Fine-Tuning Language Models with Foundry

Off-the-shelf artificial intelligence models are powerful, but adapting them to your specific business domain is what unlocks their true potential. This written course guides you through the core principles and practical steps of tailoring large language models to deliver precise, consistent results. By reading through this comprehensive guide, you will transition from using general-purpose models to successfully preparing datasets and applying advanced tuning methodologies. What you'll learn: - Understand foundational fine-tuning concepts, key terminology, and model architectures before modifying weights. - Prepare high-quality training datasets manually and explore synthetic data generation to maximize model consistency. - Apply supervised fine-tuning workflows to adapt models to specific instruction formats and domain-specific vocabulary. - Explore direct preference optimization and alignment techniques to guide model behavior and safety. - Evaluate model performance using standard validation metrics to ensure accurate and reliable outputs. - Implement basic MLOps best practices for versioning datasets and tracking your training runs. The course begins with essential definitions and data preparation fundamentals, then moves step-by-step through configuring and executing training runs. You will learn to analyze the final outputs to ensure alignment with your target goals. This text-only course is designed for beginners, developers, and data enthusiasts looking to understand language model customization, with no prior machine learning experience required. Start reading today to unlock the full potential of customized language models.