Model Description
This Memory Decoder model is trained on the Finance domain and can be adapted to enhance any model in the Qwen2 and Qwen2.5 families.
Paper: Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
GitHub: https://github.com/LUMIA-Group/MemoryDecoder
Training & Evaluation Data
Finance Domain Dataset: yahoo_finance_stockmarket_news
Test Split: MemoryDecoder-domain-data
Performance Results
Qwen2 Family
| Model | Base Model | Base + MemDec |
|---|---|---|
| Qwen2-0.5B | 16.00 | 3.84 |
| Qwen2-1.5B | 10.96 | 3.61 |
| Qwen2-7B | 8.31 | 3.38 |
| Qwen2-72B | 6.62 | 3.20 |
Qwen2.5 Family
| Model | Base Model | Base + MemDec |
|---|---|---|
| Qwen2.5-0.5B | 16.04 | 3.87 |
| Qwen2.5-1.5B | 11.20 | 3.61 |
| Qwen2.5-3B | 9.83 | 3.52 |
| Qwen2.5-7B | 8.61 | 3.42 |
| Qwen2.5-14B | 7.60 | 3.31 |
| Qwen2.5-32B | 7.38 | 3.29 |
| Qwen2.5-72B | 6.80 | 3.23 |
Perplexity scores on Finance domain test set. Lower is better.
Citation
@article{cao2025memory,
title={Memory decoder: A pretrained, plug-and-play memory for large language models},
author={Cao, Jiaqi and Wang, Jiarui and Wei, Rubin and Guo, Qipeng and Chen, Kai and Zhou, Bowen and Lin, Zhouhan},
journal={arXiv preprint arXiv:2508.09874},
year={2025}
}
Contact
For questions and support: maximus.cao@outlook.com