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…
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that bridges retrieval systems and large language models (LLMs). However, while the concept is elegant, its practical implementation faces structural challenges. In this blog, we’ll explore RAG’s architecture, identify its core issues, and…
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 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…
In this blog, we will discuss the often-overlooked yet essential role of benchmarking in large language model (LLM) development. A benchmark, or evaluation set, serves as a pre-defined standard to assess an LLM’s performance and is a critical first step…
In this blog, we will dive deep into the true costs associated with fine-tuning large language models (LLMs) and explain why GPU resources might not be the most crucial factor. From GPU expenses and labor costs to inference and data…
