Llm

Introduction Hello everyone! By now, you’ve likely heard of ChatGPT, the revolutionary AI system that has taken the world and the AI community by storm. This remarkable technology allows you to interact with an AI through text-based tasks. The Technology Behind ChatGPT: Transformers The neural network that powers ChatGPT is based on the Transformer architecture, introduced in the 2017 paper “Attention is All You Need.” GPT stands for “Generatively Pre-trained Transformer.” The Transformer architecture is a landmark development in AI that revolutionized the field, primarily in natural language processing (NLP). The Transformer architecture, initially designed for machine translation, became the backbone for numerous AI applications, including ChatGPT. ...

The Busy Person’s Guide to Large Language Models: From Inner Workings to Future Possibilities (and Security Concerns) This post explores the fascinating world of large language models (LLMs) like ChatGPT and llama2, diving into their inner workings, potential future developments, and even the security challenges they present. It’s a summary of a talk by Andrej Karpathy, offering a comprehensive overview for anyone curious about this rapidly evolving technology. What are LLMs and How Do They Work? Imagine a massive file containing compressed knowledge from the internet – that’s essentially what an LLM is. It’s a complex neural network trained on vast amounts of text data, enabling it to predict and generate human-like text. The process involves two key stages: ...

Introduction Large Language Models (LLMs) are the powerhouses behind cutting-edge AI applications like chatbots and text generation tools. These complex models have traditionally relied on high-performance GPUs to handle the massive amounts of computation involved. But what if that wasn’t necessary? Recent breakthroughs, like the BitNet B1.58 model, hint at a future where LLMs can thrive without the need for expensive, power-hungry GPUs. The Problem with Floating-Point Precision Most LLMs today rely on floating-point numbers (e.g., 32-bit or 16-bit) to represent the complex data they process. While powerful, these representations require significant computational resources, which is where those powerful GPUs come in. But what if we could change the rules of the game? ...

Introduction Complex tasks, such as writing unit tests, can benefit from multi-step prompts. In contrast to a single prompt, a multi-step prompt generates text from GPT and then feeds that output text back into subsequent prompts. This can help in cases where you want GPT to reason things out before answering, or brainstorm a plan before executing it. Multi-Step Prompting Technique We will use a 3-step prompt to write unit tests in Java ...