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Clover-Hill/MemoryDecoder-Llama-finance

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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Model Description

This Memory Decoder model is trained on the Finance domain and can be adapted to enhance any model in the Llama3, Llama3.1, and Llama3.2 families.

[!IMPORTANT] These Llama models are initialized from Qwen models with the embedding layer adapted to fit the Llama tokenizer. This enables efficient cross-model family knowledge transfer.

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

Llama3 Family

Model	Base Model	Base + MemDec
Llama3-8B	8.63	4.32
Llama3-70B	6.87	4.01

Llama3.1 Family

Model	Base Model	Base + MemDec
Llama3.1-8B	8.46	4.30
Llama3.1-70B	6.68	3.97

Llama3.2 Family

Model	Base Model	Base + MemDec
Llama3.2-1B	11.85	4.85
Llama3.2-3B	9.70	4.45

Perplexity scores on Finance domain test set. Lower is better.

Citation

bibtex

@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