GLiNER2 is a unified 205M parameter model that combines four AI tasks—Named Entity Recognition, Text Classification, Structured Data Extraction, and Relation Extraction—into a single efficient system. Developed by fastino-ai, it processes text locally without requiring GPUs or external APIs, making it ideal for privacy-sensitive applications. The model handles entities, classification, structured data, and relations in one forward pass.

What sets GLiNER2 apart is its CPU-first design, enabling lightning-fast inference on standard hardware. Users can extract entities like companies, people, products, and locations with simple Python code, receiving results with confidence scores and character positions. The system supports enhanced accuracy through field descriptions and offers both base (205M) and large (340M) model variants available on Hugging Face.

The framework includes comprehensive training guides, LoRA adapters for parameter-efficient fine-tuning, and multi-task extraction capabilities. From medical data parsing to product information extraction, GLiNER2 processes complex JSON structures and supports regex validation. Its unified schema approach eliminates the need for multiple specialized models, reducing complexity while maintaining high accuracy across diverse text processing tasks.