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

1. Introduction “The best way to predict the future is to create it.” – Peter Drucker I’ve spent a lot of time fine-tuning large language models, and let me tell you—Falcon 40B is an absolute powerhouse. But like any massive model, getting it to work efficiently for a specific task is where things get tricky. … Read more

1. Introduction “The only way to truly understand a model is to break it, rebuild it, and make it work for you.” I’ve spent a good amount of time working with Mistral 7B, tweaking it, optimizing it, and learning what works—and more importantly, what doesn’t. If you’ve been fine-tuning LLMs before, you already know that … Read more

1. Introduction Why Fine-Tune BERT? “Pretrained models are like all-purpose kitchen knives—they get the job done, but sometimes you need a specialized tool.” BERT is powerful out of the box, but let’s be honest—it’s not perfect for your specific task. Whether it’s sentiment analysis, named entity recognition (NER), or question answering, fine-tuning BERT makes all … Read more

Introduction “If you torture the data long enough, it will confess to anything.” – Ronald Coase I’ve spent a lot of time experimenting with Fine-Tuning and RAG in real-world applications, and let me tell you—most people are using them the wrong way. Some assume fine-tuning is the answer to everything, dumping terabytes of text into … Read more