Meet LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

The introduction of Pre-trained Language Models (PLMs) has signified a transformative shift within the discipline of Natural Language Processing. They have demonstrated distinctive proficiency in performing a variety of language duties, together with Natural Language Understanding (NLU) and Natural Language Generation (NLG). These fashions sometimes incorporate hundreds of thousands and even billions of parameters, resulting in substantial computational and reminiscence necessities. However, the appreciable computational and reminiscence wants of those fashions current vital challenges, as acknowledged by the analysis neighborhood.

In this paper, the authors introduce a novel quantization framework often known as LoRA-Fine-Tuning-aware Quantization (LoftQ). This framework is particularly tailor-made for pre-trained fashions that necessitate quantization and LoRA fine-tuning. The framework actively combines low-rank approximation, working at the side of quantization to collectively approximate the unique high-precision pre-trained weights.

The above picture demonstrates QLoRA efficiency with totally different bits. Left: QLoRA initialization of LLAMA-2-13b on WikiText-2. Right: Apply QLoRA to LLAMA-2-13b on WikiText-2 language modelling activity. Smaller perplexity signifies higher efficiency. 

Quantization Methods. We apply two quantization strategies to show LoftQ is appropriate with totally different quantization features:

• Uniform quantization is a basic quantization methodology. It uniformly divides a steady interval into 2N classes and shops an area most absolute worth for dequantization.

• NF4 and its 2-bit variant NF2 are quantization strategies utilized in QLoRA. They assume that the high-precision values are drawn from a Gaussian distribution and map these values to discrete slots which have equal chance.

We carry out 2-bit and 4-bit quantization on all fashions, attaining compression ratios of 25-30% and 15-20% on the 4-bit and 2-bit ranges, respectively. All the experiments are performed on NVIDIA A100 GPUs.

The analysis of their quantization framework is carried out by means of in depth experiments on varied downstream duties, together with NLU, query answering, summarization, and NLG. The outcomes of those experiments show that LoftQ constantly surpasses QLoRA throughout all precision ranges. For instance, with 4-bit quantization, they attain a 1.1 and 0.8 enchancment in Rouge-1 for XSum and CNN/DailyMail, respectively. As the sector of NLP continues to advance, it’s anticipated that additional improvements and optimizations will assist bridge the hole between the immense potential of PLMs and their sensible deployment, benefiting a variety of purposes and customers.

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