Retrieval-Augmented Generation (RAG) is the concept of providing large language models (LLMs) with additional information from an external knowledge source. This allows them to generate more accurate and contextual answers while reducing hallucinations.

Large language models require huge amounts of GPU memory. Is it possible to run inference on a single GPU? If so, what is the minimum GPU memory required?

The 70B large language model has parameter size of 130GB. Just loading the model into the GPU requires 2 A100 GPUs with 100GB memory each.

In the rapidly evolving landscape of technology, Generative AI stands as a revolutionary force, transforming how developers & AI/ML engineers approach complex problems and innovate. This article delves into the world of Generative AI, uncovering frameworks and tools that are essential for every developer.

The good, bad, and unexpected of Gemini Ultra.

Seamlessy implement information extraction pipeline with LangChain and Neo4j

Extracting structured information from unstructured data like text has been around for some time and is nothing new. However, LLMs brought a significant shift to the field of information extraction. If before you needed a team of machine learning experts to curate datasets and train custom models, you only need access to an LLM nowadays.