Generative AI from Basic to Advance - LLM Fine-Tuning 16: Preference Alignment & Preference Training in LLMs with RLHF, RLAIF, DPO, LoRA

Master Generative AI: From Basics to Breakthroughs in AI Models and RAG Systems

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26 learners

What you'll learn

Understand the evolution of generative AI from classical to modern techniques
Apply RAG and LangChain for building scalable AI applications
Implement end-to-end pipelines using LlamaIndex and LLM fine-tuning methods
Deploy AI models with CI/CD pipelines and container orchestration tools

This course includes

124.5 hours of video
Certificate of completion
Access on mobile and TV

Summary

Keywords

python programming generative ai langchain sunny savita rag LLM FineTuning FineTuning LargeLanguageModels HuggingFace Transformers LlamaFactory TransferLearning DeepLearning

Full Transcript

Preference Alignment / Preference Training Explained (Full Guide) In this video, I explain how Large Language Models learn human preferences using advanced techniques like RLHF, RLAIF, DPO, and LoRA adapters. This is a complete beginner-to-advanced breakdown with math, formulas, datasets, examples, and practical implementation. You’ll understand: ✔ What is Preference Alignment / Preference Training? ✔ Why modern LLMs require Preference Alignment for safety, helpfulness & honesty ✔ Real Human Preference Datasets (chosen vs rejected samples) ✔ RLHF Pipeline (Reward Model + PPO) ✔ RLAIF (AI-feedback alignment) ✔ DPO Training — full intuition + mathematical formula ✔ Practical DPO implementation in code ✔ LoRA Adapter for parameter-efficient fine-tuning ✔ How companies like OpenAI, Anthropic, Meta do alignment ✔ End-to-end diagram + stepwise understanding By the end, you’ll clearly understand how to create your own Human Preference Dataset (chosen vs rejected pairs) for domain-specific alignment, and how it transforms a base LLM into a safer, more helpful, and more human-aligned assistant. Material & Resources: https://github.com/sunnysavita10/Complete-LLM-Finetuning/tree/main/LLM%20Fine-Tuning-16-Preference-based-training 🔔 Like, Share & Subscribe to stay updated with the full LLM fine-tuning playlist. Got questions or topic requests? Drop a comment below 👇. 📌 Keywords Covered: #LLMFineTuning #LLMQuantization #GPTQ #PTQ #QAT #AWQ #GGUF #GGML #llamaCpp #DeepLearning #NeuralNetworkOptimization #Transformers #HuggingFace #LangChain #LangGraph #RAG #AdvancedRAG #AIAgents #AgenticAI #GenerativeAI #LLMTutorial #AIProjects #AIForDevelopers #TransferLearning #FineTuning #PretrainedModels #OpenSourceAI #LLM #MachineLearning #ArtificialIntelligence #AITutorial #Python #Chatbot #StructuredOutput #PromptEngineering #TextGeneration #Embedding #LLMWorkflow #SunnyAI #YouTubeLearning #AIautomation #AIForBusiness #EndToEndTutorial #LLMFineTuning #DomainSpecificLLM #HuggingFace #SunnySavita #AIProjects #LangChain #FineTuningTutorial #AIML #LoRA #QLoRA #AITraining #CustomLLM #PDFData #preference alignment #rlhf #dpo #ppo #rewardmodel Multimodel RAG Playlist: https://www.youtube.com/watch?v=7CXJWnHI05w&list=PLQxDHpeGU14D6dm0rmAXhdLeLYlX2zk7p&pp=gAQBiAQB RAG detailed playlist: https://www.youtube.com/watch?v=wTVTkOb3SZc&list=PLQxDHpeGU14Blorx3Ps1eZJ4XvKET1_vx&pp=gAQBiAQB GenAI Foundation Playlist: https://www.youtube.com/watch?v=ajWheP8ZD70&list=PLQxDHpeGU14D7NiPgqxC9qhKkx4jMQcDk&pp=gAQBiAQB Connect with me on social media LinkedIn: https://www.linkedin.com/in/sunny-savita/ One-to-One Call: https://topmate.io/sunny_savita10 GitHub: https://github.com/sunnysavita10 Telegram: https://t.me/aimldlds