SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators

Large Language Models (LLMs) have transformed natural language processing, but face significant challenges in widespread deployment due to their high runtime cost. In this paper, we introduce SeedLM, a novel post-training compression method that uses seeds of a pseudo-random generator to encode and compress model weights. Specifically, for each block of weights, we find a …

claudio

Prompting for the best price-performance

In the drive to remain competitive, businesses today are turning to AI to help them minimize cost and maximize efficiency. It’s incumbent on them to find the most suitable AI model—the one that will help them achieve more while spending less. For many businesses, the migration from OpenAI’s model family to Amazon Nova represents not …

Google’s AI Dreamer learns how to self-improve over time by mastering Minecraft

A trio of AI researchers at Google’s Google DeepMind, working with a colleague from the University of Toronto, report that the AI algorithm Dreamer can learn to self-improve by mastering Minecraft in a short amount of time. In their study published in the journal Nature, Danijar Hafner, Jurgis Pasukonis, Timothy Lillicrap and Jimmy Ba programmed …