nypgd/qwen3-4b-grpo-tr-matematik-merged
The nypgd/qwen3-4b-grpo-tr-matematik-merged model is a 4 billion parameter Qwen3-based language model specifically fine-tuned for solving Turkish mathematical problems. Developed by nypgd, this model utilizes a Supervised Fine-Tuning (SFT) followed by GRPO training methodology. It excels at understanding and solving mathematical word problems presented in Turkish, making it suitable for educational and analytical applications requiring Turkish language mathematical reasoning.
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Model Overview
The nypgd/qwen3-4b-grpo-tr-matematik-merged is a specialized 4 billion parameter language model built upon the unsloth/Qwen3-4B-Base architecture. Its primary distinction lies in its fine-tuning process, which involved Supervised Fine-Tuning (SFT) followed by GRPO (Gradient Regularized Policy Optimization) to enhance its mathematical reasoning capabilities.
Key Capabilities
- Turkish Mathematical Problem Solving: The model is specifically trained to understand and solve mathematical problems presented in Turkish.
- Specialized Training: It leverages a two-stage fine-tuning approach (SFT + GRPO) on the
NovusResearch/gsm8k-Translated-TRdataset, which consists of Turkish mathematical problems. - Reasoning Structure: Designed to output a structured thought process (
<start_working_out>and<end_working_out>) before providing the final solution (<SOLUTION>), aiding in transparency and interpretability of its problem-solving steps.
Use Cases
This model is particularly well-suited for applications requiring:
- Educational Tools: Assisting students with Turkish mathematical word problems.
- Automated Problem Solving: Developing systems that can interpret and solve mathematical challenges in Turkish.
- Language-Specific AI: Projects focused on enhancing AI's understanding and application of mathematics within the Turkish language context.