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--- |
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title: Math-MCQ-Generator-v1 |
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colorFrom: blue |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 4.0.0 |
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app_file: app.py |
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pinned: false |
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license: mit |
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tags: |
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- text-generation |
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- mathematics |
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- mcq-generation |
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- education |
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- fine-tuned |
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- deepseek-math |
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--- |
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# Math-MCQ-Generator-v1 |
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## Model Description |
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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. |
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## Capabilities |
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- **Subject**: Mathematics |
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- **Question Types**: Multiple Choice Questions (MCQs) |
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- **Topics**: Applications of Trigonometry, Conic Sections, and more |
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- **Difficulty Levels**: Easy, Medium, Hard |
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- **Cognitive Skills**: Recall, Direct Application, Pattern Recognition, Strategic Reasoning, Trap Aware |
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## Training Information |
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- **Base Model**: `deepseek-ai/deepseek-math-7b-instruct` |
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- **Training Method**: QLoRA (4-bit quantization) |
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- **Dataset Size**: 1519 examples |
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- **Training Epochs**: 5 |
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- **Final Loss**: ~0.20 |
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- **Training Date**: 2025-09-03 |
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## Usage |
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### Via Python API |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from peft import PeftModel |
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# Load model |
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base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-math-7b-instruct") |
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model = PeftModel.from_pretrained(base_model, "danxh/math-mcq-generator-v1") |
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tokenizer = AutoTokenizer.from_pretrained("danxh/math-mcq-generator-v1") |
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# Generate MCQ |
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prompt = '''### Instruction: |
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Generate a math MCQ similar in style to the provided examples. |
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### Input: |
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chapter: Applications of Trigonometry |
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topics: ['Heights and Distances'] |
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Difficulty: medium |
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Cognitive Skill: direct_application |
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### Response: |
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''' |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.7) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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``` |
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## Performance |
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The model demonstrates strong performance in generating contextually appropriate mathematics MCQs with: |
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- Proper question formatting |
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- Relevant multiple choice options |
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- Appropriate difficulty scaling |
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- Subject-matter accuracy |
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## License |
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MIT License - Feel free to use, modify, and distribute. |
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