Update README.md

README.md

@@ -21,12 +21,6 @@ For more details, please refer to our [Hugging Face page](https://huggingface.co
 
 Marco-LLM-ES series includes models of varying sizes, from 7B to 72B parameters, including both base and instruction-tuned (Instruct) models. The models are based on the Transformer architecture with SwiGLU activation, attention QKV bias, and group query attention. Additionally, the models employ an improved tokenizer adaptive to multiple languages.
 
-## Requirements
-
-The Marco-LLM-ES models are compatible with the latest Hugging Face Transformers library. We recommend installing `transformers>=4.37.0` to ensure full functionality and avoid potential errors like:
-```
-KeyError: 'qwen2'
-```
 ## 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.
@@ -44,7 +38,6 @@ The datasets used for evaluation include:
 
 |  Datasets         |   Marco-LLM-ES-7B  |
 | :---------------- | :-----------------: |
-| **Spanish**       |                     |
 | Spanish           | **44.49**           |
 | Catalan           | **39.45**           |
 | Basque            | **28.66**           |
@@ -54,11 +47,20 @@ The datasets used for evaluation include:
 ## 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={},
 
-@article{marco_llm_es,
+number={2412.04003},
 
-title={Marco-LLM-ES Technical Report},
+year={2024},
 
-year={2024}
+url={https://arxiv.org/abs/2412.04003}
 
 }
+```