Perfectly Awesome

We’re introducing Llama 4 Scout and Llama 4 Maverick, the first open-weight natively multimodal models with unprecedented context support and our first built using a mixture-of-experts (MoE) architecture.

Psst, kid, want some cheap and small LLMs?

Meta's recent release of Llama 3.1 has stirred excitement in the AI community, offering an array of remarkable applications. This groundbreaking model, particularly the 405B variant, stands out for its superior performance and open-source accessibility, outpacing even top-tier closed models. Here are ten wild examples showcasing the versatile use cases of Llama 3.1, from enhancing personal gadgets to innovative AI deployments. Efficient Task Automation: Llama 3.1 405B can be harnessed to teach the smaller 8B model how to execute tasks perfectly, reducing costs and latency. This setup allows users to train the 8B model to handle various operations, providing a

Llama 3.1 is the latest version of Meta's large language models, with a new model weight, 405 billion parameters, the biggest model it's trained.

The newly unveiled Llama 3.1 collection of 8B, 70B, and 405B large language models (LLMs) is narrowing the gap between proprietary and open-source models. Their open nature is attracting more…

Meta announced the release of Llama 3.1, the most capable model in the LLama Series. This latest iteration of the Llama series, particularly the 405B model, represents a substantial advancement in open-source AI capabilities, positioning Meta at the forefront of AI innovation.  Meta has long advocated for open-source AI, a stance underscored by Mark Zuckerberg’s assertion that open-source benefits developers, Meta, and society. Llama 3.1 embodies this philosophy by offering state-of-the-art capabilities in an openly accessible model. The release aims to democratize AI, making cutting-edge technology available to various users and applications. The Llama 3.1 405B model stands out for

Meta llama 3.1 405b kicks off a fresh chapter for open-source language models. This breakthrough brings unmatched skills to AI

llama3 implementation one matrix multiplication at a time - naklecha/llama3-from-scratch

The models have some pretty good general knowledge.

I wrote 84 new matmul kernels to improve llamafile CPU performance.

I want to provide some tips from my experience implementing a paper. I'm going to cover my tips so far from implementing a dramatically scaled-down versio...

A multifaceted challenge has arisen in the expansive realm of natural language processing: the ability to adeptly comprehend and respond to intricate and lengthy instructions. As communication nuances become more complicated, the shortcomings of prevailing models in dealing with extensive contextual intricacies have been laid bare. Within these pages, an extraordinary solution crafted by the dedicated minds at Together AI comes to light—a solution that holds the promise of reshaping the very fabric of language processing. This innovation has profound implications, especially in tasks requiring an acute grasp of extended contextual nuances. Contemporary natural language processing techniques rely heavily on

Sundays, The Sequence Scope brings a summary of the most important research papers, technology releases and VC funding deals in the artificial intelligence space.

Dead simple way to run LLaMA on your computer