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---
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
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/62d93cd728f9c86a4031562e/YxvxPWRpINziJaAftl4XE.png" width="600"/>
</p>
**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**: [IntelLabs/hebrew-math-tutor](https://huggingface.co/spaces/IntelLabs/hebrew-math-tutor)
- 📔 **Blog**: [Hugging Face blog](https://huggingface.co/blog/danf/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 `<think>...</think>` 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
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/62d93cd728f9c86a4031562e/tbOIu47QLmja_z-Ce20a2.png" width="600"/>
<br>
<em>Example Streamlit interface showing Hebrew Math Tutor providing step-by-step reasoning. The detailed reasoning can be collapsed for cleaner presentation.</em>
</p>
## 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**: `<think>...</think>` 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.*

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"</think>": 151668,
"</tool_call>": 151658,
"</tool_response>": 151666,
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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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},
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},
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},
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},
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},
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},
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},
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},
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}

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vocab.json (Stored with Git LFS) Normal file
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