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
```

deep-reinforce commited on 6 days ago

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@@ -32,59 +32,78 @@ This model card documents **Ornith-1.0-397B**, the lightweight member of the Orn
 <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:28%;padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #FD8E5B;color:#FD8E5B"></th><th style="width:14.4%;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-A3B</th><th style="width:14.4%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;">Qwen3.5-35B-A3B</th><th style="width:14.4%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;">Qwen3.6-35B-A3B</th><th style="width:14.4%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;">Gemma4-31B</th><th style="width:14.4%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;border-left:2px solid rgba(253, 142, 91, 0.55);">Qwen3.5-397B</th></tr></thead>
 <tbody>
-<tr><td colspan="5" 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><td colspan="1" style="border-bottom:1px solid rgba(253, 142, 91, 0.2);background:rgba(253, 142, 91, 0.1);border-left:2px solid rgba(253, 142, 91, 0.55);"></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)">64.2</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.4</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52.5</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);border-left:2px solid rgba(253, 142, 91, 0.55);">53.5</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)">75.6</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.0</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.4</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52.0</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);border-left:2px solid rgba(253, 142, 91, 0.55);">76.4</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)">50.4</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">44.6</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">49.5</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">35.7</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);border-left:2px solid rgba(253, 142, 91, 0.55);">51.6</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)">69.3</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.3</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.2</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.7</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);border-left:2px solid rgba(253, 142, 91, 0.55);">69.3</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)">34.6</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">20.5</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">29.4</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">15.5</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);border-left:2px solid rgba(253, 142, 91, 0.55);">36.8</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)">69.8</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.4</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.7</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.5</td>
-<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);border-left:2px solid rgba(253, 142, 91, 0.55);">70.7</td>
 </tr>
-</tbody>
-</table>
 <p style="margin-top:12px;font-size:10px;opacity:0.7">
 * Terminal-Bench 2.1: Harbor/Terminus-2, 3h timeout, 32 CPU / 48GB RAM, avg of 5 runs.<br/>
 * All SWE-Bench：Mini-SWE-Agent, temp=1.0, top_p=0.95, 200K context window.<br/>

 <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 6px;text-align:left;font-weight:600;border-bottom:2px solid #FD8E5B;color:#FD8E5B"></th>
+<th style="width:10.85%;padding:10px 5px;text-align:center;font-weight:700;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:13px;background:rgba(253, 142, 91, 0.12)">Ornith-1.0-397B</th>
+<th style="width:10.85%;padding:10px 5px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:13px;">Qwen3.5-397B</th>
+<th style="width:10.85%;padding:10px 5px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:13px;">Qwen3.7-Max</th>
+<th style="width:10.85%;padding:10px 5px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:13px;">Minimax M3</th>
+<th style="width:10.85%;padding:10px 5px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:13px;">DeepSeek-V4-Pro</th>
+<th style="width:10.85%;padding:10px 5px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:13px;">Claude Opus 4.7</th>
+<th style="width:10.85%;padding:10px 5px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:13px;">Claude Opus 4.8</th>
+</tr></thead>
 <tbody>
+<tr><td colspan="8" 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 5px;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 5px;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 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">53.5</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.5</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.3</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85</td>
 </tr>
 <tr>
+<td style="padding:7px 5px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
+<td style="padding:7px 5px;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 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.4</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.4</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.6</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">80.8</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.6</td>
 </tr>
 <tr>
+<td style="padding:7px 5px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Pro</td>
+<td style="padding:7px 5px;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 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.6</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.6</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">55.4</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">64.3</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.2</td>
 </tr>
 <tr>
+<td style="padding:7px 5px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Multilingual</td>
+<td style="padding:7px 5px;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 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.3</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.3</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.2</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
 </tr>
 <tr>
+<td style="padding:7px 5px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">NL2Repo</td>
+<td style="padding:7px 5px;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 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.8</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">47.2</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">42.1</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">69.7</td>
 </tr>
 <tr>
+<td style="padding:7px 5px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Claw-eval Avg</td>
+<td style="padding:7px 5px;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 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.7</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.2</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">75.8</td>
+<td style="padding:7px 5px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.2</td>
+<td style="padding:7px 5px;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: Harbor/Terminus-2, 3h timeout, 32 CPU / 48GB RAM, avg of 5 runs.<br/>
 * All SWE-Bench：Mini-SWE-Agent, temp=1.0, top_p=0.95, 200K context window.<br/>