Chunking Strategies for LLM Applications

📖 TUTORIALS & CASE STUDIES

How to Build a $300 AI Computer for the GPU-Poor

Read time: 11 minutes

This article demonstrates how software developers can build a powerful $300 AI computer using an AMD-based mini PC. It covers prepping the system with the right drivers and middleware, running large language models like LLaMA 2 on the CPU, and generating images with Stable Diffusion on the integrated AMD GPU. The key is optimizing memory allocation and leveraging open-source tools like llama.cpp to enable generative AI capabilities on a budget-friendly system.

Breaking up is hard to do: Chunking in RAG applications

Read time: 7 minutes

This article explores the importance of "chunking" data when building Retrieval Augmented Generation (RAG) systems. The author discusses various chunking strategies, such as fixed sizes, random chunks, and context-aware chunking, and how they impact the performance of LLM-based applications. The key takeaway is that choosing the right chunking approach is crucial for creating accurate and relevant responses grounded in the source data.

Chunking Strategies for LLM Applications

Read time: 10 minutes

Another article on chunking, this time from Pinecone, explores the importance of "chunking" - breaking down large text into smaller segments - when building applications that leverage Large Language Models (LLMs). It discusses how chunking can optimize the relevance of content retrieved from a vector database, and covers various chunking methods, including fixed-size, sentence-based, and semantic chunking. The article provides guidance on determining the optimal chunk size and method for your specific application needs, such as semantic search, conversational agents, or integrating with LLMs with token limits.

Intro to LLM Agents with Langchain: When RAG is Not Enough

Read time: 15 minutes (+ 1 hour video)

This article provides a detailed introduction to building LLM agents using Langchain, a platform for productionizing LLM applications. It covers key agent architecture components like planning, memory, and tools, demonstrating how to leverage techniques like chain of thought, self-consistency, and custom tools. The author showcases how to construct a complete agent that can solve tasks like searching for information and generating CSV files, highlighting the importance of integrating all architectural elements for effective agent performance.

Retrieval Augmented Generation (RAG) from Scratch

Read time: 20 minutes (Jupyter notebook)

This tutorial walks through building a Retrieval Augmented Generation (RAG) system from scratch using Python and the Hugging Face Transformers library. RAG is a powerful framework for leveraging large language models and knowledge bases to generate high-quality, fact-based responses. The tutorial covers key concepts like vector databases, retrieval, and integration with a language model, providing a hands-on introduction to this cutting-edge AI technique. It does NOT use Langchain or Llamaindex, so you will come out with a deeper understanding the the mechanics of the system vs using the afore mentioned abstraction frameworks.  Read the full tutorial here.

💰Cost of Self-Hosting Llama-3 8B-Instruct

Read Time: 10 minutes

This article explores the cost of self-hosting the Llama-3 8B-Instruct language model, comparing it to the pricing of ChatGPT. It covers the hardware requirements, implementation challenges, and an unconventional approach of self-hosting the hardware to significantly reduce the cost per 1 million tokens, from $17 with a cloud provider to less than $0.01. However, the article notes that the breakeven point for this approach is around 5.5 years, making it more suitable for high-usage scenarios. 

🧰 TOOLS

LSP-AI: An Open-Source Language Server for AI-Powered Programming Assistance

Read time: 6 minutes

LSP-AI is an open-source language server that provides AI-powered functionality, including code completion, to software developers through their favorite editors like VS Code, Neovim, and Emacs. LSP-AI aims to assist and empower engineers by integrating AI tools into their existing workflows, rather than replacing them. Key features include unified AI features, simplified plugin development, broad editor compatibility, and flexible support for various language models.

CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

Read Time: 7 minutes

CAMEL is an open-source library for studying autonomous and communicative agents built on large language models. It introduces a novel "role-playing" framework to facilitate autonomous cooperation among agents and provide insights into their "cognitive" processes. CAMEL enables software developers to generate conversational data for investigating the behaviors and capabilities of chat-based AI agents, aiming to advance research on multi-agent systems and conversational language models.

Parakeet, an Easy Way to Create GenAI Applications with Ollama and Golang

Read Time: 8 minutes

This article introduces Parakeet, a Go library that simplifies the use of the Ollama API for building Generative AI applications. Parakeet provides abstractions for managing conversation memory, giving instructions to models, and adding context, making it easy for Go developers to leverage LLMs like Ollama's TinyDolphin. The article demonstrates Parakeet's "Hello World" completion example and shows how to build a conversational chat with memory and context, highlighting Parakeet's developer-friendly approach to working with Generative AI.

Autoregressive Model Beats Diffusion: 🦙 Llama for Scalable Image Generation

Read time: 6 minutes

LlamaGen is a new family of image generation models that apply the next-token prediction paradigm of large language models to the visual generation domain. It demonstrates that vanilla autoregressive models like Llama can achieve state-of-the-art image generation performance without specialized visual inductive biases. The repository releases pre-trained class-conditional and text-conditional generation models ranging from 100M to 3B parameters, along with online demos and a serving framework for high-throughput inference.

🦀 Blazingly Fast LLM Inference with Mistral.rs

Read Time: 8 minutes

Mistral.rs is a fast and powerful LLM inference platform for software developers, supporting a variety of models, quantization, and easy-to-use APIs in Rust and Python. It offers blazing-fast performance, accelerator support, and advanced features like X-LoRA, dynamic adapter swapping, and speculative decoding. Mistral.rs aims to help developers leverage cutting-edge generative AI capabilities in their applications.

📰 NEWS & EDITORIALS

Announcing ARC Prize: A $1,000,000+ Competition Towards Open AGI Progress

Read time: 9 minutes

ARC Prize is a new competition to solve the ARC-AGI benchmark, a measure of general intelligence that current AI systems struggle with. The competition aims to incentivize new ideas in AI research and open-source solutions to push the boundaries of artificial general intelligence (AGI). It highlights how closed-source progress and a focus on scaling large language models may be hindering true AI advancements.

Nvidia's 'Nemotron-4 340B' Model Redefines Synthetic Data Generation, Rivals GPT-4

Read Time: 8 minutes

Nvidia's new Nemotron-4 340B model is a groundbreaking family of open models that can generate high-quality synthetic data to train large language models. With unmatched performance and a commercially-friendly license, Nemotron-4 340B empowers businesses to create custom AI models tailored to their needs, potentially revolutionizing industries from healthcare to finance. This development showcases Nvidia's leadership in AI and the growing competition in the chip market as the company strives to stay ahead of tech giants.

Technique Improves Reasoning Capabilities of Large Language Models

Read time: 8 minutes

Researchers have developed a new technique called "natural language embedded programs" (NLEPs) that enables large language models like GPT-4 to perform complex reasoning tasks more accurately and transparently. NLEPs prompt the model to generate a step-by-step Python program to solve a query, enhancing its ability to handle numerical, symbolic, and natural language reasoning. This approach offers advantages like improved data privacy and the ability to reuse one program for multiple tasks.

LiveBench: A Challenging, Contamination-Free LLM Benchmark

Read time: 7 minutes

LiveBench is a new benchmark for evaluating Large Language Models (LLMs) that aims to address issues of test set contamination and objective evaluation. LiveBench releases new questions monthly, drawing from recent datasets, papers, news, and more, ensuring LLMs cannot simply memorize the test set. Each question has verifiable ground-truth answers, allowing for accurate, automated scoring. This diverse, challenging benchmark will help software developers building applications with LLMs and Retrieval Augmented Generation frameworks stay up-to-date on the latest capabilities.