Add DSPy GEPA optimization tutorial for mathematical reasoning

Introduce comprehensive notebook demonstrating automated prompt optimization using DSPy's GEPA (Generalized Error-driven Prompt Augmentation) optimizer on the NuminaMath-1.5 dataset.

Key features:
- Complete setup guide for both local (Ollama) and cloud (OpenRouter) LLMs
- Dataset processing and filtering for mathematical problems with numeric answers
- Baseline Chain-of-Thought implementation achieving 42.3% accuracy
- GEPA optimization workflow with error-driven feedback mechanism
- Performance improvement to 64.0% accuracy (+21.7% gain)
- Detailed evaluation and metrics tracking

The notebook showcases how GEPA automatically refines prompts by analyzing errors and generating targeted feedback, making it particularly effective for complex reasoning tasks where prompt quality significantly impacts model performance.

Includes comprehensive documentation, code examples, and performance benchmarks demonstrating the power of automated prompt engineering for mathematical reasoning tasks.

Author: behroozazarkhalili

notebooks/en/_toctree.yml

@@ -88,9 +88,11 @@
           title: Hyperparameter Optimization with Optuna and Transformers
         - local: function_calling_fine_tuning_llms_on_xlam
           title: Fine-tuning LLMs for Function Calling with the xLAM Dataset
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-        
-          
+        - local: dspy_gepa
+          title: Optimizing Language Models with DSPy GEPA
+
+
+
     - title: Computer Vision Recipes
       isExpanded: false
       sections: