LLM Fine-Tuning and Customization

This LLM Fine-Tuning course equips you with the skills to optimize and deploy domain-specific large language models for advanced Generative AI applications. Begin with foundational concepts—learn supervised fine-tuning, parameter-efficient methods (PEFT), and reinforcement learning with human feedback (RLHF). Master data preparation, hyperparameter tuning, and key evaluation strategies. Progress to implementation using LLM frameworks and libraries, and apply best practices for model selection, bias monitoring, and overfitting control. Conclude with hands-on demos—fine-tune Falcon-7B and build an image generation app using LangChain and OpenAI DALL·E. You should have a solid background in Python, deep learning fundamentals, and prior exposure to large language models. By the end of this course, you will be able to: - Fine-tune LLMs using PEFT, RLHF, and supervised methods - Prepare datasets and optimize hyperparameters for tuning - Evaluate and deploy fine-tuned models using GenAI frameworks - Apply tuning concepts in real-world use cases like Falcon-7B and DALL·E apps Ideal for AI developers, ML engineers, and GenAI researchers

• Fine-tune LLMs using supervised, PEFT, and RLHF techniques
• Prepare and organize datasets for efficient model training
• Optimize model accuracy with hyperparameter tuning and bias monitoring
• Build generative AI applications using fine-tuned models like Falcon-7B and DALL·E

• Experience with Python and deep learning fundamentals
• Basic understanding of large language models