L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Quantization-aware PEFT methods have been developed to reduce memory and computational costs associated with large language models. |
| Approach: | They propose a method that integrates Quantization-Aware Training (QAT) with LoRA to reduce memory overhead and improve model accuracy. |
| Outcome: | The proposed method significantly reduces QAT’s memory overhead while preserving the advantage of QAT in producing fully quantized LLMs with high accuracy. |
Similar Papers
PE-QAT: Parameter-Efficient Quantization-Aware Training for Large Language Models (2026.acl-srw)
Copied to clipboard
| Challenge: | Quantization Aware Training (QAT) is expensive to train and unscalable to large models. |
| Approach: | They propose a parameter-efficient framework targeting per-channel 4-bit weight-activation quantization of large language models. |
| Outcome: | The proposed framework preserves accuracy within 0.11 percentage points of the full-precision baseline on Llama-2-7B zero-shot tasks while training only 1.26% of total parameters. |
DL-QAT: Weight-Decomposed Low-Rank Quantization-Aware Training for Large Language Models (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Quantization-aware Training (QAT) is a popular technique for reducing memory usage and improving computational efficiency in large language models. |
| Approach: | They propose a weight-decomposed low-rank quantization-aware training approach that integrates QAT with a group-specific quantization magnitude adjustment. |
| Outcome: | The proposed method outperforms the state-of-the-art method on LLaMA and LLama2 models. |
EfficientQAT: Efficient Quantization-Aware Training for Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Quantization-aware training (QAT) is a low-bit training solution that requires substantial training resources. |
| Approach: | They propose an algorithm that reduces memory consumption by low-bit representations with minimal accuracy loss. |
| Outcome: | EfficientQAT achieves 2-bit Llama-2-70B model on single GPU in 41 hours . compared to previous methods, it obtains model with less than 3 points accuracy degradation . |
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for quantizing weights and activations of large language models suffer from non-negligible accuracy drops, especially on massive multitask language understanding. |
| Approach: | They propose a weight-activation quantization method that reconstructs the outputs of an intermediate Transformer block by leveraging low-rank weight-scaling matrices. |
| Outcome: | The proposed method reduces the complexity of the weight-activation quantization techniques while achieving high throughput and reducing inference costs. |
ApiQ: Finetuning of 2-Bit Quantized Large Language Model (2024.emnlp-main)
Copied to clipboard
| Challenge: | Memory-efficient finetuning of large language models (LLMs) has attracted huge attention with the increasing size of LLMs due to the constraints posed by GPU memory limitations and the effectiveness of these methods compared to full finetune. |
| Approach: | They propose a memory-efficient finetuning framework called ApiQ to restore lost information from quantization by initializing LoRA components and quantizing weights of LLMs. |
| Outcome: | The proposed framework maintains the original LLM’s activation precision while mitigating error propagation from shallower into deeper layers. |
RA-LoRA: Rank-Adaptive Parameter-Efficient Fine-Tuning for Accurate 2-bit Quantized Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) with their extensive parameters and high memory demands are challenging to fine-tune for specific applications with limited resources. |
| Approach: | They propose a method that dynamically adjusts the adapter’s rank using rank-subspace analysis, optimizing performance with fewer parameters. |
| Outcome: | The proposed method improves model accuracy with minimal parameter changes and demonstrates the importance of rank dynamics in optimizing quantized LLMs. |
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models (2024.findings-acl)
Copied to clipboard
Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, Vikas Chandra
| Challenge: | Several post-training quantization methods have been shown to perform well down to 8-bits. |
| Approach: | They propose a data-free distillation method that leverages generations produced by the pre-trained model to quantize any generative model independent of its training data. |
| Outcome: | The proposed method outperforms SoTA PTQ and LLaMA models at low bit precision. |
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models (2024.acl-long)
Copied to clipboard
Zhengxin Zhang, Dan Zhao, Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Qing Li, Yong Jiang, Zhihao Jia
| Challenge: | Existing methods to finetun large language models (LLMs) only update a small number of trainable parameters, or attempt to reduce the memory footprint during the training phase of the finetune process. |
| Approach: | They propose quantized side tuing (QST) which quantizes an LLM’s model weights into 4-bit to reduce the memory footprint of the original weights. |
| Outcome: | The proposed method reduces the memory footprint of the model weights, optimizer states, and intermediate activations while reducing the memory requirements. |
LRQuant: Learnable and Robust Post-Training Quantization for Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for post-training quantization (PTQ) are limited by the complexity of the quantization parameter and performance degradations when tested on unseen datasets. |
| Approach: | They propose a learnable smooth-based PTQ framework that allows for rapid adaptation during testing. |
| Outcome: | The proposed framework improves performance on unseen datasets and reduces memory constraints. |
ACBQ: Adaptive Cross-Block Quantization of Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for post-training quantization struggle to support weight–activation joint quantization and extreme low-bit weight quantization. |
| Approach: | They propose a framework that addresses weight–activation joint quantization and extreme weight quantization. |
| Outcome: | The proposed framework achieves superior performance under both W4A4 and highly aggressive W2 settings while incurring negligible additional computational overhead. |