---
title: Math-MCQ-Generator-v1
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false
license: mit
tags:
- text-generation
- mathematics
- mcq-generation
- education
- fine-tuned
- deepseek-math
---

# Math-MCQ-Generator-v1

## Model Description

This is a fine-tuned version of `deepseek-ai/deepseek-math-7b-instruct` specialized for generating high-quality mathematics multiple choice questions (MCQs). The model has been trained using QLoRA (Quantized Low-Rank Adaptation) to efficiently adapt the base model for educational content generation.

## Capabilities

- **Subject**: Mathematics
- **Question Types**: Multiple Choice Questions (MCQs)
- **Topics**: Applications of Trigonometry, Conic Sections, and more
- **Difficulty Levels**: Easy, Medium, Hard
- **Cognitive Skills**: Recall, Direct Application, Pattern Recognition, Strategic Reasoning, Trap Aware

## Training Information

- **Base Model**: `deepseek-ai/deepseek-math-7b-instruct`
- **Training Method**: QLoRA (4-bit quantization)
- **Dataset Size**: 1519 examples
- **Training Epochs**: 5
- **Final Loss**: ~0.20
- **Training Date**: 2025-09-03

## Usage

### Via Python API
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load model
base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-math-7b-instruct")
model = PeftModel.from_pretrained(base_model, "danxh/math-mcq-generator-v1")
tokenizer = AutoTokenizer.from_pretrained("danxh/math-mcq-generator-v1")

# Generate MCQ
prompt = '''### Instruction:
Generate a math MCQ similar in style to the provided examples.

### Input:
chapter: Applications of Trigonometry
topics: ['Heights and Distances']
Difficulty: medium
Cognitive Skill: direct_application

### Response:
'''

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```

## Performance

The model demonstrates strong performance in generating contextually appropriate mathematics MCQs with:
- Proper question formatting
- Relevant multiple choice options
- Appropriate difficulty scaling
- Subject-matter accuracy

## License

MIT License - Feel free to use, modify, and distribute.