Fine-tuning — Domain-Specializing Models with LoRA

Short summary

Fine-tuning, specifically LoRA (Low-Rank Adaptation), embeds domain knowledge directly in the model rather than retrieving documents at query time like RAG. Unlike full fine-tuning, LoRA trains only 0.1–1% of model parameters, enabling efficient training on CPU with minimal cost. The tutorial compares RAG vs. fine-tuning across knowledge freshness, hallucination risk, and deployment cost, then provides production-ready Python code for dataset preparation, training, and inference with open-source tools.

• •LoRA fine-tuning trains only 0.1–1% of parameters, enabling CPU-based training at lower cost than full fine-tuning
• •RAG retrieves documents at query time; fine-tuning embeds knowledge in the model, trading knowledge freshness for lower hallucination and no retrieval costs
• •Includes production-ready Python examples using Hugging Face, PyTorch, and scikit-learn for dataset preparation and model evaluation

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