Knowledgator Releases GLiFormer: 575M Encoder Achieving 91.1 F1 on Nested JSON Extraction Without Token Generation

Knowledgator has open-sourced GLiFormer, a 575-million-parameter encoder model that achieves 91.10 F1 on nested JSON structured extraction tasks without using autoregressive token generation. Instead of generating output token-by-token, GLiFormer uses a discriminative encoder approach, making it dramatically faster and more deterministic for structured data extraction use cases. This is directly relevant to developers building data pipelines, document parsing systems, or any workflow that requires reliable extraction of structured output from unstructured text. The token-free design also means lower latency and better throughput compared to decoder-based approaches for this class of task. At 575M parameters, it is compact enough to run efficiently on standard inference hardware.