1. Why Fine-Tune GPT-4 When You Have Prompt Engineering? “Give me six hours to chop down a tree and I will spend the first four sharpening the axe.”—Abraham Lincoln Prompt engineering is a sharp axe. But at some point, you need a custom blade. I’ve spent enough time wrestling with GPT models to know this: … Read more

1. Why Fine-Tuning Matters (Brief but Insightful) “Give me six hours to chop down a tree, and I will spend the first four sharpening the axe.” — LincolnThat’s how I think about fine-tuning. It’s not a shortcut. It’s preparation done right. I’ve seen fine-tuning outperform training from scratch more times than I can count — … Read more

1. Why TinyLlama? “Sometimes, smaller isn’t just faster — it’s smarter.” I’ve fine-tuned a bunch of models over the past few months — Mistral, Phi, even the newer LLaMA variants. But when I stumbled upon TinyLlama, it hit a sweet spot I didn’t expect. If you’re working with constrained resources — say, a single A100 … Read more

1. Why I Fine-Tuned Gemini Instead of Using It Out-of-the-Box “A model that knows everything—until it needs to know your everything.” That pretty much sums up my experience with base Gemini. When I first got access to Gemini Pro via Google Cloud, I was genuinely impressed by the zero-shot reasoning. It was fast, fluent, and … Read more

1. Intro: Why I Still Fine-Tune GPT-3 in 2025 “Just because you’ve got a spaceship doesn’t mean you stop using the plane.” It’s 2025. GPT-4-Turbo is running laps, and everyone’s building apps on top of APIs. But me? I’m still fine-tuning GPT-3 — and not out of nostalgia. I’ve personally worked on projects where GPT-3, … Read more

1. Quick Context: Why Fine-Tune GPT-2 in 2025? “Just because you have a hammer, doesn’t mean everything needs to be a nail.”That’s how I see the GPT-4 hype sometimes. Yes, GPT-4 is powerful. Yes, it’s everywhere. But here’s the deal: not every project needs a 100B+ parameter monster chewing through tokens on rented A100s. I … Read more

1. Why Fine-Tune LLaMA 3 Instead of Just Prompting? “Give a man a prompt and you solve one task. Teach a model through fine-tuning, and you automate that task forever.” I’ve worked with LLMs long enough to know that prompting can only take you so far. I remember this specific internal project where we were … Read more

1. Why Fine-Tune LLaMA 2 Instead of Using It Out-of-the-Box? “You don’t need a scalpel to slice bread — unless the bread is custom-made and laced with data-specific requirements.” That’s pretty much how I explain fine-tuning to other folks on my team. Here’s the deal: I’ve worked on projects where prompt engineering just couldn’t cut … Read more

1. Intro: Why Fine-Tune LLaMA Instead of Starting from Scratch? “Give me a GPU and a foundation model, and I’ll show you results that a scratch-built model could only dream of.” When I first started working with LLaMA, I wasn’t aiming to reinvent anything. I just needed a model that could adapt quickly to my … Read more