shivs28/jee_nujan_math_expert

JEE NUJAN Math Expert 🎯📚

The Ultimate JEE Mathematics AI Tutor - Fine-tuned Specialist

This is a fine-tuned version of JEE NUJAN Mix v2 Base specifically trained on JEE-style mathematics problems to excel at Indian competitive exam mathematics.

🏆 Model Details

• Base Model: shivs28/jee_nujan_mix_v2_base
• Fine-tuning Dataset: 500+ JEE-relevant mathematics problems from MATH dataset
• Training Steps: 150 (optimized for mathematical reasoning)
• LoRA Configuration: Rank 32, Alpha 64 (high-performance setup)
• Specialization: JEE Main & Advanced mathematics problems

🎯 Mathematical Capabilities

This model excels at:

Core JEE Topics

• Algebra: Quadratic equations, inequalities, sequences & series
• Calculus: Limits, derivatives, integrals, applications
• Coordinate Geometry: Lines, circles, parabolas, ellipses, hyperbolas
• Trigonometry: Identities, equations, inverse functions
• Probability: Conditional probability, distributions, combinatorics
• Number Theory: Divisibility, modular arithmetic, prime numbers
• Vector Algebra: Dot product, cross product, scalar triple product

Problem-Solving Approach

• Step-by-step Solutions: Clear mathematical progression
• Multiple Methods: Shows different approaches when applicable
• Error Prevention: Highlights common JEE mistakes
• Time-Efficient: Optimized for exam conditions

🚀 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "shivs28/jee_nujan_math_expert"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

# JEE problem format
jee_prompt = '''<|problem|>
Find the number of real solutions of the equation x³ - 3x² + 2x - 1 = 0 in the interval [0, 3].

<|solution|>'''

inputs = tokenizer(jee_prompt, return_tensors="pt")
outputs = model.generate(
    **inputs, 
    max_length=800, 
    temperature=0.1,  # Low temperature for mathematical accuracy
    do_sample=True,
    pad_token_id=tokenizer.pad_token_id,
    repetition_penalty=1.05
)

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

Advanced JEE Problem

complex_problem = '''<|problem|>
In triangle ABC, if a = 7, b = 8, c = 9, find:
1. The area of triangle ABC
2. The radius of the circumscribed circle
3. The radius of the inscribed circle

<|solution|>'''

# Generate comprehensive solution
inputs = tokenizer(complex_problem, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_length=1200,
    temperature=0.05,  # Very low for multi-step problems
    top_p=0.95,
    do_sample=True,
    pad_token_id=tokenizer.pad_token_id
)

⚙️ Recommended Generation Settings

For JEE Main Problems

generation_config = {
    "max_length": 800,
    "temperature": 0.1,
    "top_p": 0.95,
    "do_sample": True,
    "repetition_penalty": 1.05,
    "pad_token_id": tokenizer.pad_token_id
}

For JEE Advanced Problems

advanced_config = {
    "max_length": 1200,      # Longer for complex solutions
    "temperature": 0.05,     # Very low for accuracy
    "top_p": 0.9,
    "do_sample": True,
    "repetition_penalty": 1.1,
    "pad_token_id": tokenizer.pad_token_id
}

🎯 Training Details

• Architecture: LoRA fine-tuning on base model
• Training Data: Carefully curated JEE-relevant problems
• Optimization: Focused on mathematical reasoning patterns
• Validation: Tested on held-out JEE problems

LoRA Configuration

• Rank (r): 32
• Alpha: 64
• Dropout: 0.1
• Target Modules: All attention and MLP layers
• Trainable Parameters: ~2.1% of total parameters

🏅 Best Practices for JEE Preparation

1. Use specific problem format: Always use <|problem|> and <|solution|> tags
2. Low temperature: Use 0.05-0.1 for mathematical accuracy
3. Adequate length: Set max_length based on problem complexity
4. Multiple attempts: Try different seeds for various solution approaches
5. Verify results: Always cross-check mathematical calculations

📈 Use Cases

For Students

• Practice Problems: Generate solutions with explanations
• Concept Clarification: Understand mathematical reasoning
• Exam Preparation: Practice with JEE-style problems
• Error Analysis: Learn from common mistakes

For Educators

• Solution Generation: Create detailed problem solutions
• Teaching Aid: Step-by-step mathematical explanations
• Problem Variation: Generate similar problems for practice
• Assessment: Evaluate student understanding

🔧 Technical Specifications

• Base Architecture: Transformer-based language model
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Precision: 16-bit floating point
• Context Length: 768 tokens (optimized for detailed solutions)
• Vocabulary: Extended with mathematical notation

📝 Citation

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

@model{jee_nujan_math_expert,
  title={JEE NUJAN Math Expert: Fine-tuned Mathematics Specialist},
  author={shivs28},
  year={2025},
  url={https://huggingface.co/shivs28/jee_nujan_math_expert}
}

🤝 Contributing

Found an issue or have suggestions? Open an issue on the model repository!