Update README.md

README.md

@@ -73,7 +73,7 @@ def generate_with_reasoning(prompt_text):
     return generated, duration, num_generated_tokens
 
 # Example Arabic math problem
-prompt = """A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer either in Arabic or English based on user's language. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer> في مدينة يبلغ عدد سكانها 1 مليون نسمة، إذا كان 60% من السكان بالغين، و40% من البالغين يعملون، فكم عدد العاملين في المدينة؟"""
+prompt_text = '''في مدينة يبلغ عدد سكانها 1 مليون نسمة، إذا كان 60% من السكان بالغين، و40% من البالغين يعملون، فكم عدد العاملين في المدينة؟'''
 
 result, time_taken, tokens = generate_with_reasoning(prompt)
 print(result)
@@ -128,7 +128,7 @@ torch==2.4.1
 The model is trained to follow a reasoning-first format:
 
 ```
-<think> First, we calculate 60% of 1 million, which is 600,000. Then, 40% of that is 240,000. </think>
+<think> أولاً، نحسب 60% من مليون نسمة، وهو 600,000. ثم نحسب 40% من هذا العدد، وهو 240,000. </think>
 <answer> 240,000 </answer>
 ```
 
@@ -168,26 +168,8 @@ The model is trained to follow a reasoning-first format:
 
 ---
 
-## 🧑‍🔬 Authors
-
-Developed and trained by **Omar Paniego** with adaptation of the DeepSeek-R1 training recipe using Hugging Face's open tools and datasets.
-
----
-
-## 📢 License
-
-Refer to the license file in the repository.
-
----
-
-## ❤️ Acknowledgements
-
-Thanks to:
-- **Hugging Face Science Team** for `trl` and `math_verify`
-- **AI-MO** for the NuminaMath-TIR dataset
-- **DeepSeek Team** for releasing their methodology and insights
-
 Happy reasoning! 🔍✨
+
 ## Citations
 
 Cite GRPO as: