Version 1.0.0 - Initial Release

Complete LLM and Generative AI Study Materials

��� What's Included

• 335-page comprehensive study guide covering transformers, pre-training, fine-tuning, RLHF, and advanced techniques
• 4 hands-on Jupyter notebooks for practical implementation
• 47 educational visualizations across 6 categories
• LaTeX source files for study material compilation
• Python visualization scripts for custom diagram generation

��� Key Features

Theoretical Coverage:

• Transformer architecture and self-attention mechanisms
• Pre-training methodologies and optimization
• Fine-tuning with LoRA, QLoRA, and PEFT
• Reinforcement Learning from Human Feedback (RLHF)
• Direct Preference Optimization (DPO)
• Retrieval-Augmented Generation (RAG)
• LangChain and LangGraph frameworks
• Safety, ethics, and bias mitigation

Practical Implementations:

• Production-ready code examples with extensive documentation
• Hyperparameter guidance with justifications
• Common pitfalls and debugging strategies
• Memory optimization techniques
• Step-by-step implementation walkthroughs

��� Repository Statistics

• 66 files
• 169,657+ lines of content
• 6 visualization categories
• 12 major topic sections

��� Getting Started

git clone https://github.com/ayanalamMOON/GenAi_Prep.git
cd GenAi_Prep
pip install -r requirements.txt

See the README for detailed usage instructions and learning path.

��� Target Audience

• University students studying machine learning and NLP
• Researchers exploring LLM techniques
• AI practitioners implementing generative AI systems
• Self-learners seeking comprehensive LLM knowledge

��� Study Material Highlights

Section 8 (RLHF) - Recently Enhanced:

• Complete RLHF pipeline (SFT → Reward Model → PPO)
• Production TRL implementation with detailed explanations
• DPO as alternative to PPO-RLHF
• Hyperparameter guidance boxes
• Common pitfalls and debugging tips
• Key takeaways for each subsection