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
```

Humble request for an Ornith 122–a10b

by xms991 - opened 1 day ago

Discussion

xms991

1 day ago

First off, thank you for these awesome models.

It’s fantastic to see a company build off of Qwen and actually produce a meaningful improvement. This is a major accomplishment, and tuning the Qwen3.5 series to out-perform the equivalent Qwen3.6 models is incredible. This is a huge testament to your skills, and it is especially meaningful, considering that the Qwen team has stopped releasing its larger model versions.

You are probably aware that the local AI community is desperate for a newer, better 120b-class MoE.

Qwen3.5 122b-a10b is barely better than Qwen3.6 35b-a3b, and Gemma4 teased a 120b MoE but never released it.

It appears that you all have the data and training expertise to make this dream a reality, so I am politely begging you to consider training an Ornith model on top of Qwen3.5 122b-a10b.

I realize this is a big ask, but I cannot overstate how much the local AI space would appreciate it. We could probably even crowdfund it to help offset training costs.

Please consider it, and maybe discuss it with the r/localllama community on Reddit. I think you would find a good number of people willing to donate to make this happen (myself included).

Thank you :)

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