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What is Supervised Fine-Tuning (SFT)? Supervised fine-tuning is a training strategy where a pre-trained language model is further refined on a carefully curated dataset of prompt-response pairs. The primary goal is to “teach” the model how to generate appropriate, contextually relevant, and human-aligned responses. Key points about SFT include: For many contemporary language models, SFT … Read more
Tokenization is a fundamental yet often misunderstood process in the realm of large language models (LLMs). Despite its crucial role, it is a part of working with LLMs that many find daunting due to its complexity and the numerous challenges it introduces. In this blog post, we will explore the concept of tokenization, its importance … Read more
Introduction This blog post dives into the fascinating world of computer vision, exploring how we can teach machines to “see” using convolutional neural networks (CNNs). This post is based on a lecture from MIT’s 6.S191: Introduction to Deep Learning course. What Does it Mean to “See”? Before diving into the technical details, let’s define “vision”. … Read more
