Upload README.md

README.md

@@ -4,21 +4,23 @@ license: apache-2.0
 
 ## Introduction
 
-Aquila is a large language model trained by BAAI, and AquilaMed-RL is an industry model from Aquila language model. Based on the Aquila general pre-trained model, we continued pre-training , SFT and RL in the medical domain and obtained our AquilaMed-RL model.
+Aquila is a large language model independently developed by BAAI. Building upon the Aquila model, we continued pre-training, SFT (Supervised Fine-Tuning), and RL (Reinforcement Learning) through a multi-stage training process, ultimately resulting in the AquilaMed-RL model. This model possesses professional capabilities in the medical field and demonstrates a significant win rate when evaluated against annotated data using the GPT-4 model. The AquilaMed-RL model can perform medical triage, medication inquiries, and general Q&A. We will open-source the SFT data and RL data required for training the model. Additionally, we will release a technical report detailing our methods in developing the model for the medical field, thereby promoting the development of the open-source community.
 
 ## Model Details
 
-The pipeline of the training procedure is bellow, for more details you can read our technical report: https://github.com/FlagAI-Open/industry-application/blob/main/Aquila_med_tech-report.pdf
+The training process of the model is described as follows. For more information, please refer to our technical report. https://github.com/FlagAI-Open/industry-application/blob/main/Aquila_med_tech-report.pdf
 
 ![pipeline](./img/pipeline.png)
 
 ## Evaluation
 
+Using GPT-4 for evaluation, the win rates of our model compared to the reference answers in the annotated validation dataset are as follows.
+
 ![pipeline](./img/eval-result.jpeg)
 
 ## usage
 
-when you have downloaded the model, you can use the bellow code to run the model
+Once you have downloaded the model locally, you can use the following code for inference.
 
 ```python
 
@@ -82,8 +84,8 @@ predict: 肚子疼可能是多种原因引起的，例如消化不良、胃炎
 If you find our work helpful, feel free to give us a cite.
 
 ```
-@article{AquilaMed,
-  title={AquilaMed Technical Report},
+@article{Aqulia-Med LLM,
+  title={Aqulia-Med LLM: Pioneering Full-Process Open-Source Medical Language Models},
   year={2024}
 }
 ```