Model Description
This Memory Decoder model is trained on the Biomedical 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
Biomedical Domain Dataset: mimic_iii_diagnosis_anonymous
Test Split: MemoryDecoder-domain-data
Performance Results
Qwen2 Family
| Model | Base Model | Base + MemDec |
|---|---|---|
| Qwen2-0.5B | 18.41 | 3.75 |
| Qwen2-1.5B | 12.42 | 3.68 |
| Qwen2-7B | 8.36 | 3.59 |
| Qwen2-72B | 6.15 | 3.45 |
Qwen2.5 Family
| Model | Base Model | Base + MemDec |
|---|---|---|
| Qwen2.5-0.5B | 17.01 | 3.74 |
| Qwen2.5-1.5B | 11.33 | 3.67 |
| Qwen2.5-3B | 9.70 | 3.63 |
| Qwen2.5-7B | 8.19 | 3.57 |
| Qwen2.5-14B | 7.01 | 3.51 |
| Qwen2.5-32B | 6.65 | 3.48 |
| Qwen2.5-72B | 5.90 | 3.44 |
Perplexity scores on Biomedical 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