Maswali yanayounganishwa na mwongozo · Kati Kiwango

Maswali ya Kurekebisha kwa Ufanisi wa LoRA na Parameta

Kagua uzani wa LoRA uliogandishwa, uokoaji wa hali inayoweza kufunzwa, cheo, hifadhi ya QLoRA, uunganisho unaooana, besi zilizoshirikiwa, hesabu za vigezo na ujanibishaji maradufu.

Njia za mwongozo zinazohusianaLora Na Parameta Urekebishaji Ufanisi
Swali 1 ya 8

In the original LoRA formulation for a selected weight matrix, what is learned while that matrix stays frozen?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. In the original LoRA formulation for a selected weight matrix, what is learned while that matrix stays frozen?
    • A replacement vocabulary with no weight update
    • Every entry of the full base matrix from random initialization
    • Two factor matrices forming an additive low-rank update
    • A permanent deletion of the entire selected layer
  2. Which resource requirements can fewer trainable LoRA parameters reduce compared with full fine-tuning?
    • Every base-model computation by definition
    • All activation storage regardless of the training procedure
    • A guaranteed universal percentage of total runtime memory
    • Optimizer-state and task-checkpoint requirements
  3. Je, cheo 'r' katika adapta ya LoRA kinadhibiti nini?
    • Kipimo cha ndani cha matrices mbili za kiwango cha chini, kuweka uwezo wa adapta
    • Idadi ya tabaka katika muundo wa msingi
    • Kiwango cha mafunzo wakati wa mafunzo
    • GPU ngapi zinahitajika
  4. Which storage and adaptation choices are combined in the QLoRA paper?
    • Updating every base weight directly as a 4-bit integer gradient
    • A frozen low-bit base with trainable LoRA adapters
    • Removing all adapters and all task data
    • A guarantee that every model fits every consumer GPU
  5. What can a compatible dense merge of a LoRA update remove from that layer's inference computation?
    • The need to evaluate the base layer at all
    • Every possible dtype compatibility requirement
    • A requirement that all future task updates become identical
    • The need to evaluate a separate adapter branch
  6. Which reuse pattern is supported by storing compatible task adapters separately from a base checkpoint?
    • Every adapter automatically makes every task factually correct
    • All serving systems must switch tasks with zero overhead
    • Multiple task updates can share the same pretrained base
    • The base checkpoint is no longer needed for inference
  7. For a stated 4-by-4 weight update with rank-one factors shaped 4-by-1 and 1-by-4, how many factor parameters are there?
    • Kumi na sita
    • Nane
    • Nne
    • Thirty-two
  8. What does QLoRA's double-quantization technique compress in addition to the base weight representation?
    • The quantization constants
    • Every task label into a second unrelated category
    • The adapter gradients twice by definition
    • A second complete copy of the base model