AQLoRA: A Zero-Search Recipe for Fast Quantized LoRA Fine-Tuning

arXiv:2608.23816 · cs.LG · Submitted 2026-08-24 · Read on arXiv

cs.LG

Submitted: 2026-08-24

Updated: 2026-08-24

Code: https://github.com/Romyull-Islam/AQLoRA

Terminology

• Accuracy — 48× in this paper · explained in 1 episode
• Fine-Tuning — 38× in this paper · explained in 5 episodes
• Quantization — 25× in this paper · explained in 12 episodes
• Perplexity — 12× in this paper · explained in 3 episodes
• Calibration — 10× in this paper · explained in 7 episodes
• Adaptation — 7× in this paper · explained in 1 episode
• LoRA Fine-Tuning — 5× in this paper · explained in 3 episodes
• Reconstruction Error — 5× in this paper · explained in 2 episodes
• Allocator — 4× in this paper · explained in 1 episode
• Fidelity — 4× in this paper · explained in 2 episodes
• Sparsity — 4× in this paper · explained in 4 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Edge Devices — 2× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Quantization-Aware Training — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• FALQON: Accelerating LoRA Fine-tuning with Low-Bit Floating-Point Arithmetic
• CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization
• QLoRA: Efficient Finetuning of Quantized LLMs
• HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
• Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels
• MobileFineTuner: A Mobile-Native Framework for On-Device LLM Fine-Tuning in Real-World Embedded AI Applications
• LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
• LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
• Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
• QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
• A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA
• SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
• LoRA Is Slower Than You Think
• QEFT: Quantization for Efficient Fine-Tuning of LLMs
• OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
• LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
• Parameter-Efficient Fine-Tuning without Introducing New Latency
• ApiQ: Finetuning of 2-Bit Quantized Large Language Model
• DoRA: Weight-Decomposed Low-Rank Adaptation
• PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

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