README.md

---
language: en
license: apache-2.0
library_name: transformers
base_model: google/gemma-2b
tags:
- mathematics
- jee
- chain-of-thought
- reasoning
- gemma
- fine-tuned
- educational
metrics:
- loss
pipeline_tag: text-generation
widget:
- text: "Question: Find the derivative of f(x) = x³ + 2x² - 5x + 3\n\nLet me solve this step by step:\n\n"
  example_title: "Calculus Problem"
- text: "Question: Solve the quadratic equation 2x² + 5x - 3 = 0\n\nLet me solve this step by step:\n\n"
  example_title: "Algebra Problem"
- text: "Question: Find the area of a triangle with sides 3, 4, and 5\n\nLet me solve this step by step:\n\n"
  example_title: "Geometry Problem"
---

# mathAI-Gemma

## Model Description

**mathAI-Gemma** is a specialized mathematical reasoning model based on Gemma 2B, fine-tuned specifically for solving JEE (Joint Entrance Examination) level mathematics problems. This model has been trained using Chain-of-Thought reasoning to provide detailed, step-by-step solutions to complex mathematical problems.

## Key Features

- 🧮 **Mathematical Reasoning**: Specialized for JEE-level mathematics
- 🔗 **Chain-of-Thought**: Provides step-by-step problem solving
- 📚 **Educational Focus**: Designed for learning and teaching
- 🎯 **High Accuracy**: Trained on curated JEE problem datasets
- 💡 **Formula Integration**: Shows relevant formulas and calculations

## Training Details

- **Base Model**: google/gemma-2b
- **Training Method**: Full fine-tuning with custom data collator
- **Training Dataset**: JEE Mathematics Problems with Chain-of-Thought reasoning
- **Problem Areas**: Algebra, Calculus, Geometry, Trigonometry, Physics Mathematics
- **Training Framework**: Hugging Face Transformers
- **Hardware**: NVIDIA A100 GPU

## Usage

### Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "kalkiai3000/mathAI-Gemma",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("kalkiai3000/mathAI-Gemma")

# Solve a math problem
question = "Find the derivative of f(x) = x³ + 2x² - 5x + 3"
prompt = f'''Question: {question}

Let me solve this step by step:

'''

inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Advanced Usage

```python
# For better results, use structured prompting
def solve_math_problem(question: str, model, tokenizer):
    prompt = f'''Question: {question}

Let me solve this step by step:

Step 1: '''

    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.3,  # Lower temperature for more focused responses
        do_sample=True,
        top_p=0.9,
        repetition_penalty=1.1,
        pad_token_id=tokenizer.eos_token_id
    )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)
```

## Model Performance

The model excels at:

- **Calculus**: Derivatives, integrals, limits, optimization
- **Algebra**: Quadratic equations, polynomials, systems of equations  
- **Geometry**: Area, volume, coordinate geometry, trigonometry
- **Physics Mathematics**: Mechanics, waves, thermodynamics calculations
- **Step-by-step reasoning**: Clear explanation of solution methodology

## Example Outputs

### Calculus Problem
```
Question: Find the derivative of f(x) = x³ + 2x² - 5x + 3

Let me solve this step by step:

Step 1: Apply the power rule to each term
Step 2: d/dx(x³) = 3x²
Step 3: d/dx(2x²) = 4x  
Step 4: d/dx(-5x) = -5
Step 5: d/dx(3) = 0

Therefore, f'(x) = 3x² + 4x - 5
```

## Limitations

- **Domain Specific**: Optimized for mathematics, may not perform well on general tasks
- **Language**: Primarily trained on English mathematical problems
- **Complexity**: Best suited for JEE-level problems (may struggle with research-level mathematics)
- **Format Dependency**: Works best with structured prompting format

## Responsible AI Usage

- Designed as an educational tool to assist learning
- Should be used alongside human verification for critical applications
- Not intended to replace mathematical education or understanding
- Users should verify results for important calculations

## Citation

If you use this model in your research or applications, please cite:

```bibtex
@misc{mathAI-Gemma,
  title={MathAI-Gemma: A Specialized Mathematical Reasoning Model for JEE Problems},
  author={kalkiai3000},
  year={2024},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/kalkiai3000/mathAI-Gemma}}
}
```

## License

This model is released under the Apache 2.0 License, following the base Gemma model licensing.

## Acknowledgments

- Google for the base Gemma 2B model
- Hugging Face for the transformers library and hosting
- JEE problem dataset contributors
- Mathematical education community

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