In this article, I’ve delved into the critical factors that influence the storage size of generative AI models. I began by defining model storage size and then explored how the number and precision of parameters, model architecture, and compression techniques
Category: Gen AI For All
Resources designed for every stage of the Generative AI journey, from articles and posts to detailed documents. Each is crafted in a clear and accessible format, making them easy to understand for both beginners and experienced professionals alike.
Recently Andrew Ng in his post shared about the falling cost of ChatGPT tokens, The cost has plummeted by approximately 80%, from $36 per million tokens in March 2023 to a mere $4 per million tokens today. Such a significant
Generative AI-powered code autocompletion tools offer significant benefits to developers, including increased productivity, reduced errors, and faster learning. However, it’s crucial to be mindful of the potential challenges, such as over-reliance on AI, code quality issues, and security risks. By
Generative Adversarial Networks (GANs) are quite important in the field of Generative AI. They have revolutionized how we create and understand synthetic data. GANs have opened up new possibilities in AI, making them a crucial component of generative modeling. Today
In this document I will share an overview of agents in generative AI. The document offers a comprehensive introduction to the concept and functionality of agents in the field of generative AI. This will cover: • Definition: Explains what agents in
Meta has recently released the largest version of its mostly free Llama 3 artificial intelligence model, boasting impressive multilingual capabilities and superior general performance. With 405 billion parameters, this new version is significantly larger than its predecessor released last year.
With a free Gemini Basic version available and a paid Gemini Advanced model, it’s always interesting to wonder whether it’s worth getting access to the Advanced model. Today in this document, I will share my detailed analysis and comparison between
Quantum computing in the Generative AI space is in its early stages, marked by significant research and experimental developments. While practical applications are limited by current technological constraints, ongoing advancements and increasing investment suggest a promising future where quantum computing
Research papers often present a daunting level of complexity or an overwhelming amount of detail that can be challenging to understand and comprehend. Generative AI can make this process of understanding and decoding the document very easy. The document discusses
Today’s post shares a summary of key performance indicators (KPIs) for validating large language models (LLMs). It offers concise explanations and formulas for each KPI, along with example values to illustrate what constitutes “good” and “poor” model performance. The KPIs