Instructions to use nvidia/NV-Reason-CT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/NV-Reason-CT with Transformers:

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

pipe = pipeline("image-text-to-text", model="nvidia/NV-Reason-CT", trust_remote_code=True)
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("nvidia/NV-Reason-CT", trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained("nvidia/NV-Reason-CT", trust_remote_code=True, device_map="auto")
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 nvidia/NV-Reason-CT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/NV-Reason-CT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/NV-Reason-CT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/NV-Reason-CT

SGLang

How to use nvidia/NV-Reason-CT 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 "nvidia/NV-Reason-CT" \
    --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": "nvidia/NV-Reason-CT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "nvidia/NV-Reason-CT" \
        --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": "nvidia/NV-Reason-CT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use nvidia/NV-Reason-CT with Docker Model Runner:
```
docker model run hf.co/nvidia/NV-Reason-CT
```

NV-Reason-CT

File size: 1,713 Bytes

33b5e09

{
  "auto_map": {
    "AutoProcessor": "processor.VLM3D_Processor"
  },
  "image_processor": {
    "auto_map": {
      "AutoProcessor": "processor.VLM3D_Processor"
    },
    "do_convert_rgb": true,
    "do_normalize": true,
    "do_rescale": true,
    "do_resize": true,
    "image_mean": [
      0.5,
      0.5,
      0.5
    ],
    "image_processor_type": "Qwen2VLImageProcessor",
    "image_std": [
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    "patch_size": 16,
    "resample": 3,
    "rescale_factor": 0.00392156862745098,
    "size": {
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      "shortest_edge": 65536
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    "temporal_patch_size": 2
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  "image_processor_3d": {
    "final_grid_size": [
      24,
      24,
      24
    ],
    "image_processor_type": "ImageLoader3D",
    "merge_size": 1,
    "normalize_mode": "ct",
    "pixdim": [
      2.0,
      2.0,
      2.0
    ],
    "spatial_size": [
      192,
      192,
      192
    ]
  },
  "processor_class": "VLM3D_Processor",
  "video_processor": {
    "auto_map": {
      "AutoProcessor": "processor.VLM3D_Processor"
    },
    "do_convert_rgb": true,
    "do_normalize": true,
    "do_rescale": true,
    "do_resize": true,
    "do_sample_frames": true,
    "fps": 2,
    "image_mean": [
      0.5,
      0.5,
      0.5
    ],
    "image_std": [
      0.5,
      0.5,
      0.5
    ],
    "max_frames": 768,
    "merge_size": 2,
    "min_frames": 4,
    "patch_size": 16,
    "resample": 3,
    "rescale_factor": 0.00392156862745098,
    "return_metadata": false,
    "size": {
      "longest_edge": 25165824,
      "shortest_edge": 4096
    },
    "temporal_patch_size": 2,
    "video_processor_type": "Qwen3VLVideoProcessor"
  }
}