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README.md

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----
-base_model: shivs28/jee_nujan_mix_v2_base
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:shivs28/jee_nujan_mix_v2_base
-- lora
-- transformers
----
+# 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](https://huggingface.co/shivs28/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
+```python
+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
+```python
+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
+```python
+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
+```python
+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:
+
+```bibtex
+@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!
 
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-## How to Get Started with the Model
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-### Framework versions
-
-- PEFT 0.17.0