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Ornith-1 is an open-source family of agentic coding models from DeepReinforce AI. It is designed for coding agents that need to solve software engineering tasks through iterative tool use and solution rollouts. The project presents 9B dense, 31B dense, 35B mixture-of-experts, and 397B mixture-of-experts variants. These models are post-trained on top of Gemma 4 and Qwen 3.5 foundations. Its training approach uses reinforcement learning to optimize both the solution and the scaffold that guides the solution process. The repository emphasizes benchmark performance on Terminal-Bench, SWE-bench, NL2Repo, OpenClaw, and SWE Atlas while keeping the project MIT licensed and globally accessible.
Features
- Agentic coding model family
- 9B, 31B, 35B, and 397B variants
- Dense and mixture-of-experts architectures
- Reinforcement learning scaffold optimization
- Terminal-Bench and SWE-bench focus
- MIT-licensed open-source release
Project Samples
Categories
AI Coding AgentsLicense
MIT LicenseFollow Ornith-1.0
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