Update README.md

README.md

@@ -32,4 +32,51 @@ language:
 - pl
 base_model:
 - Qwen/Qwen2-7B
----
+---
+
+
+# Marco-LLM-GLO
+
+## Introduction
+
+Marco-LLM is a series of advanced multilingual language models designed to bridge the performance gap between high-resource languages and low-resource languages. This repository contains the Marco-LLM base language model with 7 billion parameters. 
+
+The model has undergone extensive multilingual continual pretraining on a diverse dataset containing over 5 trillion tokens, with a particular focus on enhancing performance in low-resource languages while maintaining strong capabilities in high-resource languages like English and Chinese.
+
+Compared to state-of-the-art open-source language models, Marco-LLM demonstrates significant improvements in multilingual tasks, including machine translation, question answering, and reasoning across multiple languages.
+For more details, please refer to our [Hugging Face page](https://huggingface.co/AIDC-AI/Marco-LLM-GLO).
+
+## Model Details
+
+Marco-LLM includes a 7B parameter model based on the Transformer architecture. The key features of Marco-LLM are:
+
+-Multilingual Training: The model is trained on a large-scale multilingual dataset covering 29 languages, including both high-resource languages (e.g., English, Chinese) and low-resource languages (e.g., Kazakh, Nepali).
+
+-Enhanced Tokenizer: An improved tokenizer is used to better handle multilingual data, ensuring higher efficiency and accuracy in tokenization.
+
+-Post-Training: Marco-LLM supports various post-training methods, such as Supervised Fine-tuning (SFT) and Direct Preference Optimization (DPO), to further enhance performance for specific tasks and languages.
+## Usage
+
+It is not advised to use the base language models for direct text generation tasks. Instead, it is recommended to apply post-training methods such as Supervised Fine-tuning (SFT), Reinforcement Learning with Human Feedback (RLHF), or continued pretraining to adapt the models for specific use cases.
+
+
+## Citation
+
+If you find our work helpful, please give us a citation.
+```
+@article{unique_identifier,
+
+title={Marco-LLM: Bridging Languages via Massive Multilingual Training for Cross-Lingual Enhancement},
+
+journal={arXiv},
+
+volume={},
+
+number={2412.04003},
+
+year={2024},
+
+url={https://arxiv.org/abs/2412.04003}
+
+}
+```