Fine-Tuning LLMs
Adapt pre-trained language models to specific tasks with fine-tuning, LoRA, and dataset preparation strategies. Fine-Tuning vs Prompting Fine-tuning adapts the model's weights for specific tasks. Prompting works with pre-trained knowledge via instructions alone. Fine-tuning excels at: learning specialized formats, improving consistency, reducing prompt length, domain-specific knowledge. Prompting is cheaper and faster. Start with prompting, only fine-tune when you need reliability improvements that prompting can't achieve. LoRA (Low-Rank Adaptation) LoRA trains small rank-decomposition matrices instead of full model weights. Benefits: train on a single GPU (even for 70B models), multiple LoRAs can be swapped without full model copies, LoRA weights are tiny (MBs vs GBs). QLoRA quantizes the base model to 4-bit and trains LoRA adapters. Start with rank 16-32, increase for more capacity, decrease for less overfitting. Dataset Preparation Dataset quality is more important than quantity. 1000 high-quality examples beat 100K noisy ones. Include diverse edge cases. Format consistently: use the same instruction template, output format, and separator tokens. Split into train/validation/test. Remove duplicates, fix errors, balance classes. For instruction tuning: use diverse instructions covering the range of expected tasks. Evaluation Hold out test examples that represent real usage. Compare fine-tuned vs base model. Track: task accuracy, output format compliance, response length, hallucination rate. Human evaluation is essential — automated metrics miss nuance. Use A/B testing in production. Fine-tuning can regress on unrelated tasks — test on a broad evaluation set.