Ornith on X: "Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding.

Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on https://t.co/7g1rmacLps" / X<br>Post

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Ornith

@ornith_

Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding.

Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks including:<br>✅Terminal-Bench 2.1(77.5)<br>✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual)<br>✅NL2Repo(48.2)<br>✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW)<br>✅ClawEval(77.1)

Post-trained on top of gemma4 and qwen3.5, Ornith-1.0 employs a novel self-improving training strategy in which reinforcement learning is used to generate not only solution rollouts, but also the task-specific scaffolds that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model generate higher-quality solutions in agentic coding.😎

All models are released under the MIT license, enabling full commercial and research use.

📖Tech Blog: deep-reinforce.com/ornith_1_0.html<br>🤗Huggingface: huggingface.co/collections/de…

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