Welcome to **LLM Fine-tuning Fundamentals**, a comprehensive course designed for developers, AI researchers, and machine learning enthusiasts who possess a basic to intermediate understanding of programming (primarily Python) and machine learning concepts. This course aims to equip you with the practical skills needed to fine-tune large language models (LLMs) and leverage them to build autonomous AI agents—systems capable of independent decision-making, task execution, and interaction with environments.
– Understand the core principles of LLMs and the necessity of fine-tuning.
– Learn hands-on techniques for data preparation, model training, and evaluation.
– Explore advanced methods like parameter-efficient fine-tuning to optimize resource usage.
– Apply fine-tuned LLMs to construct autonomous agents that can reason, plan, and act.
– Gain experience through code examples, exercises, and two practical demos.
Prerequisites
– Basic Python programming.
– Familiarity with machine learning basics (e.g., neural networks, loss functions).
– Access to a GPU-enabled environment (recommended for training; alternatives like Google Colab are suggested). – Installed libraries: `transformers`, `datasets`, `peft`, `torch`, `accelerate` (installation instructions provided in modules).
Course Instructor
