QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
akhaliq
Abstract
Recently years have witnessed a rapid development of large language models (LLMs). Despite the strong ability in many language-understanding tasks, the heavy computational burden largely restricts the application of LLMs especially when one needs to deploy them onto edge devices. In this paper, we propose a quantization-aware low-rank adaptation (QA-LoRA) algorithm. The motivation lies in the imbalanced degrees of freedom of quantization and adaptation, and the solution is to use group-wise operators which increase the degree of freedom of quantization meanwhile decreasing that of adaptation. QA-LoRA is easily implemented with a few lines of code, and it equips the original LoRA with two-fold abilities: (i) during fine-tuning, the LLM's weights are quantized (e.g., into INT4) to reduce time and memory usage; (ii) after fine-tuning, the LLM and auxiliary weights are naturally integrated into a quantized model without loss of accuracy. We apply QA-LoRA to the LLaMA and LLaMA2 model families and validate its effectiveness in different fine-tuning datasets and downstream scenarios. Code will be made available at https://github.com/yuhuixu1993/qa-lora.
Community
Here is an ML-generated summary
Objective
The paper proposes QA-LoRA, a quantization-aware low-rank adaptation method to efficiently fine-tune and deploy large language models by balancing the degrees of freedom between quantization and adaptation.
Insights
- Quantization-awareness is important for joint optimization of quantization and adaptation. Otherwise post-training quantization causes accuracy loss.
- There is an imbalance between the degrees of freedom of quantization and adaptation in methods like QLoRA. This imbalance causes large quantization errors.
- Introducing group-wise operations increases quantization flexibility and reduces adaption parameters to achieve better balance.
- QA-LoRA allows end-to-end INT4 quantization without post-training quantization. It is efficient and achieves higher accuracy than QLoRA.
- QA-LoRA works well across different model sizes, tasks, and is particularly effective in very low-bit quantization like INT2/INT3.
Implementation
- Quantization-awareness is important for joint optimization of quantization and adaptation. Otherwise post-training quantization causes accuracy loss.
- There is an imbalance between the degrees of freedom of quantization and adaptation in methods like QLoRA. This imbalance causes large quantization errors.
- Introducing group-wise operations increases quantization flexibility and reduces adaption parameters to achieve better balance.
- QA-LoRA allows end-to-end INT4 quantization without post-training quantization. It is efficient and achieves higher accuracy than QLoRA.
- QA-LoRA works well across different model sizes, tasks, and is particularly effective in very low-bit quantization like INT2/INT3.
Results
Experiments on LLaMA models show QA-LoRA consistently outperforms QLoRA in accuracy, especially in low-bit quantization, while being efficient without post-training quantization.
This is really cool!
Here is a summary of the key points from the paper:
The paper proposes a new method called Quantization-Aware Low-Rank Adaptation (QA-LoRA) for efficient fine-tuning and deployment of large language models (LLMs).
QA-LoRA introduces quantization awareness into the low-rank adaptation process. It balances the degrees of freedom between quantization and adaptation by using group-wise operations.
This allows QA-LoRA to quantize the LLM weights into low-bit integers during fine-tuning to reduce memory and computation. The quantized weights can be merged with the low-rank adapter weights after fine-tuning without accuracy loss.
Experiments on LLaMA and LLaMA2 models show QA-LoRA outperforms methods like QLoRA and PEQA, especially for smaller models and lower bit widths. It is efficient in both tuning and inference.
Steps to reproduce QA-LoRA:
Obtain pretrained LLM like LLaMA or LLaMA2 as the base model
Define quantization settings - group size, bit width
Implement the group-wise quantization and pooling operations
Initialize and optimize the low-rank adapter parameters
Quantize weights during fine-tuning backprop and merge with adapters after fine-tuning
Evaluate on benchmarks like MMLU, commonsense QA
FAQ:
Q: What is the key advantage of QA-LoRA?
A: QA-LoRA allows efficient deployment of quantized LLMs without accuracy loss that normally occurs during post-training quantization.
Q: How does QA-LoRA work?
A: It uses group-wise quantization and low-rank adapters to balance the degrees of freedom and reduce quantization errors.
Q: What models can use QA-LoRA?
A: QA-LoRA can work with any large pretrained language model like BERT, GPT-3, T5 as the base model.
Q: Does QA-LoRA improve accuracy over baseline methods?
A: Yes, QA-LoRA shows gains over QLoRA and PEQA especially for smaller models and aggressive quantization.
Q: What are the limitations of QA-LoRA?
A: The group size hyperparameter may need tuning for optimal efficiency-accuracy tradeoff. More analysis on very low-bit quantization can be done.
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning (2023)
- FPTQ: Fine-grained Post-Training Quantization for Large Language Models (2023)
- OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models (2023)
- Norm Tweaking: High-performance Low-bit Quantization of Large Language Models (2023)
- ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers (2023)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
Hi Team,
Need your help.
How we can use QA-LoRA for Vision Foundation models like OWL-ViT and Grounding Dino? If we need to deploy the VFM in edge devices we need to reduce the memory size of VFM. Can we use QA-LoRA for this purpose.
Your feedback and reference link will be useful.
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper