Update README.md

README.md

@@ -150,4 +150,12 @@ it wan't successfull, the model couln't predict some handwritten recipes, and so
 we decided to try using: Qwen2.5-VL Vision-Language Model and the results were much better! 
 Comparison:
 
-![image](https://cdn-uploads.huggingface.co/production/uploads/6910977ace661438b728d763/tSsR5JNuGO_yNBRCxoH3s.png)
+![image](https://cdn-uploads.huggingface.co/production/uploads/6910977ace661438b728d763/tSsR5JNuGO_yNBRCxoH3s.png)
+
+
+## Challenges:
+- Synthetic Data: Creating a synthetic dataset that accurately mimicked real-world distributions was difficult. We had to carefully tune the generation parameters to ensure the data was diverse and reliable for training.
+
+- Hugging Face Deployment & Optimization:
+The Issue: We struggled initially with configuring the Hugging Face environment; the model execution was too slow for a real-time application.
+The Solution: After investigating various deployment strategies, we shifted to using the Hugging Face Inference Client. This transition optimized our inference pipeline, allowing the model to run within acceptable time limits and ensuring a smooth user experience.