--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Intel/hebrew-math-tutor-v1/blob/main/LICENSE pipeline_tag: text-generation language: - he - en tags: - mathematics - education - hebrew - reasoning - math - tutoring base_model: - Qwen/Qwen3-4B-Thinking-2507 --- # Hebrew Math Tutor

**Hebrew Math Tutor** is a specialized mathematical reasoning model that provides step-by-step solutions to math problems in Hebrew. Built on Qwen3-4B-Thinking-2507, this model bridges the gap between advanced AI mathematical capabilities and Hebrew-language education. - 🎯 **Model ID**: `Intel/hebrew-math-tutor-v1` - 🤖 **Demo**: [Intel/hebrew-math-tutor](https://huggingface.co/spaces/Intel/hebrew-math-tutor) - 🏗️ **Base Model**: [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) - 🏛️ **Architecture**: Decoder-only causal language model (~4B parameters) - 🗣️ **Primary Language**: Hebrew (retains multilingual capabilities) - 📄 **License**: Apache-2.0 ## Model Description Hebrew Math Tutor is a supervised fine-tune of Qwen3-4B-Thinking, specifically optimized to: - **Provide detailed mathematical reasoning in Hebrew** with clear step-by-step explanations - **Maintain mathematical accuracy** while adapting to Hebrew language patterns - **Preserve multilingual capabilities** for cross-language mathematical workflows - **Support educational applications** with natural Hebrew mathematical discourse The model excels at translating complex mathematical concepts into clear, pedagogically sound Hebrew explanations while maintaining the computational precision of its base model. ## Intended Use Cases ### ✅ **Primary Applications** - **Educational Technology**: Hebrew-language math tutoring systems and learning platforms. - **Research Tools**: Mathematical reasoning research in Hebrew educational contexts. - **Prototype Development**: Building Hebrew-first educational AI applications. - **Accessibility**: Providing advanced math AI assistance to Hebrew-speaking communities. ### ✅ **Secondary Applications** - Multilingual educational workflows requiring Hebrew mathematical explanations. - Cross-cultural mathematics education research. - Hebrew mathematical content generation for educational materials. ### ❌ **Not Intended For** - **High-stakes assessments**: Medical, legal, or financial decision-making. - **Unsupervised grading**: Certification or evaluation without human verification. - **Production systems**: Critical applications without proper validation and oversight. ## Model Details | **Specification** | **Details** | |-----------------------|--------------------------------------------------| | **Architecture** | Decoder-only transformer (causal language model) | | **Parameters** | ~4 billion | | **Context Length** | Inherited from Qwen3-4B-Thinking-2507 | | **Tokenizer** | Qwen3-compatible tokenizer with Hebrew support | | **Training Type** | Supervised Fine-Tuning (Hebrew SFT) | | **Base Model** | Qwen3-4B-Thinking-2507 | | **Fine-tuning Focus** | Mathematical reasoning in Hebrew | ## Training Details ### **Dataset** - **Source**: ~10,000 selected problems from [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning). - **Translation Approach**: Automated high-quality translation using internal LLMs. - **Language Adaptation**: Questions and final answers translated to Hebrew; reasoning chains preserved. - **Mathematical Notation**: Equations and formal math notation kept intact. - **Internal Reasoning**: Model's `...` blocks intentionally remain in English (representing internal reasoning processes). ### **Training Configuration** - **Method**: Supervised Fine-Tuning (Hebrew SFT) - **Epochs**: 3 - **Learning Rate**: 5e-6 - **Warmup**: 0.1 - **Scheduler**: Cosine learning rate decay - **Objective**: Maintain mathematical accuracy while adapting output to Hebrew ## Performance Evaluation We evaluated Hebrew Math Tutor on three challenging mathematical benchmarks: **MATH500**, **AIME24**, and **AIME25**. ### **Evaluation Metrics** - **pass@16**: Percentage of problems where at least one of 16 generated samples is correct. - **maj@16**: Majority-vote accuracy across 16 samples. - **Hebrew Answers**: Percentage of responses generated in Hebrew. ### **Hebrew Evaluation Results** | Dataset | Metric | Base Model | Hebrew Math Tutor | Improvement | |-------------|----------------|------------|-------------------|-------------| | **MATH500** | pass@16 | 93% | **95%** | +2% | | | maj@16 | 88% | **90%** | +2% | | | Hebrew Answers | 75% | **100%** | +25% | | **AIME24** | pass@16 | 76.7% | **80%** | +3.3% | | | maj@16 | 76.7% | **76.7%** | No change | | | Hebrew Answers | 35.2% | **96.7%** | +61.5% | | **AIME25** | pass@16 | 80% | **83.3%** | +3.3% | | | maj@16 | 70% | **60%** | -10% | | | Hebrew Answers | 36% | **95.2%** | +59.2% | ### **English/Original Language Results** | Dataset | Metric | Base Model | Hebrew Math Tutor | Change | |-------------|---------|------------|-------------------|-----------| | **MATH500** | pass@16 | 99% | **98%** | -1% | | | maj@16 | 98% | **98%** | No change | | **AIME24** | pass@16 | 93.3% | **90%** | -3.3% | | | maj@16 | 86.7% | **86.7%** | No change | | **AIME25** | pass@16 | 83.3% | **90%** | +6.7% | | | maj@16 | 73% | **80%** | +7% | ### **Key Findings** 🎯 **Dramatic Language Improvement**: Hebrew answer generation increased by 25-61.5% across all benchmarks, reaching 95-100% Hebrew output. 📈 **Maintained Technical Performance**: Consistent improvements in pass@16 on Hebrew evaluations while preserving competitive English performance. 🔍 **Mixed Majority Vote Results**: Strong performance on MATH500, stable on AIME24, with one notable decrease on AIME25 requiring further investigation. ✅ **Preserved Core Capabilities**: The fine-tuning successfully adapted language output without sacrificing fundamental mathematical reasoning abilities. ## Usage ### **Quick Start** ```python from transformers import pipeline model = "Intel/hebrew-math-tutor-v1" pipe = pipeline("text-generation", model) messages = [ { "role": "system", "content": """You are a helpful AI assistant specialized in mathematics and problem-solving who can answer math questions with the correct answer. Answer shortly, not more than 500 tokens, but outline the process step by step. Answer ONLY in Hebrew!""", }, {"role": "user", "content": "מהו סכום הסדרה הבאה: 1 + 1/2 + 1/4 + 1/8 + ..."}, ] out = pipe( messages, return_full_text=False, max_new_tokens=1024, temperature=0.6, top_p=0.95, top_k=20, ) print(out[0]["generated_text"]) ``` ### **Recommended Parameters** - **Temperature**: 0.6 (balanced creativity and accuracy) - **Top-p**: 0.95 (diverse but focused sampling) - **Top-k**: 20 (controlled vocabulary selection) - **Max tokens**: 500-1024 (sufficient for detailed explanations) ### **Best Practices** - **Request explicit structure**: Ask for step-by-step reasoning and clearly marked final answers. - **Use Hebrew formatting cues**: Include phrases like "תשובה סופית:" or request `\boxed{}` formatting. - **Specify language**: Explicitly request Hebrew-only responses for consistent output. - **Verify solutions**: Always validate mathematical results, especially in educational contexts. ## Demo Interface


Example Streamlit interface showing Hebrew Math Tutor providing step-by-step reasoning. The detailed reasoning can be collapsed for cleaner presentation.

## Limitations & Considerations ### **Technical Limitations** - **Potential errors**: May produce incorrect solutions or mathematical hallucinations. - **Language mixing**: Occasional mixing of Hebrew and English or inconsistent number formatting. - **Training biases**: May reflect biases present in the original training datasets. - **Internal reasoning**: `...` blocks remain in English due to training scope. ### **Usage Recommendations** - **Human verification required**: Always validate outputs before use in educational settings - **Not a replacement for educators**: Designed as an assistive tool, not a substitute for qualified instruction. - **Appropriate context**: Best suited for educational prototyping and research applications. ## Ethical Guidelines ### **Responsible Deployment** - Include clear disclaimers about AI-generated content in user-facing applications. - Implement human oversight for any educational or assessment applications. - Ensure compliance with relevant privacy laws when collecting user data. - Provide transparency about model capabilities and limitations. ### **Educational Impact** - Designed to enhance, not replace, human mathematical instruction. - Intended to increase accessibility of advanced math AI for Hebrew speakers. - Should be used as part of comprehensive educational approaches with human guidance. ## Technical Details ### **Evaluation Methodology** - **Correctness verification**: Solutions validated using Math-verify framework. - **Statistical significance**: Results based on 16 samples per problem for robust evaluation. - **Language detection**: Automated classification of response language for Hebrew Answers metric. - **Benchmark diversity**: Evaluation across competition mathematics (AIME) and curriculum problems (MATH500). ### **Reproducibility** - All evaluation protocols follow standard mathematical reasoning assessment practices. - Sampling parameters and evaluation metrics clearly documented. - Training configuration and hyperparameters provided for reproduction. ## Attribution & Licensing - **Model License**: [Apache-2.0](https://huggingface.co/Intel/hebrew-math-tutor-v1/blob/main/LICENSE) - **Base Model**: [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) (Alibaba) - **Training Dataset**: [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) (NVIDIA) - **Development**: Intel Labs ## Citation If you use Hebrew Math Tutor in your research or applications, please cite: ```bibtex @misc{hebrew-math-tutor-v1, title={Hebrew Math Tutor: A Hebrew-focused Mathematical Reasoning Model}, author={Intel AI}, year={2025}, url={https://huggingface.co/Intel/hebrew-math-tutor-v1}, note={Fine-tuned from Qwen3-4B-Thinking-2507} } ``` ## Community & Support - **Blog Post**: [more details in the blog](https://huggingface.co/blog/danf/hebrew-math-tutor). - **Model Repository**: [https://huggingface.co/Intel/hebrew-math-tutor-v1](https://huggingface.co/Intel/hebrew-math-tutor-v1) - **Issues & Feedback**: Use the Hugging Face repository issues for bug reports and feature requests. - **Community Discussions**: Join conversations in the repository discussions tab. ## Changelog - **v1.0** — Initial public release with Hebrew mathematical reasoning capabilities. --- *Hebrew Math Tutor represents a step forward in making advanced mathematical AI accessible across languages. We encourage responsible use and welcome community feedback to improve multilingual mathematical reasoning capabilities.*