Introducing North Mini Code: Cohere’s First Model For Developers

Cohere has introduced North Mini Code, a 30B-parameter Mixture-of-Experts model optimized for software engineering tasks, available on Hugging Face under the Apache 2.0 license. This model, the first in a new family, is designed for complex coding workflows and outperforms larger models in agentic coding benchmarks. North Mini Code employs a Mixture-of-Experts Transformer architecture and undergoes a rigorous training process involving supervised fine-tuning and reinforcement learning with verifiable rewards, focusing on agentic coding tasks. The model's training includes a diverse data mixture to enhance robustness across various coding harnesses and environments, improving performance on benchmarks like SWE-Bench and Terminal-Bench. North Mini Code also benefits from asynchronous reinforcement learning to optimize agentic coding rollouts, and it shows notable improvements in robustness and efficiency over its SFT-only counterpart, particularly in code editing tasks. The model is accessible in OpenCode, Cohere API, and on Hugging Face with both BF16 and FP8 weights.