RefinedNeuro/RN_TR_R2: Turkish Reasoning Model
RefinedNeuro/RN_TR_R2 is an 8 billion parameter language model specifically designed for Turkish-language reasoning tasks. It was fine-tuned from ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1 using the GRPO (Generalized Reinforcement-Preference Optimization) method, leveraging distilled Q&A data from the Qwen3 model.
Key Capabilities & Performance
- Specialized Reasoning: Excels in open-ended reasoning across various domains, including STEM (Mathematics, Physics, Chemistry, Biology, Geometry, Trigonometry, Statistics), history, and Turkish culture.
- Benchmark Performance: Achieves a score of 82.4% on the RN_TR_R2_Benchmark_Results, an evaluation focused on open-ended Turkish culture and reasoning questions. This represents a 17.6 percentage point improvement over its RN_TR_R1 baseline and outperforms Qwen3-8B, ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1, and Meta-Llama-3.1-8B-Instruct on this specific benchmark.
- Training Data: Fine-tuned on a 13,000-example dataset (
RefinedNeuro/Qwen3-Reasoning-Distill-Q-A-Dataset) covering 6th-12th grade STEM subjects, ensuring robust reasoning capabilities.
Intended Use Cases
- Question Answering: Highly effective for complex Turkish questions requiring step-by-step reasoning.
- Educational Tools: Ideal for developing educational applications in Turkish, particularly for subjects like math, physics, chemistry, biology, geometry, trigonometry, statistics, history, and culture.
Limitations
While strong in reasoning, it is not recommended for generating creative fiction or tasks requiring memorized facts outside its specific training scope. Like all LLMs, it may exhibit hallucination.