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Ornith-1.0-35B is an efficient 35-billion parameter open-source reasoning model optimized for single-GPU deployment and trained via reinforcement learning to achieve state-of-the-art performance in agentic coding and tool-calling tasks.

vision 35b
ollama run AI-TAVS/Ornith-vision:35b

Details

yesterday

0442ce2c93ab · 22GB ·

qwen35moe
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34.7B
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Q4_K_M
clip
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447M
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F16
{{ if .System }}<|im_start|>system {{ .System }}<|im_end|> {{ end }}{{ if .Prompt }}<|im_start|>user
{ "stop": [ "<|im_start|>", "<|im_end|>" ] }

Readme

Ornith-1.0-35B 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 35B-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.

https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B-GGUF