AI

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? ...

What are vector databases? A Vector Database is a type of database that stores information in a structured way using vectors. Now, what are vectors? Think of them as mathematical representations of data that capture its meaning and context. Let’s say you have a photo of a cat. Instead of just storing the image file, a Vector Database will convert this photo into a vector, which is essentially a set of numbers that represent various features of the cat, like its color, shape, and size. This vector will contain information about the cat in a way that a computer can understand. ...

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 ...

Introduction Embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships between those concepts. Whether it’s natural language processing, computer vision, recommender systems, or other applications, embeddings play a crucial role in enhancing model performance and scalability. Text embeddings measure the relatedness of text strings. Embeddings are commonly used for: Search (where results are ranked by relevance to a query string) Clustering (where text strings are grouped by similarity) Recommendations (where items with related text strings are recommended) Anomaly detection (where outliers with little relatedness are identified) Diversity measurement (where similarity distributions are analyzed) Classification (where text strings are classified by their most similar label) Embedding vector from a string ...