Leveraging Wikipedia as a Public RAG Model to Boost LLM Performance

Discover how Retrieval-Augmented Generation (RAG) can significantly improve the performance of large language models, especially smaller ones, by injecting external knowledge while preserving reasoning abilities. This article explores the methodology, datasets, and evaluation results, demonstrating public RAG's potential to bridge the gap between different models and provide access to up-to-date information

Enhancing Large Language Models with Public RAG Models

Exploring Decentralized AI Model Building: Innovations and Challenges

Dive into the second part of our series on Decentralized AI, focusing on model building. Discover how decentralization addresses the challenges of data and compute resource needs, enhances privacy and security, and improves model verification. Learn about cutting-edge projects and technologies shaping the future of AI model development in a decentralized ecosystem.

Toward Decentralized AI, Part 2: Model Building

Exploring the Future of AI: Decentralization in Data Collection and Challenges

Dive into the first part of our insightful series on AI's future with decentralization. Discover how decentralization revolutionizes data collection, addresses challenges in AI pipelines, and enhances quality, privacy, and copyright issues. Explore the series for comprehensive insights into AI/ML's decentralization across various domains, from language processing to robotics.

Toward Decentralized AI, Part 1: Data Collection

Open Source AI Insights 

Discover the exciting world of open source AI software and development tools. Dive into the rise of open source AI, the selection of the right tools, and innovative AI projects. Learn about on-device private LLMs, fine-tuning, and RAG in our comprehensive guide to navigating the AI landscape