Ornith-1.0

About

What Ornith-1.0 is

[](https://deep-reinforce.com/ornith.html) Ornith-1.0-9B-GGUF 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 9B This model card documents Ornith-1.0-9B, the most lightweight member of the Ornith family, designed for efficient single-GPU deployment. Benchmarks Terminal-Bench 2.1 (Terminus-2): We evaluate Terminal-Bench 2.1 using the Harbor/Terminus-2 framework with parser=json, temperature=1.0, topp=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/main/chattemplate.jinja), and modify Harbor to align with vLLM's reasoningcontent key.<br/ Terminal-Bench 2.1 (Claude Code): We evaluate Terminal-Bench 2.1 using Claude Code 2.1.126 with parser=json, temperature=1.0, topp=1.0, maxnewtokens=131072. Results are averaged over 5 runs. Again, Qwen chat template needs to be modified.<br/ SWE-Bench Verified, Pro and Multilingual: using OpenHands harness with temp=1.0, topp=0.95, 256k context window.<br/ SWE Atlas QnA, RF, TW: using mini SWE agent harness with temp=1.0, topp=0.95, 128K context window. Results are averaged over 5 runs.<br/ NL2Repo: with temperature=1.0, topp=1.0, 400K context, 48K output and anti-hacking filters.<br/ ClawEval: An agentic code benchmark over real-user tas

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys ornith-1-0 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ornith-1-0 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"ornith-1-0","messages":[{"role":"user","content":"Hello"}]}'

Details

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Open weights

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