( dhimis 4x ah)
- Maxay GPTQ iyo AWQ labaduba caadi ahaan u cabbiraan miisaanka laakiin ay hawl-qabadyadu si sax ah ugu hayaan?
- Miisaanka ayaa xukuma xusuusta, iyo qiyaasida firfircoonida firfircooni waxay u egtahay inay dhaawacdo saxnaanta
- Waxqabadyadu kuma jiraan transformers
- Dhaqdhaqaaqyadu waxay hore u ahaayeen 1-bit
- Sharci ayaa looga baahan yahay
- Qalabkee ayaa inta badan loo isticmaalaa in lagu socodsiiyo moodooyinka la qiyaasey gudaha qalabka macaamiisha?
- Microsoft Eray
- llama.cpp
- Barnaamij xaashida
- Biraawsarreeye kaligiis
- How does GPTQ use a calibration dataset during layer-wise weight quantization?
- It adds new training labels to the model
- It selects a new tokenizer vocabulary
- It estimates second-order information to compensate for rounding errors
- It measures only GPU temperature
- How does AWQ identify which weight channels deserve protection?
- It computes a full Hessian inverse
- It adds random noise to all weights
- It selects channels only by their index
- It uses activation magnitudes to identify channels important to preserve