Introduction I’ve written a lot in recent months about what I call Large Action Models, or LAMs—a more active, autonomous variation on LLMs that don’t merely generate content like text or images but accomplish entire tasks and even participate in workflows, whether alongside people or on their

Introduction Open source has become one of the hottest topics in AI, and the fanfare is well-deserved. The open source community is keeping a nimble pace with the state of the art, delivering ever-growing and ever-more-capable models that often compete impressively with their commercial counterparts. It’s an exciting thing

TLDR We trained a series of 7B LLMs named XGen-7B with standard dense attention on up to 8K sequence length for up to 1.5T tokens. We also fine tune the models on public-domain instructional data. The main take-aways are: * On standard NLP benchmarks, XGen achieves comparable or better results

LAMs: From Large Language Models to Large Action Models There’s no question that we’re living in the era of generative AI, and its impact is only growing. More and more, AI is helping us write emails, create imagery, consume information, and even code. But as empowering as it

Imagine you find yourself in the cockpit of a next-generation spacecraft—the kind that can get you from low Earth orbit to the Kuiper belt without breaking a sweat. How do you envision controlling it? Science fiction has conditioned us to equate futuristic technology with dazzling complexity, so you might