Created by Haotong Qin, Xudong Ma, Xingyu Zheng, Xiaoyang Li, Yang Zhang, Shouda Liu, Jie Luo, Xianglong Liu and Michele Magno from Beihang University, Bytedance AI Lab, and ETH Zürich. [arXiv]
This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retention. The LoRA-finetuning quantization of LLMs has been extensively studied to obtain accurate yet compact LLMs for deployment on resource-constrained hardware. However, existing methods cause the quantized LLM to severely degrade and even fail to benefit from the finetuning of LoRA. This paper proposes a novel IR-QLoRA for pushing quantized LLMs with LoRA to be highly accurate through information retention. The proposed IR-QLoRA mainly relies on two technologies derived from the perspective of unified information: (1) statistics-based Information Calibration Quantization allows the quantized parameters of LLM to retain original information accurately; (2) finetuning-based Information Elastic Connection makes LoRA utilizes elastic representation transformation with diverse information. Comprehensive experiments show that IR-QLoRA can significantly improve accuracy across LLaMA and LLaMA2 families under 2-4 bit-widths, e.g., 4-bit LLaMA-7B achieves 1.4% improvement on MMLU compared with the state-of-the-art methods. The significant performance gain requires only a tiny 0.31% additional time consumption, revealing the satisfactory efficiency of our IR-QLoRA. We highlight that IR-QLoRA enjoys excellent versatility, compatible with various frameworks (e.g., NormalFloat and Integer quantization) and brings general accuracy gains.
This tutorial uses the Alpaca dataset as an example to perform IR-QLoRA fine-tuning on the LLaMA-7B model in PyTorch.
The irqlora.py code is a starting point for finetuning and inference on various datasets. Basic command for finetuning a baseline model on the Alpaca dataset:
python irqlora.py --model_name_or_path <path_or_name>We provide a script in scripts/finetune.sh file for fine-tuning on alpaca, and you are free to adjust its parameters to get the results you want.
To evaluate the fine-tuned model, you can use the evaluate_mmlu.py script. Basic command for evaluating a fine-tuned model on the MMLU dataset:
python evaluate_mmlu.py --model_name_or_path <path_or_name> --lora_ckpt_dir <lora_checkpoint_dir>We provide a script in scripts/evaluate_mmlu.sh file for evaluating on mmlu (5-shot).
