Many teams need fast, reliable decisions from AI without the risk of uncontrolled text generation. Traditional large language models can produce unrelated content, making them unsuitable for simple choice, yes/no, or scoring tasks. Deploying such models often requires cloud services, expensive GPUs, and complex authentication, which adds latency and cost for internal tools.
Strands Decider 2B solves these problems. It is a 1.9 billion‑parameter decision model derived from Qwen3.5‑2B‑Base with the language‑modeling head removed and replaced by a tiny pointer head. The model reads a state description and a set of options, then returns a single choice, a yes/no probability, or a score on an ordered scale together with a calibrated confidence value. Because there is no decoding loop, a single forward pass yields the answer in sub‑second latency on a CPU, consumer GPU, or Apple silicon Mac.
Deployment is straightforward for local or self‑hosted use. The weights are available on Hugging Face under an Apache‑2.0 license. Installing the package via pip provides both a command‑line interface and a lightweight HTTP server. The bundled server binds to 127.0.0.1 without authentication, so adding your own auth layer is required for production exposure. No external inference provider currently hosts the model, keeping data on‑premise.
The model supports three question types: choice (pick one of N options), noul (yes/no probability), and score (level on an ordered rubric). Each answer includes a confidence score; the project recommends treating results with confidence ≥ 0.9 as automatic actions and lower confidence as cues for human review or escalation to a larger LLM. Multiple questions about the same state are cheap because the state is encoded once and each extra question adds only its own tokens.
Practical steps: install the package, start the server, protect it with your preferred authentication, send JSON requests specifying the state, question type, and options, read the returned choice, probability, or score plus confidence, and apply the confidence gate to decide whether to act automatically or involve a person.
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