LLM Fine-Tuning and Customization Training

Overview

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.

Syllabus

Foundations of LLM Fine-Tuning

Explore the foundations of LLM fine-tuning in this comprehensive module. Learn core principles of large language model (LLM) fine-tuning, from supervised and parameter-efficient methods (PEFT) to reinforcement learning with human feedback (RLHF). Gain hands-on experience in data preparation and hyperparameter tuning through real-world demos to optimize GenAI performance.

Evaluation, Implementation, and Best Practices

Master LLM fine-tuning evaluation and deployment in this hands-on module. Learn to optimize and assess fine-tuned models, explore key libraries and frameworks, and implement best practices for data preparation, model selection, and bias monitoring. Apply concepts in real-time through demos including tuning Falcon-7B and building an AI image generation app with LangChain and DALL·E.