danxh
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Math-MCQ-Generator-v1

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 ---
-base_model: deepseek-ai/deepseek-math-7b-instruct
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:deepseek-ai/deepseek-math-7b-instruct
-- lora
-- transformers
 ---
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-### Framework versions
-- PEFT 0.17.1

 ---
+title: Math-MCQ-Generator-v1
+emoji: 🧮
+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 Gradio Interface (Recommended)
+Visit the Spaces page to interact with the model through a user-friendly interface.
+### 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, "your-username/math-mcq-generator-v1")
+tokenizer = AutoTokenizer.from_pretrained("your-username/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
+## 🤝 Collaboration
+This model is part of a collaborative effort to create specialized educational AI tools. A companion Physics MCQ generator is also available.
+## 📄 License
+MIT License - Feel free to use, modify, and distribute.
+## 🙏 Acknowledgments
+- Base model by DeepSeek AI
+- Training infrastructure supported by Hugging Face
+- Educational content expertise from domain specialists