What is Fine-Tuning

How It Works

Fine-tuning initializes a model with pretrained weights and trains it further on domain-specific data with a task-specific objective. The pretrained features serve as a strong starting point, and training adjusts them to better capture domain-specific patterns. This requires significantly less data and compute than training from scratch because the model already understands general patterns.

Technical Details

Full fine-tuning updates all model parameters on the new data. Parameter-efficient methods include LoRA (Low-Rank Adaptation, adding small trainable matrices), QLoRA (quantized LoRA for memory efficiency), prefix tuning, and adapter layers. Typical fine-tuning uses 100-100K labeled examples, 1-10 epochs, and learning rates 10-100x smaller than pretraining. Evaluation should use held-out data from the target domain to measure actual task performance.

Best Practices

• Start with parameter-efficient methods (LoRA) before attempting full fine-tuning
• Use a validation set from the target domain to prevent overfitting and select the best checkpoint
• Monitor both target task performance and general capability preservation during fine-tuning
• Use data augmentation to increase effective training set size for small datasets

Common Pitfalls

• Overfitting to a small fine-tuning dataset, losing generalization ability
• Using too many epochs or too high a learning rate, causing catastrophic forgetting
• Fine-tuning on noisy or low-quality data that teaches the model bad patterns
• Not evaluating on out-of-distribution examples to test robustness

Advanced Tips

• Fine-tune multimodal models (CLIP, BLIP-2) on domain-specific paired data for specialized retrieval
• Use QLoRA to fine-tune large language models on consumer GPUs with minimal memory
• Implement multi-task fine-tuning to preserve general capabilities while adding specialized skills
• Apply reinforcement learning from human feedback (RLHF) for alignment-style fine-tuning