IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI Systems

IQuest released IQuest-Q1, a sparse Mixture-of-Experts agentic foundation model with 320B total parameters and 15B activated parameters, built for agentic CLI systems. The model is trained in three stages, beginning with pre-training that combines Stable SplitNorm, Muon and muP under a two-stage curriculum. The release details the architecture and training approach for developers building command-line AI agents.

We present IQuest-Q1, a sparse Mixture-of-Experts MoE agentic foundation model with 320B total parameters 15B activated . IQuest-Q1 is developed through a three-stage process: 1 stable and efficient pre-training, combining Stable SplitNorm, Muon, and muP with a two-stage curriculum whose second