Introducing Meta Llama 3

📖 TUTORIALS & CASE STUDIES

Attention in transformers, visually explained

watch time: 26 minutes

This video is part of a series on deep learning by 3Blue1Brown. In this chapter, the video dives into the concept of attention in transformers, a key mechanism in large language models.

Building High-Accuracy Serverless RAG with Amazon Bedrock and Kendra

read time: 20 minutes

This blog post provides a comprehensive guide on building a high-accuracy, serverless Retrieval-Augmented Generation (RAG) application using Amazon Bedrock, Amazon Kendra, and AWS Lambda. The post also demonstrates how to augment the knowledge of a Large Language Model (LLM) with proprietary data, and deploy the solution with a serverless architecture, ultimately saving efforts and reducing costs.

Avoiding Common Traps in RAG Vector Databases

read time: 20 minutes

This article discusses common pitfalls in using Retrieval Augmented Generation (RAG) and vector databases for generative AI applications. It provides insights into the RAG model architecture, the importance of metadata, and the need for regular refreshing of document vectors. The article also highlights five common traps developers fall into and how to avoid them.

Mastering Advanced Retrieval Techniques for RAGs

read time: 15 minutes
This article provides an in-depth exploration of advanced retrieval techniques to optimize the selection of relevant documents for Retrieval-Augmented Generation (RAG) models. It covers methods like Naive Retriever, Parent Document Retriever, Self-Query Retriever, and Contextual Compression Retriever, offering insights on how to improve the efficiency of your RAGs.

Master Generative AI with freeCodeCamp's Comprehensive Course

read time: 8 minutes
freeCodeCamp.org has launched a comprehensive Generative AI course on YouTube. Led by AI experts, the course covers OpenAI API, LangChain, Vector Databases, Meta Llama 2, and real-world projects. It blends theoretical knowledge with practical projects, making it suitable for various learning levels.

🧰 TOOLS

Instructor: A Python Library to Streamline LLM Workflows

read time: 10 minutes
Instructor is a Python library that simplifies working with structured outputs from large language models (LLMs). It offers features like response models, retry management, validation, and streaming support. It also integrates with various LLM providers. Check out Instructor to supercharge your LLM workflows.

LLM for Unity: Integrating Large Language Models into Unity Engine

read time: 10 minutes

LLM for Unity is a tool that allows seamless integration of Large Language Models (LLMs) into the Unity engine, enabling the creation of intelligent, interactive characters. It supports major LLM models, runs locally, and is free for both personal and commercial use. Learn more about it here.

Gateway: A Unified API for 100+ AI Models

read time: 10 minutes
Gateway is a production-ready tool that streamlines requests to over 100 open and closed source models with a unified API. It offers features like load balancing, automatic retries, and configurable request timeouts. It's compatible with the OpenAI API & SDK and is used by companies like Postman and Turing. Learn more about it here.

Jina AI's Reader: A New Tool for LLM-Friendly Input Conversion

read time: 5 minutes

Jina AI has introduced Reader, a tool that converts any URL into an LLM-friendly input (Markdown). It's free, stable, scalable, and actively maintained. It now supports image reading, providing captions for images at the specified URL. The tool also offers a streaming mode for more complete results and efficient data handling. It's a core product of Jina AI, demonstrating their commitment to improving AI tools.

📰 NEWS & EDITORIALS

Introducing Meta Llama 3: The Next Generation of Open Source Language Models

read time: 15 minutes

Meta has released the first two models of the next generation of Llama, Meta Llama 3, featuring pretrained language models with 8B and 70B parameters. These models demonstrate state-of-the-art performance on industry benchmarks and offer improved reasoning capabilities. The company aims to make Llama 3 multilingual and multimodal, and continue to improve its performance. The models are available for the community to use and provide feedback. Read more about it here.

Understanding AI Risks: Follow the Money

read time: 15 minutes
This article discusses the economic risks of AI, focusing on the misalignment between a company's profit-driven incentives and societal interests. It highlights the need for open, accountable AI algorithms that distribute value equitably, and the role of disclosures and open technological standards in ensuring the benefits of AI are widely shared.

2024 AI Index Report: Key Insights and Trends

read time: 30 minutes
The 2024 AI Index Report provides a comprehensive overview of AI trends, including technical advancements, public perceptions, and geopolitical dynamics. It highlights the rise in AI training costs, the surge in generative AI funding, and the increasing influence of AI on various sectors. The report also emphasizes the need for standardization in responsible AI reporting and the growing concern over AI's societal impacts.

Unraveling the Power of Chain-of-Thought Reasoning in Neural Networks

read time: 15 minutes
Researchers are exploring the power of chain-of-thought reasoning in large language models like ChatGPT. A technique called chain-of-thought prompting has enabled these models to solve complex problems. Theoretical studies are being used to understand the intrinsic capabilities and limitations of these models. This article provides an in-depth look at this research and its implications for the future of AI.