Extreme quantization reduces the size and computational complexity of LLMs, allowing them to be more accessible and efficient on resource-constrained devices. This technique involves converting floating-precision model weights to lower-precision formats, such as 8-bit integers. Extreme quantization presents several benefits, including reduced model size, increased speed, and the ability to deploy complex models on devices where it was not previously possible.

Extreme Quantization Of LLMs

This paper presents a new learning system that uses generative artificial intelligence (GenAI) to create interactive stories for children. The system is based on two classic narrative frameworks: Freytag's pyramid and Propp's 31 narrative functions. The system uses large language models (LLMs) to generate stories, text-to-speech (TTS) models to convert the stories into audio, text-to-video (TTV) models to create animations, and text-to-music (TTM) models to generate background music.  https://arxiv.org/pdf/2409.11261

Multi - Agent Generative AI For Dynamic Multimodal Stories

This podcast explores the possibility that Earth may have had a ring during the Ordovician period. The authors analyzed the distribution of Ordovician meteorite impact craters and found that they were unusually concentrated near the equator. Calculating the probability that this distribution was a product of chance, they found that it was extremely unlikely, leading them to propose that a large L-type asteroid broke up near Earth, forming a ring of debris. https://www.sciencedirect.com/science/article/pii/S0012821X24004230?via%3Dihub

Earth and its ring during the Ordovician period

This podcast discusses the use of the "chain of thought" (CoT) technique in large language models (LLMs) to improve their performance on reasoning tasks

Multi - Agent Generative AI For Dynamic Multimodal Stories

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