Instructions to use deepreinforce-ai/Ornith-1.0-397B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepreinforce-ai/Ornith-1.0-397B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="deepreinforce-ai/Ornith-1.0-397B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("deepreinforce-ai/Ornith-1.0-397B")
model = AutoModelForMultimodalLM.from_pretrained("deepreinforce-ai/Ornith-1.0-397B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepreinforce-ai/Ornith-1.0-397B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepreinforce-ai/Ornith-1.0-397B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepreinforce-ai/Ornith-1.0-397B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepreinforce-ai/Ornith-1.0-397B

SGLang

How to use deepreinforce-ai/Ornith-1.0-397B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "deepreinforce-ai/Ornith-1.0-397B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepreinforce-ai/Ornith-1.0-397B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "deepreinforce-ai/Ornith-1.0-397B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepreinforce-ai/Ornith-1.0-397B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepreinforce-ai/Ornith-1.0-397B with Docker Model Runner:
```
docker model run hf.co/deepreinforce-ai/Ornith-1.0-397B
```

Ornith-1.0-397B / README.md

deep-reinforce

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	---
	library_name: transformers
	license: mit
	license_link: https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B/blob/main/LICENSE
	pipeline_tag: text-generation
	---


	<img width="600px" src="assets/ornith_logo.png">

	[![Ornith Blog](https://img.shields.io/badge/%F0%9F%A6%A2%EF%B8%8F%20Ornith%20Blog%20-FD8E5B)](https://deep-reinforce.com/ornith.html)

	# Ornith-1.0-397B

	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.

	<img style="width: 100%; max-width: 900px;" src="assets/ornith_397b_eval.png" alt="Ornith 35B Benchmark Results" title="Ornith 35B Benchmark Results">

	## 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

	<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;width:100%;margin:0 auto;padding:16px 0">
	<table style="width:100%;table-layout:fixed;border-collapse:collapse;font-size:13px">
	<thead><tr>
	<th style="width:24%;padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #FD8E5B;color:#FD8E5B"></th>
	<th style="width:9.50%;padding:10px 7px;text-align:center;font-weight:700;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;background:rgba(253, 142, 91, 0.12)">Ornith-1.0-397B</th>
	<th style="width:9.50%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Qwen3.5-397B</th>
	<th style="width:9.50%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Qwen3.7-Max</th>
	<th style="width:9.50%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">GLM-5.2-744B</th>
	<th style="width:9.50%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Minimax-M3-428B</th>
	<th style="width:9.50%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">DeepSeek-V4-Pro-1.6T</th>
	<th style="width:9.50%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Claude Opus 4.7</th>
	<th style="width:9.50%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Claude Opus 4.8</th>
	</tr></thead>
	<tbody>
	<tr><td colspan="9" style="padding:8px 12px;font-weight:600;color:#FD8E5B;border-bottom:1px solid rgba(253, 142, 91, 0.2);background:rgba(253, 142, 91, 0.1)">Agentic Coding</td></tr>
	<tr>
	<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Terminal-Bench 2.1 <sub><small>(Terminus-2)</small></sub></td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">77.5</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">53.5</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.5</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.0</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.3</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85</td>
	</tr>
	<tr>
	<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Terminal-Bench 2.1 <sub><small>(Claude Code)</small></sub></td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">78.2</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.6</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.8</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.7</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.5</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.7</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.9</td>
	</tr>
	<tr>
	<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">82.4</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.4</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.4</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.6</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.8</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.6</td>
	</tr>
	<tr>
	<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Pro</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">62.2</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.6</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.6</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.1</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">55.4</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64.3</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.2</td>
	</tr>
	<tr>
	<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Multilingual</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">78.9</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.3</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.3</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.2</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	</tr>
	<tr>
	<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">NL2Repo</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">48.2</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.8</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">47.2</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.9</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">42.1</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.7</td>
	</tr>
	<tr>
	<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Claw-eval Avg</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">77.1</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.7</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.2</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.8</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.2</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	</tr>
	<tr>
	<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE Atlas - QnA</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">41.2</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">20.4</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">37.9</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">27.2</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">40.3</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.8</td>
	</tr>
	<tr>
	<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE Atlas - RF</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">42.6</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">18.4</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.6</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">46.7</td>
	</tr>
	<tr>
	<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE Atlas - TW</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">39.1</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">18.5</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">30.8</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">38.5</td>
	<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
	</tr>
	</tbody>
	</table>

	<p style="margin-top:12px;font-size:10px;opacity:0.7">
	* 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/main/chat_template.jinja), and modify Harbor to align with vLLM's reasoning_content 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, top_p=1.0, max_new_tokens=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, top_p=0.95, 256k context window.<br/>
	* SWE Atlas QnA, RF, TW: using mini SWE agent harness with temp=1.0, top_p=0.95, 128K context window. Results are averaged over 5 runs.<br/>
	* NL2Repo: with temperature=1.0, top_p=1.0, 400K context, 48K output and anti-hacking filters.<br/>
	* ClawEval: An agentic code benchmark over real-user task distributions; temp=0.6 and 256K context.<br/>
	</p>

	</div>




	## Quickstart


	<div style="border-left:4px solid #FD8E5B;background:rgba(253,142,91,0.1);border-radius:6px;padding:12px 16px;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;font-size:14px;line-height:1.6">
	<div style="font-weight:700;color:#FD8E5B;margin-bottom:6px">📝 NOTE</div>
	<p style="margin:0 0 10px"><b>Ornith-1.0-397B</b> is a <b>reasoning model</b>: by default the assistant turn opens with a <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px"><think> … </think></code> block before the final answer. The serving recipes below enable a reasoning parser so the chain-of-thought is returned in a separate <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">reasoning_content</code> field, and a tool-call parser so the model's <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px"><tool_call></code> blocks are surfaced as OpenAI-style <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">tool_calls</code>.</p>
	<p style="margin:0 0 6px">Serving Ornith-1.0-397B requires recent runtimes:</p>
	<ul style="margin:0;padding-left:20px">
	<li><b>Transformers</b> ≥ 5.8.1</li>
	<li><b>vLLM</b> ≥ 0.19.1</li>
	<li><b>SGLang</b> ≥ 0.5.9</li>
	</ul>
	</div>

	### 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.

	#### vLLM

	```bash
	vllm serve deepreinforce-ai/Ornith-1.0-397B \
	--served-model-name Ornith-1.0-397B \
	--tensor-parallel-size 8 \
	--host 0.0.0.0 --port 8000 \
	--max-model-len 262144 \
	--gpu-memory-utilization 0.90 \
	--enable-prefix-caching \
	--enable-auto-tool-choice --tool-call-parser qwen3_xml \
	--reasoning-parser qwen3 \
	--trust-remote-code
	```

	#### SGLang

	```bash
	python -m sglang.launch_server \
	--model-path deepreinforce-ai/Ornith-1.0-397B \
	--served-model-name Ornith-1.0-397B \
	--tp 8 \
	--host 0.0.0.0 --port 8000 \
	--context-length 262144 \
	--mem-fraction-static 0.85 \
	--tool-call-parser qwen3_coder \
	--reasoning-parser qwen3
	```

	#### Hugging Face Transformers

	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](https://huggingface.co/docs/transformers/installation); Ornith-1.0-397B requires `transformers >= 5.8.1`.

	```python
	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:

	```python
	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

	```python
	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:

	```python
	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.

	## Agentic Usage

	Ornith-1.0-397B excels in tool-calling and agentic coding capabilities.

	### Agent Frameworks

	Because Ornith-1.0-397B exposes an OpenAI-compatible endpoint with tool calling, it works out of the box with standard agent frameworks. Below is a minimal example that connects Ornith-1.0-397B to tools through an MCP server.

	```python
	import os
	from openai import OpenAI

	client = OpenAI(
	base_url=os.getenv("OPENAI_BASE_URL", "http://localhost:8000/v1"),
	api_key=os.getenv("OPENAI_API_KEY", "EMPTY"),
	)

	tools = [
	{
	"type": "function",
	"function": {
	"name": "run_shell",
	"description": "Run a shell command and return its output.",
	"parameters": {
	"type": "object",
	"properties": {
	"command": {"type": "string", "description": "The command to run"}
	},
	"required": ["command"],
	},
	},
	}
	]

	messages = [{"role": "user", "content": "List the Python files in the current directory."}]

	response = client.chat.completions.create(
	model="deepreinforce-ai/Ornith-1.0-397B",
	messages=messages,
	tools=tools,
	temperature=0.6,
	top_p=0.95,
	)
	print(response.choices[0].message)
	```

	Examples of using Ornith with agent harness:

	#### Hermes Agent
	```bash
	# 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

	```bash
	# 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

	```bash
	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
	```bash
	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.

	#### OpenCode
	```bash
	# Register your local Ornith endpoint as a provider in ~/.config/opencode/opencode.json:
	#
	# {
	# "$schema": "https://opencode.ai/config.json",
	# "provider": {
	# "ornith": {
	# "npm": "@ai-sdk/openai-compatible",
	# "name": "Ornith (local)",
	# "options": { "baseURL": "http://localhost:8000/v1", "apiKey": "EMPTY" },
	# "models": { "deepreinforce-ai/Ornith-1.0-397B": { "name": "Ornith-1.0-397B" } }
	# }
	# }
	# }

	opencode
	```


	### Citation

	If you find our work helpful, feel free to give us a cite.

	```bibtex
	@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}
	}
	```