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RT @ornith_: Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding. Ornith-1.0 spans the full parameter s…
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RT @ornith_: Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding. Ornith-1.0 spans the full parameter s…
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"text": "Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding.\n\nOrnith-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:\n✅Terminal-Bench 2.1(77.5)\n✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual)\n✅NL2Repo(48.2)\n✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW)\n✅ClawEval(77.1)\n\nPost-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.😎\n\nAll models are released under the MIT license, enabling full commercial and research use.\n\n📖Tech Blog: https://t.co/qT9N2HYWFn\n🤗Huggingface: https://t.co/PRrwqjeBtM",
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