论文简述:LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language ModelsQuantization is an indispensable technique for serving Large Language Models (LLMs) and has recently found its way into LoRA fi, 视频播放量 396、弹幕量 0、点赞数 9、投硬币枚数
qlora & zhihu | QLoRA: Efficient Finetuning of Quantized LLMs 这一工作在LLM发展史上具有里程碑意义,Quantization Aware Training + LoRA让其能够在显存占用极小、用时较短的情况下在特定领域数据集上进行快速Alignment而不过多下降精度。 借助Firefly 框架,可以在8h和1张3090上对一个7B的语言模型bai...
(lora-fine-tuning-aware quantization) is a novel quantization framework that simultaneously quantizes an llm and finds a proper low-rank initialization for lora fine-tuning. such an initialization alleviates the discrepancy between the quantized and full-precision model and significantly improves the ...
Quantization is an indispensable technique for serving Large Language Models (LLMs) and has recently found its way into LoRA fine-tuning. In this work we focus on the scenario where quantization and LoRA fine-tuning are applied together on a pre-trained model. In su...
As illustrated in Figure 1(b), the fine-tuning performance drops as the quantization bit decreases when applying QLoRA. Moreover, it is noteworthy that QLoRA fails below the 3-bit level. In this paper, we introduce a novel quantization framework, called LoRA-Fine-Tuning-aware Quantization (...
python3 quant_scripts/sample_lora_intmodel.py Fine-tuned EfficientDM Weights ModelDatasetLink LDM-4 ImageNet https://drive.google.com/file/d/1xSGY5lXnBhXK9beq3j1NUXkFRr76mDLO/view BibTeX If you find this work useful for your research, please consider citing: @inproceedings{he2024efficientdm...