QLoRA (LLM quantization): superseded — cited as a baseline and beaten by newer methods. 3 paper(s) critique it, 1 beat it on benchmarks — #20 of 80 most-superseded. Sub-problem: cluster led by RTN. Newer alternatives in the same sub-problem include STaR-Quant, Timestep-Aware SVDQuant-GPTQ, BWLA, Bit-by-Bit, Benford-Quant.

Superseded baseline#20 of 80 most-superseded

QLoRA

QLoRA: Efficient Finetuning of Quantized LLMs

LLM quantization · first seen May 23, 2023

superseded — cited as a baseline and beaten by newer methods

3 papers critique it · 1 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites QLoRA as a baseline.

“Limited to fine-tuning (not training from scratch) and requires GPU hardware.”
— True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity
“However, these methods only apply quantization to the weight during fine-tuning to reduce memory consumption.”
— RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization
“Although QLoRA substantially reduces GPU memory and fine-tuning time while maintaining strong performance at the 4-bit level, it suffers from considerable performance degradation below 4 bits.”
— Enhancing Ultra-Low-Bit Quantization of Large Language Models Through Saliency-Aware Partial Retraining

Beaten on benchmarks

Head-to-head results where a newer method reports beating QLoRA. Values are copied from the source paper's tables — verify against the cited paper.

RoLoRA beats QLoRA · Avg. (ZCSR) [W4A16 + RTN]
45.3 vs 36.2
RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization
RoLoRA beats QLoRA · Avg. (MMLU) [W4A16 + RTN]
24.5 vs 23.5
RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization
RoLoRA beats QLoRA · Avg. (ZCSR) [W4A4 + GPTQ]
58.2 vs 37.7
RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization
RoLoRA beats QLoRA · Avg. (MMLU) [W4A4 + GPTQ]
29.9 vs 23.6
RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization

Newer alternatives

Recent methods in the same sub-problem, not yet superseded in the knowledge base.