Strands Decider 2B: a small, open-source, decision model
4 hours ago
- Strands Labs announces Strands Decider 2B, a 2 billion parameter decision model optimized for fast experimentation and local development.
- Decision models select from predefined options and assign confidence scores, unlike LLMs that generate arbitrary text; they are faster and more reliable but less flexible.
- The model achieves competitive accuracy and calibration on JevBench (3rd of 33 in 2B class), with median latency ~115ms on RTX 3090 and ~153ms on M3 MacBook.
- It is open-sourced on GitHub with weights on Hugging Face, including training data and scripts, encouraging community innovation.
- Use cases include model routing, tool selection, evaluations, guardrails, memory, context management, policy classification, and hybrid agents with LLMs.
- A CLI and Strands agent integration example demonstrate easy experimentation, including an intervention system for decision-making before tool calls.