1. Why Fine-Tune PaLM 2 in 2025? “You can’t squeeze water from a stone—unless you teach the stone where the river is.” That’s how I’ve come to see base LLMs. They’re powerful, sure—but they don’t always get you the specificity you need. In my own projects, I’ve hit several hard walls where prompt engineering and … Read more

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