Update readme.md

README.md

@@ -141,18 +141,62 @@ The following results of the **Arabic-QwQ** and **QwQ-Preivew** models were anal
 
 1. An example illustrating how base models generate Chinese responses when provided with an Arabic question:
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/WheOO0ZgoCkwwKQ55kLsS.png)
 
+> [!NOTE]
+> The fine-tuned model effectively resolves the issue of unintended Chinese interference in the responses, delivering clear and accurate answers in Arabic.
 
+2. An example demonstrating how base models sometimes respond in English unless explicitly instructed to answer in Arabic. In contrast, the fine-tuned model seamlessly responds in Arabic without requiring additional instructions, simply by providing the question in Arabic.
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/GFvctZu9rN7SHgv8BQUjR.png)
 
+> [!NOTE]
+> Although the base model provides the correct answer, it is often in English, making it challenging for Arabic users to understand unless they are proficient in English.
 
+3. At times, the base models provide additional context, resulting in unnecessarily lengthy answers. The fine-tuned model addresses this issue by focusing on delivering concise, straightforward solutions without extra context.
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/IX4oUhVZyAo4aiJQ9WQa1.png)
 
 
+4. There are instances where both the base and fine-tuned models perform well in answering the query, demonstrating their capability to comprehend and provide accurate responses. However, the fine-tuned model consistently outperforms by aligning more closely with the requirements of Arabic users. 
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/apKq-ip2XestSEQacunof.png)
 
 
+## How to Use
 
+To utilize the Arabic-QwQ model effectively:
 
+1. **Use Unsloth for Faster Inference**  
+   We recommend using [Unsloth](https://github.com/unslothai/unsloth) to load and perform inference with the model. This method is optimized for speed and offers better performance compared to traditional loading methods.
 
+2. **Incorporate Prompt Templates for Structured Instructions**  
+   For more specific instructions or complex tasks, use a prompt template to guide the model's responses. For example, structure your input like:  
+   ```plaintext
+        prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
+        
+        ### Instruction:
+        {}
+        
+        ### Response:
+        {}"""
+   ```
 
+## Acknowledgments
+
+We would like to express our gratitude to Prince Sultan University for their support in the development and fine-tuning of this model. Their contributions were invaluable in making this work possible.
+
+## Citation
+
+If you use this model in your research or application, please cite it as follows:
+
+```plaintext
+@misc{Arabic_QWQ,
+  author = {Omer Nacar},
+  title = {Arabic-QwQ: Fine-tuned QwQ LLM for Arabic Reasoning and Understanding},
+  year = {2024},
+  url = {https://huggingface.co/Omartificial-Intelligence-Space/Arabic-QWQ-32B-Preview},
+  institution = {Prince Sultan University},
+  note = {Fine-tuned version of the QwQ-32B model for Arabic-specific tasks.}
+}
+```