1. Introduction: Why Fine-Tune Qwen2-VL-7B? “You don’t always need a bigger hammer—sometimes you just need a better grip.”That’s how I’d describe working with vision-language models like Qwen2-VL-7B. In my case, I needed a model that could understand what’s in an image and generate meaningful, context-aware responses. Out-of-the-box, Qwen2-VL-7B is pretty solid—but if you’re working with … Read more

1. Intro: Why LayoutLMv3 and Why Fine-Tuning is Still Hard “Just throw your documents at a transformer and call it a day.”Whoever believes that, clearly hasn’t wrestled with LayoutLMv3 in production. In my experience, document understanding is one of those tasks that looks clean in papers but feels chaotic in the real world. OCR outputs … Read more

1. Why I Still Use BERT (Even in 2025) “Old tools, when wielded well, still cut deep.” You’d think by 2025, BERT would be dead weight. I mean, with DeBERTa, RoBERTa, and a dozen instruction-tuned sentence encoders floating around, why would anyone still reach for a 2018 model? Here’s the deal: BERT still gets the … Read more

1. Introduction “In theory, there’s no difference between theory and practice. In practice, there is.” — Yogi Berra I’ve fine-tuned Wav2Vec2 for a few different use cases, but the one that stands out is domain-specific transcription in noisy environments — think phone call audio with overlapping speech and lots of background noise. Whisper didn’t cut … Read more

1. Why I Had to Fine-Tune SAM 2 “A generalist can see everything, but rarely with precision.” That’s what I realized after throwing SAM 2 at some industrial inspection data I had on hand. On paper, it’s brilliant — segment anything, anywhere, zero-shot. But in reality? The masks it produced on high-res images of metallic … Read more

1. Why Fine-Tune Donut in the First Place? “All models are wrong, but some are useful.” — George Box probably wasn’t thinking about Donut when he said this, but the idea still holds. I’ve used the naver-clova-ix/donut-base model across multiple real-world projects, and while it’s impressive out-of-the-box, it doesn’t generalize well to custom document layouts. … Read more

1. Why I Chose Flux.1-dev + LoRA for Fine-Tuning “If it ain’t broke, break it—and make it better.” That’s how I approach models that almost do what I need. When I started experimenting with Flux.1-dev, it wasn’t because it was trending. It was because the architecture had just enough quirks to be useful for personal … Read more

1. Introduction: Why Fine-Tune DinoV2 At All? I’ll be honest—fine-tuning DinoV2 isn’t something I reach for every day. But when I’m dealing with data that’s far from ImageNet—think industrial defect images, medical scans, or satellite captures—it starts to make a lot of sense. DinoV2 already gives you strong representations, but out-of-the-box features don’t always cut … Read more

1. Why I Chose MPT-7B for Fine-Tuning “You don’t pick a model like MPT-7B on a whim — it’s a decision that comes from repeatedly hitting the wall with others.” When I first started working on a domain-specific assistant for technical documentation and long-form structured Q&A, most open models like LLaMA 2, Falcon, and OpenLLaMA … Read more

1. Why I Started Fine-Tuning GGUF Models “If you’re not running your own models locally by now, you’re probably giving away more than just latency.” When I first started experimenting with local LLMs, my priority was clear: full control. I didn’t want to rely on cloud APIs, and I certainly didn’t want inference latency bottlenecks … Read more