Model Fine-Tuning

Purpose: Adapt pre-trained foundation models to domain-specific tasks through parameter-efficient and full fine-tuning techniques.

When to Use This Skill

• Adapting an LLM to follow domain-specific instructions or style
• Improving model accuracy on specialized tasks (classification, extraction, generation)
• Reducing inference cost by distilling a large model into a smaller one
• Building training data pipelines for fine-tuning datasets
• Choosing between fine-tuning approaches (LoRA, QLoRA, full, distillation)

Prerequisites

• Base model selection (open-source or API-based)
• Training dataset (instruction pairs, labeled examples, or preference data)
• GPU compute (local or cloud)