Fine-Tuning

1. Introduction “Tuning a model without knowing what you’re tuning for is like sharpening a knife before you know what you’re cutting.” I’ve worked on enough production models to tell you this: XGBoost’s default settings are surprisingly strong—but when you do need to fine-tune, it’s not about blindly running a grid search. It’s about knowing … Read more

1. Introduction “You don’t always need a bigger brain—sometimes, you just need a more focused one.” That pretty much sums up why I decided to fine-tune Claude. If you’re already working with Claude 2 or Claude 3, you know the models are powerful straight out of the box. In many cases, prompt engineering and retrieval … Read more

1. Introduction “When you have a hammer, everything looks like a nail. But when you’ve got a 2B parameter model, things get a bit more nuanced.” Let’s be real — most of us don’t need to train a model from scratch anymore. The cost, the infrastructure, the sheer madness of debugging early training runs… it’s … Read more

1. Introduction “When it comes to training custom object detectors, YOLOv8 makes the process feel deceptively simple—but fine-tuning it properly is where things get interesting.” In this guide, I’ll walk you through how I personally fine-tuned YOLOv8 on a custom industrial inspection dataset—something with tiny defects, overlapping parts, and inconsistent lighting. These weren’t textbook-perfect images, … Read more

1. Introduction “Speed is rarely the enemy—unless you’re sacrificing too much of your model’s mind to get it.” When I first started using transformer models for real-world NLP tasks, I leaned heavily on BERT. It was the go-to model—reliable, expressive, and battle-tested. But once projects started scaling and latency became a deal-breaker, I found myself … Read more

1. Introduction “Big models don’t scare me anymore. What scares me is debugging tokenizer mismatches at 3 A.M.” If you’re anything like me, you’ve probably already gone through enough papers, benchmarks, and huggingface model cards to last a lifetime. So I’ll get straight to the point—Falcon 7B is not just another LLM. It’s fast, open-weight, … Read more

1. Introduction: Why LoRA Still Matters in 2025 “You don’t need to move a mountain when you only want to reshape the peak.” That’s how I’d describe the shift we’ve seen from full fine-tuning to parameter-efficient approaches like LoRA. If you’ve trained large models recently, you already know how unsustainable full fine-tuning has become. Between … Read more

1. Intro “The only real way to understand these models is to break them, fine-tune them, and then run them yourself.” I’ve worked with a lot of open-weight models, but when I started experimenting with LLaMA 3, I realized there wasn’t a solid, no-nonsense guide that covered everything—from fine-tuning to exporting it in a format … Read more

1. Introduction: Why Fine-Tuning Matters for RAG “A good retrieval system isn’t just about finding relevant information—it’s about finding the right information at the right time.” I’ve worked with enough retrieval-augmented generation (RAG) pipelines to know that off-the-shelf embedding models often fall short when dealing with domain-specific data. They might be decent for general tasks, … Read more

1. Introduction “Fine-tuning a 70B parameter model? That sounds like a terrible idea.” At least, that’s what I thought when I first started working with Llama 2 70B. Let’s be real—handling a model of this scale isn’t just about slapping some GPUs together and running a few training loops. If you don’t plan your approach … Read more