Llama

The AI industry is seeing a surge in 'model routers' designed to intelligently distribute LLM queries to the most cost-effective and capable models. Companies including Cursor, Ramp, and Meta are developing these systems to optimize performance and reduce the high costs associated with frontier-grade models.

This paper presents a method for converting pretrained transformers into linear-time architectures by focusing on the efficiency of state update designs. By analyzing softmax attention through a first-order approximation, the authors prove that delta-style updates are superior for post hoc linearization.

This paper presents a method for converting pretrained transformers into linear-time architectures by focusing on the efficiency of state update designs. By analyzing softmax attention through a first-order approximation, the authors prove that delta-style updates are superior for post hoc linearization.

A critical vulnerability in Ollama allows unauthenticated attackers to trigger an out-of-bounds heap read via malicious GGUF files. This exploit can expose sensitive information like user messages and system prompts by leaking them into newly created model files.