The self-attention mechanism lies at the core of the transformer architecture, a breakthrough innovation responsible for the remarkable success of modern large language models. In fact, understanding self-attention is key to grasping 80% of what makes transformers so effective. What…
Retrieval-Augmented Generation (RAG) workflows have gained significant traction as a means to enhance the capabilities of large language models. However, integrating knowledge graphs into this workflow can address some of its inherent limitations. Let’s explore why this integration is beneficial…
In this post, we’ll explore what a knowledge graph is and how it relates to large language models (LLMs). While the concept of knowledge graphs gained popularity around 2014 or 2015 and then plateaued, it has resurfaced strongly in 2023…
Meta has launched the Llama 3.1 series, including the groundbreaking 405B parameter model, the largest and most efficient open-source model to date. In this blog, I summarize the key features, insights, and advancements this release brings to the AI landscape.…
How much GPU memory do you need to deploy a large language model like Llama 70B for real-world applications? In this video, I’ll break down a simple method to estimate GPU memory requirements based on your use case. Key Points…
How much GPU memory do you need to fine-tune a model? In this blog, we break down the memory requirements for full fine-tuning and parameter-efficient methods like LoRA and QLoRA, helping you plan your AI projects effectively. We’ll cover: Memory…
In this blog, we dive deeper into the practical application of RAG (Retrieval-Augmented Generation) and fine-tuning by exploring real-world scenarios. If you’re deciding between these approaches for your AI solutions, this breakdown will clarify which to use based on specific…
When building applications with large language models, the choice between RAG (Retrieval-Augmented Generation) and fine-tuning depends on the use case. Here’s a quick breakdown: RAG: Adds external knowledge to the base model without altering it, making it efficient for dynamic…
In this blog, we explain how Temperature influence large language models by controlling token sampling probabilities, balancing randomness, and improving output consistency. For detailed information, please watch our YouTube video: What is Temperature in LLM: Simply Explained When working with…
In this blog, we explain how top-k, top-p influence large language models by controlling token sampling probabilities, balancing randomness, and improving output consistency. For detailed information, please watch the YouTube video: What are Top-K & Top-P in LLM?: Simply Explained…
