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 397B
This model card documents Ornith-1.0-397B, the lightweight member of the Ornith family, designed for efficient single-GPU deployment.
Benchmarks
Quickstart
Serving Ornith-1.0-397B
The two recipes below stand up an OpenAI-compatible server on a single 8×80GB GPU node (tensor-parallel 8). Adjust --tensor-parallel-size / --tp to the number of GPUs you have.
For a quick local test (or to script offline generation), load the model directly with Transformers. Make sure you have a recent release installed — see the Transformers installation guide; Ornith-1.0-397B requires .
transformers >= 5.8.1
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "deepreinforce-ai/Ornith-1.0-397B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Write a Python function is_prime(n). Keep it short."}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
generated = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.6,
top_p=0.95,
top_k=20,
)
output_ids = generated[0][inputs.input_ids.shape[1]:]
# The reply contains a <think> ... </think> reasoning block followed by the answer.
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print(content)
To split the reasoning trace from the final answer, parse on the </think> marker:
text = tokenizer.decode(output_ids, skip_special_tokens=True)
if "</think>" in text:
reasoning, answer = text.split("</think>", 1)
reasoning = reasoning.replace("<think>", "").strip()
answer = answer.strip()
else:
reasoning, answer = "", text.strip()
Using Ornith-1.0-397B via the Chat Completions API
Once a vLLM or SGLang server is running, talk to it with any OpenAI-compatible client.
Basic Usage
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="EMPTY", # any non-empty string works for a local server
)
response = client.chat.completions.create(
model="Ornith-1.0-397B",
messages=[
{"role": "user", "content": "Write a one-line Python lambda that squares a number."}
],
temperature=0.6,
top_p=0.95,
max_tokens=1024,
)
message = response.choices[0].message
# reasoning_content holds the <think> trace; content holds the final answer.
print("reasoning:", getattr(message, "reasoning_content", None))
print("answer:", message.content)
You can also stream tokens, or hand the model tools — Ornith-1.0-397B emits well-formed function calls that the server parses into the standard tool_calls field:
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a city",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
},
},
}
]
response = client.chat.completions.create(
model="Ornith-1.0-397B",
messages=[{"role": "user", "content": "What is the weather in Paris right now?"}],
tools=tools,
tool_choice="auto",
temperature=0.6,
max_tokens=2048,
)
tool_call = response.choices[0].message.tool_calls[0]
print(tool_call.function.name, tool_call.function.arguments)
# -> get_weather {"city": "Paris"}
You can point any OpenAI-compatible SDK (Python, Node.js, etc.) or curl at the same /v1/chat/completions endpoint.
Examples of using Ornith with agent harness:
Hermes Agent
# Hermes talks to any OpenAI-compatible endpoint — point it at your Ornith server.
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"
export MODEL="deepreinforce-ai/Ornith-1.0-397B"
OpenClaw
# OpenClaw talks to any OpenAI-compatible endpoint — point it at your Ornith server.
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"
export OPENAI_MODEL="deepreinforce-ai/Ornith-1.0-397B"
Unsloth Studio
pip install unsloth
# Load Ornith for fast local inference or fine-tuning (Python):
# from unsloth import FastLanguageModel
# model, tokenizer = FastLanguageModel.from_pretrained(
# "deepreinforce-ai/Ornith-1.0-397B",
# max_seq_length=262144,
# load_in_4bit=True,
# )
OpenHands
pip install openhands-ai
# OpenHands routes through LiteLLM; the "openai/" prefix selects the OpenAI-compatible path.
export LLM_MODEL="openai/deepreinforce-ai/Ornith-1.0-397B"
export LLM_BASE_URL="http://localhost:8000/v1"
export LLM_API_KEY="EMPTY"
# Launch the CLI (or run the official OpenHands Docker image with the same env vars).
openhands
Coding CLIs
Ornith-1.0-397B is optimized for terminal-based coding agents. Point any OpenAI-compatible coding CLI at your Ornith-1.0-397B endpoint (set OPENAI_BASE_URL and OPENAI_API_KEY) to understand large codebases, automate tedious work, and ship faster.
If you find our work helpful, feel free to give us a cite.
@misc{ornith_397b,
title = {{Ornith-1.0-397B}: Agentic Coding, Open to All},
url = {https://deep-reinforce.com/ornith_1_0.html},
author = {{DeepReinforce Team}},
year = {2026}
}
