Generative AI

  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...

 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...