🤖 Enhanced AI Tutor System using LLaMA-3 and LangGraph
An adaptive, feedback-based AI tutor system built using:
- 🧠 Meta's LLaMA-3.2-3B-Instruct
- 🔄 LangGraph for multi-agent workflow
- ⚡ Hugging Face Transformers (4-bit quantization for efficiency)
- ✅ PyTorch, BitsandBytes, Accelerate for seamless GPU usage
🎓 What It Does
This notebook walks you through a complete interactive tutor session that:
- 📚 Asks a question from a topic you choose
- 📝 Evaluates your answer and gives structured feedback
- 🧪 Generates a new practice question
- 📈 Tracks your progress and adapts difficulty
It's like having your own AI teacher, personalized to your learning!
📄 View Notebook in Colab
You can explore the full .ipynb notebook on Google Colab using the button above.
📁 Project Structure
├── EnhancedTutorSystem.ipynb
├── README.md
├── requirements.txt
🧠 Model Info
This project uses (but does not rehost) Meta's official instruction-tuned model:
The model is loaded via transformers using 4-bit quantization (BitsAndBytes)
Note: You must agree to Meta's license to access the model.
🎯 Features
- ✍️ Adaptive questions across difficulty levels
- 📊 Real-time performance tracking
- 🤓 Intelligent feedback on every answer
- 💡 LangGraph-powered multi-agent workflow
- 🧵 Fully reproducible session history
🔜 Coming Soon
- 🌐 A Hugging Face Space with a user-friendly UI
- 📝 Student progress export to PDF
- 🎯 Topic-based quiz sessions
- 🧪 Integration with LangChain for evaluation metrics
📄 License
This project is released under the MIT License.
🙌 Acknowledgments
- 🧠 Meta AI for LLaMA-3
- 🔄 LangGraph by LangChain
- 🤗 Hugging Face for open infrastructure
📬 Contact / Feedback
Feel free to raise issues or suggestions on GitHub
Or connect via Hugging Face community tab!
Happy learning! 💡
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meta-llama/Llama-3.2-3B-Instruct