Posts

Image
  Understanding AI: The LM Glossary Artificial Intelligence (AI) is a rapidly evolving field, and keeping up with its terminology can be challenging. To simplify the learning process, we've categorized the most frequently used AI terms into five main territories, each represented by a different color. This structured approach ensures a logical flow of information, helping both beginners and experts navigate AI concepts effectively. Blue Zone: AI Model Types, Architectures, and Sizes The blue zone covers the fundamental aspects of AI models, including their types, architectures, and sizes. Terms in this category include: Large Language Model (LLM) – AI models designed to understand and generate human-like text. Transformer – A deep learning architecture that powers most modern AI models. Parameters – The numerical values that determine a model’s behavior and learning capabilities. Fine-Tuning – The process of adjusting a pre-trained model to improve performance on a spec...
Post a Comment
Read more

Agents vs. Models: Understanding the Key Differences in AI

As artificial intelligence continues to advance, the terms models and agents are often used interchangeably. However, they serve distinct roles in the AI ecosystem. Understanding the difference between AI models and AI agents is crucial for anyone looking to harness the full potential of generative AI. What is an AI Model? An AI model is a trained system that makes predictions or generates responses based on input data. These models rely solely on the information available in their training datasets. Whether it’s a large language model (LLM) like GPT or a fine-tuned transformer, AI models execute a single inference at a time without retaining memory of past interactions unless specifically designed to do so. Key Characteristics of AI Models: 🔹 Limited Knowledge Scope : The model's understanding is restricted to the data it was trained on. 🔹 Single-Step Predictions : AI models make isolated predictions per query. 🔹 No Built-In Tool Access : Models do not inherently interact wi...
2 comments
Read more

What are LoRA and QLoRA?

Image
  What are LoRA and QLoRA? In the rapidly evolving field of Natural Language Processing (NLP), fine-tuning Large Language Models (LLMs) often poses challenges due to high memory consumption and computational demands. Two groundbreaking techniques, LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA), have emerged as solutions to optimize fine-tuning by reducing memory usage and enhancing efficiency without compromising performance. Here's an overview of these transformative methods: LoRA: Low-Rank Adaptation LoRA is a parameter-efficient fine-tuning method designed to modify a model's behavior by introducing new trainable parameters without increasing its overall size. This approach keeps the original parameter count intact, significantly reducing the memory overhead typically associated with training large models. How It Works: LoRA integrates low-rank matrix adaptations into the model's existing layers. These adaptations fine-tune the model to specific tasks while req...
Post a Comment
Read more

Understanding Tokenization: The Cornerstone of Large Language Models (LLMs)

Image
 In the world of Large Language Models (LLMs), tokenization is a fundamental yet fascinating process. But what exactly is tokenization, and why is it so important for LLMs to function effectively? What is Tokenization? Tokenization is the process of breaking text into smaller, manageable units called tokens. These tokens can represent words, subwords, or even individual characters. For example, the word "tokenization" might be split into smaller subwords such as "token" and "ization." This step transforms raw text into a structured format that LLMs can process. Since LLMs cannot directly comprehend raw text, tokenization acts as a bridge, converting human-readable text into sequences of numbers that the model understands. Why is Tokenization Important in LLMs? 1. Facilitating Text Understanding Tokenization ensures that a language model can interpret text input by mapping tokens to numerical representations. This allows the model to "read" and pr...
Post a Comment
Read more