Strands Decider 2B: a small, open-source, decision model

Strands Decider 2B is a new small decision model designed for fast experimentation and innovation in agentic AI. Decision models, like Strands Decider 2B, differ from standard language models (LLMs) in that they are designed to choose between a set of options and assign numerical scores to them. This allows them to operate quickly and reliably, even on smaller hardware.

Unlike LLMs that can generate arbitrary output, decision models always produce an answer from a set of predefined options, making them better suited for tasks such as answering yes/no questions or identifying language. Additionally, decision models provide high-quality reliability scores for each decision, enabling developers to evaluate how certain they can be about the correctness of their selections.

Strands Decider 2B is a 2 billion parameter model optimized for local development and experimentation. It can run on a CPU or GPU and return answers to meaningful questions in milliseconds. The model was created by taking a pre-trained LLM torso (Qwen3.5-2B) and replacing its language generation head with a "pointer head" that scores decisions for each option. The torso is then fine-tuned with a rank-16 LoRA adapter.

The authors of Strands Decider 2B believe this class of decision models will drive innovation in agentic AI, leading to advancements in model routing, tool selection, evaluations, guardrails, memory management, context handling, and policy classification. They have released Strands Decider 2B as open source on GitHub and Hugging Face, allowing developers to build upon the model and contribute their own ideas.