Update README.md

README.md

@@ -50,11 +50,11 @@ For more details, please refer to our [Hugging Face page](https://huggingface.co
 
 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).
+- 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.
+- 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.
+- 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.