Model ID
CohereLabs/North-Mini-Code-1.0
Capabilities
North Mini Code 1.0
North Mini Code 1.0 is a 30B parameter decoder-only sparse MoE model with 3B active parameters, designed specifically for agentic software engineering, terminal workflows, and code generation. It features 49 layers, 128 experts with 8 activated per token, and a 3:1 interleaved attention pattern combining 4,096-token sliding-window RoPE attention with global attention. The model uses SwiGLU FFNs, sigmoid-based expert routing, and a 2,048-dimensional hidden size. Post-trained with SFT and RLVR, it supports tool use, 256K context, and 64K output lengths, targeting efficient, reliable coding agents.
About this model
North Mini Code 1.0 is a 30B parameter decoder-only sparse MoE model with 3B active parameters, designed specifically for agentic software engineering, terminal workflows, and code generation. It features 49 layers, 128 experts with 8 activated per token, and a 3:1 interleaved attention pattern combining 4,096-token sliding-window RoPE attention with global attention. The model uses SwiGLU FFNs, sigmoid-based expert routing, and a 2,048-dimensional hidden size. Post-trained with SFT and RLVR, it supports tool use, 256K context, and 64K output lengths, targeting efficient, reliable coding agents.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (CohereLabs/North-Mini-Code-1.0). Released under Apache 2.0. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Requires installing Cohere's companion library via uv pip install "cohere_melody>=0.9.0" to ensure accurate response parsing.
Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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