collaborative-tutor-qwen25

Collaborative Domain Knowledge Tutor (Qwen2.5-7B-Instruct)

A fine-tuned Qwen2.5-7B-Instruct model specialized in collaborative problem-solving and domain knowledge tutoring, trained on 1,781 synthetic examples implementing the Usefulness, Inspiration, and Trust framework.

🎯 Model Description

This model has been fine-tuned to excel in collaborative learning scenarios, providing:

• Step-by-step guidance for complex problems
• Critical thinking stimulation and engagement
• Real-world application of academic concepts
• Collaborative planning and conflict resolution
• Motivational support for learners

🏗️ Architecture

• Base Model: Qwen/Qwen2.5-7B-Instruct
• Training Method: LoRA (Low-Rank Adaptation)
• Training Data: 1,781 synthetic collaborative learning examples
• Framework: 11-aspect evaluation (Usefulness, Inspiration, Trust)

📊 Performance Improvements

*Note: Trust decrease is a known limitation being addressed in future iterations.

🚀 Usage

Load and Use the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "christinekwon/collaborative-tutor-qwen25")

# Generate response
prompt = "Student: I'm struggling with this algebra problem. Can you help me work through it step by step?
Tutor:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Example Prompts

• Academic Support: "I don't understand how photosynthesis works. Can you explain it?"
• Collaborative Planning: "We need to work together on this group project. How should we approach this?"
• Creative Problem Solving: "We're stuck on this creative project. How can we think outside the box?"
• Motivational Support: "I'm feeling discouraged about my progress. Can you help me stay motivated?"

🔬 Training Details

• Training Data: 1,781 synthetic dialogue examples
• Data Generation: Gemini 2.5 Pro API
• Training Framework: PyTorch + Transformers
• Optimization: LoRA with 4-bit quantization
• Training Time: ~2 hours on Colab A100 GPU

📚 Research Context

This model implements the collaborative learning framework from our research on:

• 11 User-Perceived Aspects of educational interactions
• Usefulness, Inspiration, and Trust as primary outcomes
• Data-driven weights learned via Random Forest (R²=0.841)
• Critical thinking as the highest-weighted aspect (0.157)

⚠️ Limitations

• Trust scores show some decrease compared to base model
• Peer teaching capabilities need improvement
• Consistency across different domains varies
• Memory requirements for full model loading

🔮 Future Work

• Address trust and consistency issues
• Expand training data with more diverse scenarios
• Improve peer-to-peer learning capabilities
• Optimize for lower memory requirements

📄 Citation

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

@misc{collaborative-tutor-qwen25,
  title={Collaborative Domain Knowledge Tutor: Fine-tuned Qwen2.5-7B-Instruct for Collaborative Learning},
  year={2025},
  url={https://huggingface.co/christinekwon/collaborative-tutor-qwen25}
}

📞 Contact

For questions, feedback, or collaboration opportunities, please reach out through the Hugging Face Hub.

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This model is designed for educational and research purposes. Please use responsibly and in accordance with applicable guidelines.