Llm Fine Tuning Guide

LLM Fine-Tuning Guide

Master the art of fine-tuning large language models to create specialized models optimized for your specific use cases, domains, and performance requirements.

Overview

Fine-tuning adapts pre-trained LLMs to specific tasks, domains, or styles by training them on curated datasets. This improves accuracy, reduces hallucinations, and optimizes costs.

When to Fine-Tune

• Domain Specialization: Legal documents, medical records, financial reports
• Task-Specific Performance: Better results on specific tasks than base model
• Cost Optimization: Smaller fine-tuned model replaces expensive large model
• Style Adaptation: Match specific writing styles or tones
• Compliance Requirements: Keep sensitive data within your infrastructure
• Latency Requirements: Smaller models deploy faster

When NOT to Fine-Tune

• One-off queries (use prompting instead)
• Rapidly changing information (use RAG instead)
• Limited training data (< 100 examples typically insufficient)
• General knowledge questions (base model sufficient)

Quick Start

Full Fine-Tuning:

python examples/full_fine_tuning.py