Introducing Lumma-Fev-27B

FrontiersMind has released Lumma-Fev-27B, a 27-billion-parameter decision model based on Qwen3.8-27B that evaluates supplied options in a single forward pass rather than generating free-form labels. Trained across all weights on roughly 100,000 samples in English, Hindi, Tamil, and Kannada, the Apache 2.0–licensed model supports yes/no “noul” gates and multiple-choice questions through the same /v1/systemone interface used by TypeSafe Jev, while score outputs remain untested. On 4,454 held-out decisions from nine task types, the company reports 90.7% accuracy and a 0.009 calibration error for Lumma-Fev-27B, compared with 83.9% and 0.045 for Jev, although Jev remains stronger on reasoning benchmarks including BBH and WinoGrande. The model was fine-tuned in a single pass using four H200 GPUs for about 13 GPU-hours, requires approximately 49 GiB of bfloat16 storage and an 80 GB GPU for local deployment, and can be accessed through Python or an HTTP server. FrontiersMind plans to expand its training data with broader reasoning and knowledge-oriented tasks and invites user feedback on failures in practical applications.

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