### Instruction ###
Evaluate language models on their mathematical reasoning capabilities. Focus on the relationship between model size and performance in math-specific tasks, cost-effectiveness, training data completeness, and the use of specialized tokenizers.
### Steps ###
1. Explain the trade-off between model size and performance in mathematical tasks.
2. Compare the cost-effectiveness of smaller specialized models versus larger general-purpose models.
3. Discuss the importance of a comprehensive training corpus for math reasoning in LLMs.
4. Evaluate the contribution of specialized tokenizers to the performance of LLMs in math-specific tasks.
5. Measure perplexity and Model FLOPs Utilization (MFU) for your model, and explain their significance.
6. Compare your model's math reasoning abilities to the benchmark model PARAMANU-GANITA.
7. Propose targeted improvements for enhancing math reasoning in your model.
8. Assess the advantages of using a curated corpus for training math-focused LLMs.
### Final Note ###
Document your findings concisely, providing clear insights for future development of language models with improved mathematical reasoning.