Instructions to use thinkingmachines/Inkling-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thinkingmachines/Inkling-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="thinkingmachines/Inkling-NVFP4", 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?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("thinkingmachines/Inkling-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("thinkingmachines/Inkling-NVFP4", 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 thinkingmachines/Inkling-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thinkingmachines/Inkling-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thinkingmachines/Inkling-NVFP4",
		"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/thinkingmachines/Inkling-NVFP4

SGLang

How to use thinkingmachines/Inkling-NVFP4 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 "thinkingmachines/Inkling-NVFP4" \
    --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": "thinkingmachines/Inkling-NVFP4",
		"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 "thinkingmachines/Inkling-NVFP4" \
        --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": "thinkingmachines/Inkling-NVFP4",
		"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 thinkingmachines/Inkling-NVFP4 with Docker Model Runner:
```
docker model run hf.co/thinkingmachines/Inkling-NVFP4
```

aurick commited on 1 day ago

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@@ -101,7 +101,7 @@ Inkling results are reported at effort=0.99. Comparison scores are generated Jul
 |     | IFBench | 79.8%   | 81.4%            | 70.2%     | 76.0%     | 73.3%   | 76.5%           | 77.1%                 | 63.5%                | 72.7%               |
 |     | Global-MMLU-Lite | 88.7%   | 85.6%            | 84.0%     | 88.4%     | 89.2%   | 89.3%           | 92.7%                 | 93.3%                | 91.8%               |
 | **Vision** |     |         |                  |           |           |         |                 |                       |                      |                     |
-|     | MMMU Pro (Standard 10) | 73.3%   | –                | 75.0%     | 79.0%     | –       | –               | 82.0%                 | 84.2%                | 83.0%               |
 |     | Charxiv RQ | 78.1%   | –                | 77.5%     | 80.4%     | –       | –               | 80.2%                 | 86.5%                | 84.7%               |
 |     | Charxiv RQ (with python) | 82.0%   | –                | 78.7%     | 86.7%     | –       | –               | 89.9%                 | 89.4%                | 87.8%               |
 | **Audio** |     |         |                  |           |           |         |                 |                       |                      |                     |

 |     | IFBench | 79.8%   | 81.4%            | 70.2%     | 76.0%     | 73.3%   | 76.5%           | 77.1%                 | 63.5%                | 72.7%               |
 |     | Global-MMLU-Lite | 88.7%   | 85.6%            | 84.0%     | 88.4%     | 89.2%   | 89.3%           | 92.7%                 | 93.3%                | 91.8%               |
 | **Vision** |     |         |                  |           |           |         |                 |                       |                      |                     |
+|     | MMMU Pro (Standard 10) | 73.5%   | –                | 75.0%     | 79.0%     | –       | –               | 82.0%                 | 84.2%                | 83.0%               |
 |     | Charxiv RQ | 78.1%   | –                | 77.5%     | 80.4%     | –       | –               | 80.2%                 | 86.5%                | 84.7%               |
 |     | Charxiv RQ (with python) | 82.0%   | –                | 78.7%     | 86.7%     | –       | –               | 89.9%                 | 89.4%                | 87.8%               |
 | **Audio** |     |         |                  |           |           |         |                 |                       |                      |                     |