Ornith-1.0-35B-GGUF

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. Ornith-1.0-35B Aloha! 🌺 Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding. Highlights: **State-of-the-Art Coding Agents**: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw. **Self-Improving Training Framework**: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions. **Licence**: MIT licensed, globally accessible, and free from regional limitations. Ornith 1.0 35B This model card documents **Ornith-1.0-35B**, the lightweight member of the Ornith family, designed for efficient single-GPU deployment. Benchmarks Ornith-1.0-35B Qwen3.5-35B Qwen3.6-35B Gemma4-31B Qwen3.5-397B Agentic Coding Terminal-Bench 2.1 (Terminus-2) 64.2 41.4 52.5 42.1 53.5 Terminal-Bench 2.1 (Claude Code) 62.8 38.9 49.2 48.6 SWE-bench Verified 75.6 70 73.4 52 76.4 SWE-bench Pro 50.4 44.6 49.5 35.7 51.6 SWE-bench Multilingual 69.3 60.3 67.2 51.7 69.3 NL2Repo 34.6 20.5 29.4 15.5 36.8 Claw-eval Avg 69.8 65.4 68.7 48.5 70.7 SWE Atlas - QnA 37.1 13.2 15.5 20.4 SWE Atlas - RF 29.7 10.2 11.4 18.4 SWE Atlas - TW 27.8 9.8 13.3 18.5 Terminal-Bench 2.1 (Terminus-2): We evaluate Terminal-Bench 2.1 using the Harbor/Terminus-2 framework with parser=json, temperature=1.0, top_p=1.0, and a 128K context window. Each run uses a 4-hour timeout with 32 CPU cores and 48GB RAM, and results are averaged over 5 runs. We adjust the Qwen chat template to ensure consistency between training and inference (https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B/blob/ma