Hebrew Math Tutor (Intel/hebrew-math-tutor-v1) brings advanced mathematical problem-solving capabilities directly to Hebrew speakers, providing detailed step-by-step reasoning entirely in Hebrew without sacrificing the computational accuracy that makes these models valuable for education.Advanced mathematical AI models like those trained on competition mathematics datasets have shown remarkable problem-solving abilities. However, they primarily operate in English, creating barriers for non-English speaking educational communities. Hebrew speakers, in particular, have faced challenges accessing these powerful educational tools in their native language.Simply translating outputs isn’t enough—effective mathematical tutoring requires natural language flow, culturally appropriate explanations, and seamless integration of Hebrew text with mathematical notation. This requires a more sophisticated approach.Hebrew Math Tutor addresses these challenges through targeted fine-tuning of Qwen3-4B-Thinking-2507, a powerful 4-billion parameter mathematical reasoning model. Our approach focuses on three key principles:The model provides complete mathematical explanations in natural Hebrew while preserving mathematical notation and formal expressions. It understands Hebrew mathematical terminology and can explain complex concepts using appropriate pedagogical language.By carefully fine-tuning rather than training from scratch, we maintain the model’s core mathematical reasoning capabilities while adapting its communication style to Hebrew.At ~4 billion parameters, the model strikes an optimal balance between capability and computational efficiency, making it practical for educational applications and research prototyping.Creating an effective Hebrew math model required more than simple translation. Our methodology involved:We selected ~10,000 high-quality problems from the OpenMathReasoning dataset, translating questions and answers to Hebrew while preserving the original reasoning chains and mathematical notation.We fine-tuned the model over 3 epochs with optimized parameters (learning rate 5e-6, 0.1 warmup, cosine scheduling) to adapt the output language while maintaining the underlying reasoning capabilities.The model’s internal ... reasoning blocks remain in English, as these represent core computational processes that would require more extensive training to modify.We evaluated Hebrew Math Tutor against its base model on three challenging mathematical benchmarks: MATH500 (curriculum problems), AIME24, and AIME25 (competition mathematics). The results demonstrate significant improvements in Hebrew language output while maintaining strong technical performance.🚀 Dramatic Hebrew Language Gains: Hebrew answer production jumped from 35-75% to 95-100% across all benchmarks—a transformative improvement for Hebrew-speaking users.📈 Consistent Accuracy Improvements: Notable gains in pass@16 scores on Hebrew evaluations, showing the model doesn’t just translate but actually improves problem-solving in Hebrew contexts.🔄 Preserved Core Capabilities: Maintained competitive English performance, demonstrating that Hebrew specialization didn’t compromise the model’s fundamental mathematical abilities.⚖️ Nuanced Majority Vote Results: While performance improved on MATH500 and remained stable on AIME24, there’s an interesting decrease in maj@16 on AIME25 that provides insights for future training approaches.Hebrew Math Tutor opens new possibilities across multiple domains:Hebrew Math Tutor integrates seamlessly with the Transformers ecosystem:

Hebrew Math Tutor in action: A Streamlit interface showing detailed step-by-step reasoning in Hebrew. The expandable reasoning sections allow users to dive deep into the mathematical process or focus on final answers. While Hebrew Math Tutor represents significant progress, responsible deployment requires careful consideration:The model works best as an educational aid rather than a replacement for qualified instruction. We recommend implementing human oversight, providing clear disclaimers about AI-generated content, and ensuring compliance with relevant privacy regulations in educational applications.Hebrew Math Tutor demonstrates that language barriers in AI can be effectively addressed through thoughtful fine-tuning approaches. This work represents more than just a Hebrew mathematical model—it's a proof of concept for making advanced AI capabilities truly accessible across linguistic communities.The techniques developed here can be adapted for other languages, creating a pathway toward more inclusive mathematical AI tools. As we continue to refine these approaches, we're moving closer to a future where language is no longer a barrier to accessing the most advanced educational technologies.Hebrew Math Tutor is available now under the Apache-2.0 license. We encourage the community to:🚀 Start exploring Hebrew Math Tutor today and experience mathematical AI that truly speaks your language.Built with gratitude upon the foundational work of Qwen3-4B-Thinking-2507 and the OpenMathReasoning dataset.·
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