1. Why Fine-Tune SD 3.5 Large? Let me get straight to the point—there comes a point where prompt engineering just doesn’t cut it. I’ve been there. I was trying to get Stable Diffusion to generate a specific style of technical illustration for a robotics use case. After 50+ prompt variations and CLIP interrogator tweaks, the … Read more

1. Introduction (Keep It Tight, First-Person) “You don’t truly understand a model until you’ve tried breaking it with your own data.” I’ve worked with large language models long enough to know that full fine-tuning isn’t always practical—or necessary. When I started working with LLaMA 2 and Mistral, my goal was clear: fine-tune them efficiently on … Read more

1. Why Gemma? “You don’t always need a 13B model to get 13B results.” That’s something I’ve learned firsthand after spending weeks fine-tuning various open LLMs for lightweight, on-device use cases. When I started experimenting with Gemma, I wasn’t chasing hype — I was just tired of hitting memory ceilings with LLaMA2 and constantly fighting … Read more

1. Why RoBERTa Over BERT in Real-World Fine-Tuning Scenarios? “If you treat every transformer model the same, you’re going to waste compute and time. RoBERTa is one of those models that quietly outperforms when you set it up right.” I’ve fine-tuned both BERT and RoBERTa on a range of real-world tasks — classification, QA, even … Read more

I. Introduction (Short and Practical) “The real value of transfer learning kicks in when you’ve got a solid base model and just the right amount of data to steer it where you want. That’s exactly where ResNet-50 still shines.” If you’re reading this, I’ll assume you already know your way around PyTorch and pretrained models. … Read more

1. Why Fine-Tune Mixtral? “When all you’ve got is a hammer, everything looks like a nail. But Mixtral? It’s more like a toolbox.” I’ve worked with a fair share of open-weight LLMs — LLaMA, Mistral, Falcon, you name it — but when I started experimenting with Mixtral, especially the 8x7B MoE variant, it opened up … Read more

1. Introduction Let me get straight to the point. If you’re working on a real-world text classification task—something beyond toy datasets and clean benchmarks—fine-tuning a pretrained BERT model can give you solid performance out of the box. Personally, I’ve used it across multiple domains—finance, legal, even healthcare—and while it’s not always the fastest, it just … Read more

1. Introduction Fine-tuning BERT for Question Answering isn’t new—but doing it right, especially in production setups or latency-sensitive environments, still takes a bit of finesse. I’ve gone down the rabbit hole of fine-tuning BERT across multiple QA datasets—SQuAD, Natural Questions, even some messy internal corpora—and after all the trial and error, I’ve settled on a … Read more

1. Why Fine-Tune BERT (Even in 2025)? “New doesn’t always mean better—especially when you’re deploying models that actually need to work reliably in production.” I’ve had my fair share of experiments with large language models lately, but here’s the truth: when it comes to sentiment analysis, BERT still holds up surprisingly well in 2025. I’m … Read more

1. Why I Decided to Fine-Tune Florence-2 There’s a saying I’ve always liked: “A model is only as good as its context.” And in my case, that context involved documents — receipts, forms, invoices — where off-the-shelf vision-language models just didn’t cut it. I first tried the usual suspects: CLIP was fast but too shallow … Read more