Fine-Tuning

← Series hub ← Previous | Next → In the era of LLMs/SLMs, the classic data science proverb: “Garbage In, Garbage Out” has never been more relevant. When performing Supervised Fine-Tuning (SFT) for Small Language Models (SLMs), data quality and format dictate over 90% of the model’s downstream capabilities. Feeding millions of raw, web-scraped dialogue pairs or low-quality synthetic data directly into your model will overfit it to repetitive phrasing, restrict its reasoning capabilities, and waste thousands of GPU hours. ...

← Series hub ← Previous | Next → Full-parameter fine-tuning of a large language model is a luxury. For even an 8B model like Llama 3, updating all weights in 16-bit precision requires massive clusters far beyond the reach of mid-sized teams or startups. To resolve these hardware barriers, Parameter-Efficient Fine-Tuning (PEFT) methods were developed, with LoRA and QLoRA emerging as the dominant paradigms. They allow developers to train multi-billion parameter models on a single consumer GPU (like an RTX 3090, 4090, or A10G) while maintaining near-zero performance degradation compared to full tuning. ...

Answer-first: A clear decision framework for AI engineers: when to fine-tune (LoRA/QLoRA), when to prompt-engineer, and when RAG is the right answer instead. Three engineers on the same team are trying to build the same thing: a customer support assistant that answers questions in the company’s specific support style, using terminology from their product documentation. One engineer says “just write a better system prompt.” Another says “we need to fine-tune a model.” The third says “this is clearly a RAG problem.” ...