I’m sitting here with a hot cup of coffee, about halfway through Google’s new paper on "Nested Learning," and I have to be honest: I need to get this out of my head and onto the screen. Usually, when a big lab drops a paper, I skim the abstract, nod ...
Fine-tuning large language models (LLMs) can be resource-intensive, requiring immense computational power. LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation) offer efficient alternatives for training these models while using fewer r...
Function calling has become an essential feature for working with large language models (LLMs), allowing developers to extend the capabilities of LLMs by integrating external tools and services. Instead of being confined to generic answers, function ...
In the ever-evolving domain of Natural Language Processing (NLP), supervised fine-tuning has emerged as a game-changing technique for adapting pre-trained Large Language Models (LLMs) to specific tasks. While pre-trained LLMs like those in the GPT fa...
