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MemoryDecoder-Qwen-law
Clover-Hill/MemoryDecoder-Qwen-law
MemoryDecoder-Qwen-law is a machine learning model from Clover-Hill. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This Memory Decoder model is trained on the Law domain and can be adapted to enhance any model in the Qwen2 and Qwen2.5 families.
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At a glance
- License
- apache-2.0
- Model type
- qwen2
- Access
- Public
- Created
- Oct 20, 2025
- Updated
- Oct 20, 2025
- SHA
- 8d8f794c
Base models
Model card
From the Hugging Face model README
Model Description
This Memory Decoder model is trained on the Law 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
Law Domain Dataset: AsyLex
Test Split: MemoryDecoder-domain-data
Performance Results
Qwen2 Family
| Model | Base Model | Base + MemDec |
|---|---|---|
| Qwen2-0.5B | 10.23 | 4.57 |
| Qwen2-1.5B | 7.69 | 4.32 |
| Qwen2-7B | 5.92 | 4.00 |
| Qwen2-72B | 4.84 | 3.69 |
Qwen2.5 Family
| Model | Base Model | Base + MemDec |
|---|---|---|
| Qwen2.5-0.5B | 9.86 | 4.57 |
| Qwen2.5-1.5B | 7.42 | 4.29 |
| Qwen2.5-3B | 6.68 | 4.16 |
| Qwen2.5-7B | 5.94 | 4.01 |
| Qwen2.5-14B | 5.35 | 3.86 |
| Qwen2.5-32B | 5.18 | 3.81 |
| Qwen2.5-72B | 4.84 | 3.70 |
Perplexity scores on Law 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: [email protected]
- Type
- qwen2
- License
- apache-2.0
- Languages
- en
- Created
- Oct 20, 2025
- Updated
- Oct 20, 2025