ornith

ornith

7,013 Downloads Updated 22 hours ago

A self-improving family of open-source models for agentic coding

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9b 35b

CLI cURL Python JavaScript

Documentation Documentation

ollama run ornith

curl http://localhost:11434/api/chat \ -d '{ "model": "ornith", "messages": [{"role": "user", "content": "Hello!"}] }'

from ollama import chat

response = chat( model='ornith', messages=[{'role': 'user', 'content': 'Hello!'}], ) print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({ model: 'ornith', messages: [{role: 'user', content: 'Hello!'}], }) console.log(response.message.content)

Models

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Name

9 models

Size / Usage

Context

Input

ornith:latest 5.6GB · 256K context window · Text · 22 hours ago

ornith:latest

5.6GB

256K

Text

ornith:9b latest 5.6GB · 256K context window · Text · 22 hours ago

ornith:9b latest

5.6GB

256K

Text

ornith:35b 21GB · 256K context window · Text · 22 hours ago

ornith:35b

21GB

256K

Text

Readme

Highlights:

• State-of-the-Art Coding Agents: 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.

9B

ollama run ornith:9b



35B

ollama run ornith:35b



References

• Blog

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