Update README.md

README.md

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----
-license: apache-2.0
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+# Llama Finance MemoryDecoder
+
+---
+license: apache-2.0
+language:
+- pyt
+base_model:
+- Qwen/Qwen2.5-0.5B
+---
+
+## 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](https://www.arxiv.org/abs/2508.09874)
+
+**GitHub:** [https://github.com/LUMIA-Group/MemoryDecoder](https://github.com/LUMIA-Group/MemoryDecoder/tree/main)
+
+## Training & Evaluation Data
+
+**Finance Domain Dataset:** [yahoo_finance_stockmarket_news](https://huggingface.co/datasets/jyanimaulik/yahoo_finance_stockmarket_news)
+
+**Test Split:** [MemoryDecoder-domain-data](https://huggingface.co/datasets/Clover-Hill/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