StartLux-Decision provides open models in 4B, 9B, and 27B parameter sizes that output typed probabilities for structured questions rather than open text. The repository includes an HTTP server and inference engine supporting GGUF and MLX formats, with variants that conditionally generate reasoning traces when confidence falls below 0.6. It handles text, JSON, and image inputs in a single forward pass, making it suitable for programmatic control tasks like game bots and video frame analysis.
If you need structured classification or control loops without parsing free-form LLM output, it is worth testing locally via the provided MLX or GGUF weights. However, the specialized API and decision-focused architecture mean it will not replace a general-purpose chat model or handle open-ended text generation. Skip it unless you are actively building deterministic pipelines that require explicit probability scores for discrete options.
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