Crash course: How to use and fine-tune LLMs

Ever asked a large language model (LLM) a complex question ... only to receive a baffling response?

Maybe it handles basic queries just fine, but stumbles when you need insights into your company’s internal tools or niche processes.

That’s because most LLMs are built for general use. Out-of-the-box options like Mistral-7B are powerful, but not tailored for your unique use cases.

That's why we have fine-tuning.

LLMs like Mistral-7B are powered by billions of parameters—the "knobs and dials" that shape how they process and generate text. Training all of these from scratch is astronomically expensive. But with fine-tuning—and parameter-efficient methods like LoRA—you can teach an LLM to master your domain, cost-effectively.

In this newsletter, we’ll cover:

• What fine-tuning is and why it’s a game-changer for customizing LLMs

• How to avoid pitfalls like "catastrophic forgetting"

• When to use LoRA, QLoRA, or even RAG (retrieval-augmented generation)

• A step-by-step guide to fine-tuning Mistral-7B efficiently (with code samples!)

You’ll also learn how techniques like 4-bit quantization reduce memory usage, making fine-tuning accessible even on smaller hardware setups.

By the end, you’ll know how to adapt LLMs to your specific needs—without the high cost of training from scratch.

Let’s dive in!