GLiNER2 is a unified model for Named Entity Recognition, Text Classification, Structured Data Extraction, and Relation Extraction, consisting of 205 million parameters. It allows for efficient CPU-based inference without the need for complex pipelines or external API dependencies.

Large Language Models (LLMs) exhibit the ability to adopt different personas and behaviors based on social context. This flexibility is achieved through the presence of personality subnetworks within the models, rather than relying solely on external knowledge or prompting methods.

CAD Search allows users to find 3D models using natural language queries. The platform is powered by the ABC Dataset.