Update README.md

README.md

@@ -13,4 +13,62 @@ metrics:
 base_model:
 - meta-llama/Meta-Llama-3-8B
 pipeline_tag: translation
----
+---
+
+# LaMaTE
+
+- **Github:** https://github.com/NiuTrans/LaMaTE/
+- **Paper:** https://arxiv.org/abs/2503.06594
+
+## Model Description
+
+LaMaTE is a high-performance and efficient translation model developed based on Llama-3-8B. 
+It utilizes large language models (LLMs) as machine translation(MT) encoders, paired with lightweight decoders.
+The model integrates an adapter to bridge LLM representations with the decoder, employing a two-stage training strategy to enhance performance and efficiency.
+
+**Key Features of LaMaTE**
+- Enhanced Efficiency: Offers 2.4× to 6.5× faster decoding speeds.
+- Reduced Memory Usage: Reduces KV cache memory consumption by 75%.
+- Competitive Performance: Exhibits robust performance across diverse translation tasks.
+
+
+## A Quick Start
+For more detailed usage, please refer to [github](https://github.com/NiuTrans/LaMaTE)
+
+**Note:** Our implementation is developed with transformers v4.39.2. 
+We recommend installing this version for best compatibility.
+
+To deploy LaMaTE, utilize the ```from_pretrained()``` method followed by the ```generate()``` method for immediate use:
+
+```python
+from modeling_llama_seq2seq import LlamaCrossAttentionEncDec
+from transformers import AutoTokenizer, AutoConfig
+
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
+model = LlamaCrossAttentionEncDec.from_pretrained(model_name_or_path, config=config)
+
+prompt = "Translate the following text from English into Chinese.\nEnglish: The harder you work at it, the more progress you will make.\nChinese: ",
+input_ids = tokenizer(prompt, return_tensors="pt")
+outputs_tokenized = model.generate(
+    **input_ids,
+    num_beams=5,
+    do_sample=False
+)
+outputs = tokenizer.batch_decode(outputs_tokenized, skip_special_tokens=True)
+print(outputs) 
+```
+
+
+## Citation
+
+```
+@misc{luoyf2025lamate,
+      title={Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation}, 
+      author={Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, Jingbo Zhu},
+      year={2025},
+      eprint={2503.06594},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```