Module 13: Fine-Tuning Fundamentals

Chapter Overview

Pre-trained language models are powerful general-purpose tools, but they often fall short on specialized tasks that require domain-specific knowledge, a particular output style, or strict formatting. Fine-tuning bridges this gap by adapting a pre-trained model to your specific use case through additional training on curated data. The result is a model that retains its broad language understanding while gaining the ability to excel at your particular task.

This module covers the complete fine-tuning workflow from first principles. You will learn when fine-tuning is the right approach (and when prompting or RAG is a better alternative), how to prepare high-quality training data in the correct format, and how to run supervised fine-tuning with Hugging Face TRL. The module also covers API-based fine-tuning through providers like OpenAI and Google, fine-tuning for embedding and classification tasks, and strategies for adapting models to handle longer contexts.

By the end of this module, you will be able to make informed decisions about when to fine-tune, prepare datasets in standard formats, execute training runs with appropriate hyperparameters, monitor training progress, and adapt models for specialized tasks including classification, representation learning, and long-context processing.

Learning Objectives

Apply a decision framework to choose between prompting, RAG, and fine-tuning for a given task
Prepare training datasets in standard formats (Alpaca, ShareGPT, ChatML) with appropriate splits and balancing strategies
Execute supervised fine-tuning using Hugging Face Trainer and TRL, selecting appropriate hyperparameters for learning rate, batch size, warmup, and weight decay
Use provider APIs (OpenAI, Google Vertex AI) for fine-tuning and evaluate trade-offs between ease, control, and cost
Fine-tune encoder and decoder models for representation learning and embedding tasks
Add and train classification heads for single-label, multi-label, and token-level classification tasks
Apply context extension techniques (RoPE scaling, position interpolation) to adapt models for longer input sequences
Monitor training with W&B and TensorBoard, diagnose common issues, and mitigate catastrophic forgetting

Prerequisites

Module 05: Tokenization (understanding how text is converted to tokens)
Module 06: Transformer Architecture (self-attention, positional encoding, feed-forward layers)
Module 07: Pre-training (training objectives, loss functions, training dynamics)
Module 09: LLM APIs (API usage, structured outputs)
Module 12: Synthetic Data Generation (creating training data at scale)
Familiarity with PyTorch, gradient descent, and basic training loops

Chapter Overview

Learning Objectives

Prerequisites

Sections