Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

GPTQ ati AWQ Lẹhin-Ikẹkọ Quantization Quantization

Checks the difference between GPTQ and AWQ, calibration data, curvature compensation, activation-aware channels and low-bit memory.

Jẹmọ itọsọna awọn ọnaGptq Ati Awq Post Ikẹkọ Quantization
Ibeere 1 ti 8

Kini itumo 'pipe ikẹkọ lẹhin-ikẹkọ'?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Kini itumo 'pipe ikẹkọ lẹhin-ikẹkọ'?
    • Fisinuirindigbindigbin awọn iwọn awoṣe ti o ti ni ikẹkọ tẹlẹ si iwọn konge laisi ikẹkọ ni kikun
    • Retraining awọn awoṣe šee igbọkanle ni kekere konge
    • Ṣafikun data ikẹkọ diẹ sii lẹhin imuṣiṣẹ
    • Jijẹ kika paramita awoṣe
  2. Alaye wo ni GPTQ nlo lati sanpada fun awọn aṣiṣe iyipo nigbati o ba ṣe iwọn bi?
    • Awọn awoṣe ká fokabulari akojọ
    • Ibere-keji (Hessian/curvature) alaye lati inu data isọdiwọn kan
    • Itan itọka olumulo
    • ariwo laileto
  3. Ìjìnlẹ̀ òye kọ́kọ́rọ́ wo ló ń ṣamọ̀nà AWQ (Ìfikún Ìwọ̀n Ìmúṣiṣẹ́-mọ̀)?
    • Gbogbo awọn òṣuwọn ni o wa se pataki
    • Awọn iṣẹ ṣiṣe yẹ ki o ṣe iwọn ṣugbọn awọn iwuwo fi silẹ laifọwọkan
    • Ida kan kekere ti awọn ikanni iwuwo jẹ pataki paapaa ati pe o yẹ ki o ni aabo
    • Quantization yẹ ki o ṣẹlẹ lakoko ikẹkọ iṣaaju nikan
  4. Ni aijọju melomelo iranti ni iye iwọn 4-bit fipamọ dipo awọn iwuwo 16-bit?
    • Ko si awọn ifowopamọ
    • About ė iranti
    • Gangan idaji iranti
    • Nipa idamẹrin iranti (idinku 4x)
  5. Kini idi ti awọn mejeeji GPTQ ati AWQ maa n ṣe iwọn awọn iwuwo ṣugbọn tọju awọn iṣẹ ṣiṣe ni pipe ti o ga julọ?
    • Awọn iwuwo jẹ gaba lori iranti, ati iwọn imuṣiṣẹ ibinu duro lati ṣe ipalara deede diẹ sii
    • Awọn iṣẹ ṣiṣe ko si ni awọn oluyipada
    • Awọn iṣẹ ṣiṣe jẹ 1-bit tẹlẹ
    • O nilo nipasẹ ofin
  6. Ọpa wo ni a lo nigbagbogbo lati ṣiṣe awọn awoṣe titobi ni agbegbe lori ohun elo olumulo?
    • Microsoft Ọrọ
    • llama.cpp
    • Eto iwe kaunti kan
    • A kiri lori ayelujara nikan
  7. 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
  8. 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