GPT-2 Research & Development Hub

A comprehensive research repository exploring transformer architectures, training methodologies, and conversational AI development with GPT-2.

🎯 Repository Overview

This repository contains multiple interconnected projects focused on understanding and advancing GPT-2 language models through hands-on implementation and experimentation.

🚀 Current Projects

✅ Base Model (GPT-2 From Scratch)

Status: Complete
Focus: Transformer architecture implementation and training dynamics

• Full GPT-2 architecture (768 hidden, 12 layers, 12 heads)
• Trained on TinyShakespeare dataset
• Custom BPE tokenizer implementation
• Comprehensive W&B tracking

📖 View Base Model Documentation

✅ Chat Model (Conversational Fine-tuning)

Status: Complete
Focus: Supervised fine-tuning for conversational AI

• GPT-2 fine-tuned on OpenAssistant dataset
• Proper conversation formatting and loss masking
• User/Assistant interaction handling
• Inference optimizations for chat applications

💬 View Chat Model Documentation

🔄 RLHF Model (Reinforcement Learning from Human Feedback)

Status: Planned
Focus: Alignment and preference learning

• Reward model training
• PPO policy optimization
• Human preference dataset integration
• Safety and alignment improvements

🎯 View RLHF Documentation (Coming Soon)

📊 Experiment Tracking

All experiments are tracked using Weights & Biases for:

• Training metrics and loss curves
• Model checkpoints and artifacts
• Hyperparameter sweeps
• Generated sample comparisons

📈 View Complete W&B Workspace

🔬 Research Focus Areas

1. Architecture Understanding

• Transformer component analysis
• Attention pattern visualization
• Layer-wise learning dynamics
• Scaling behavior studies

2. Training Methodologies

• From-scratch training strategies
• Fine-tuning approaches
• Efficient training techniques
• Compute optimization

3. Conversational AI

• Chat formatting and processing
• Multi-turn conversation handling
• Context management strategies
• Response quality evaluation

4. Alignment & Safety (Planned)

• Reward model development
• Human preference learning
• Safety filtering mechanisms
• Bias mitigation strategies

📈 Key Results & Findings

Base Model Training

• Convergence: Validation loss: 8.7 → 0.06 over 5 epochs
• Generation Quality: Coherent Shakespeare-style text generation
• Training Stability: Successful gradient management and attention stability

Chat Model Fine-tuning

• Conversation Quality: Natural user-assistant interactions
• Context Handling: Effective multi-turn conversation management
• Response Relevance: Contextually appropriate responses

Technical Insights

• Initialization: Critical for transformer stability
• Loss Masking: Essential for chat model training
• Temperature Tuning: Lower temperatures (0.1-0.2) optimal for chat
• Compute Efficiency: Sliding window maximizes data utilization

🎯 Future Roadmap

Phase 1: Enhancement (Current)

• Complete RLHF implementation
• Add comprehensive evaluation metrics
• Implement QLoRA/LoRA fine-tuning
• Create unified inference API

Phase 2: Scaling (Q2 2025)

• Multi-GPU training support
• Larger model variants (GPT-2 Medium/Large)
• Custom dataset integration
• Performance optimization

Phase 3: Applications (Q3 2025)

• Web-based chat interface
• API deployment
• Mobile optimization
• Domain-specific fine-tuning

🤝 Contributing

Contributions are welcome!:

• Code style and standards
• Experiment documentation
• Pull request process
• Issue reporting

🙏 Acknowledgments

• Andrej Karpathy - Educational content and inspiration
• OpenAI - GPT-2 architecture and pre-trained models
• OpenAssistant - High-quality conversational dataset
• HuggingFace - Transformer implementations and tooling
• Weights & Biases - Experiment tracking and collaboration

📄 Citation

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

@software{adewumi2025gpt2research,
  author = {Emmanuel Ayobami Adewumi},
  title = {GPT-2 Research \& Development Hub},
  year = {2025},
  url = {https://github.com/SCCSMARTCODE/gpt2-research-hub}
}

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Emmanuel Ayobami Adewumi
AI Research Engineer

Building the future of AI through systematic research and open collaboration 🚀