Fine-Tuning Large Language Models: A Practical Guide

# Fine-Tuning Large Language Models: A Practical Guide Fine-tuning allows you to customize LLMs for your specific needs. Here's a practical guide to doing it efficiently. ## Why Fine-Tune? ### Use Cases - Domain-specific language - Custom instruction following - Particular output format - Improved accuracy on narrow tasks ### When Not to Fine-Tune - Prompt engineering is enough - Limited training data - General knowledge tasks ## Techniques ### Full Fine-Tuning Update all model weights: - Best quality - Most expensive - Risk of catastrophic forgetting ### LoRA (Low-Rank Adaptation) Add small trainable matrices: - Much cheaper - Preserves base model - Easy to switch adapters ### QLoRA LoRA with quantized base model: - Even cheaper - Run on consumer GPUs - Slight quality trade-off ## Data Preparation ### Quality Over Quantity - Clean, consistent examples - Diverse scenarios - Proper formatting ### Format Instruction-response pairs work well. Consistent formatting is key. ## Training Tips ### Hyperparameters - Learning rate: 1e-4 to 5e-4 - Epochs: 3-5 for small datasets - Batch size: Largest that fits in memory ### Evaluation - Hold out test set - Human evaluation - Task-specific metrics ## Common Issues ### Overfitting - Use dropout - Early stopping - More data ### Quality Degradation - Larger base model - Better data - Lower learning rate ## Conclusion Fine-tuning is powerful but requires care. Start with good data and iterate based on evaluation.