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

Libraries

How to use thinkingmachines/Inkling with Transformers:

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

pipe = pipeline("image-text-to-text", model="thinkingmachines/Inkling")
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")
model = AutoModelForMultimodalLM.from_pretrained("thinkingmachines/Inkling")
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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thinkingmachines/Inkling with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thinkingmachines/Inkling"
# 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",
		"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

SGLang

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

aurick commited on 4 days ago

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@@ -80,10 +80,10 @@ Inkling results are reported at effort=0.99. Comparison scores are generated Jul
 |     |     | Inkling | Nemotron 3 Ultra | Kimi K2.5 | Kimi K2.6 | GLM 5.2 | DeepSeek V4 Pro | Gemini 3.1 Pro (high) | Claude Fable 5 (max) | GPT 5.6 Sol (xhigh) |
 |-----|-----|---------|------------------|-----------|-----------|---------|-----------------|-----------------------|----------------------|---------------------|
 | **Reasoning** |     |         |                  |           |           |         |                 |                       |                      |                     |
-|     | HLE (text only) | 30.0%   | 26.6%            | 29.4%     | 35.9%     | 40.1%   | 35.9%           | 44.7%                 | 53.3%                | 47.2%               |
 |     | HLE (with tools) | 46.0%   | 37.4%            | 50.2%     | 54.0%     | 54.7%   | 48.2%           | 51.4%                 | 64.5%                | 55.0%               |
 |     | AIME 2026 | 97.1%   | 94.2%            | 95.8%     | 96.4%     | 99.2%   | 96.7%           | 98.3%                 | –                    | 99.9%               |
-|     | GPQA Diamond | 87.9%   | 86.7%            | 87.9%     | 91.1%     | 89.5%   | 88.8%           | 94.1%                 | 92.6%                | 94.1%               |
 | **Agentic (coding)** |     |         |                  |           |           |         |                 |                       |                      |                     |
 |     | SWEBench Verified | 77.6%   | 70.7%            | 76.8%     | 80.2%     | –       | 80.6%           | 80.6%                 | 95.0%                | –                   |
 |     | SWEBench Pro (Public) | 54.3%   | 46.4%            | 50.7%     | 58.6%     | 62.1%   | 55.4%           | 54.2%                 | 80.0%                | 64.6%               |
@@ -91,7 +91,7 @@ Inkling results are reported at effort=0.99. Comparison scores are generated Jul
 |     | GDPVal-AA v2 | 1233    | 1164             | 1009         | 1190      | 1514    | 1307            | 962                   | 1760                 | 1748                |
 | **Agentic (general)** |     |         |                  |           |           |         |                 |                       |                      |                     |
 |     | MCP Atlas | 74.1%   | 44.7%                | 64.0%     | 68.1%     | 77.8%   | 73.2%           | 78.2%                 | 83.3%                | 81.8%               |
-|     | Tau 3 Banking | 22.3%   | 13.8%            | 13.2%     | 20.6%     | 26.8%   | 25.8%           | 16.5%                 | 26.8%                | 33.0%               |
 | **Factuality** |     |         |                  |           |           |         |                 |                       |                      |                     |
 |     | BrowseComp (w/ Ctx) | 77.1%   | –                | 74.9%     | 83.2%     | –       | 83.4%           | 85.9%                 | 88.0%                | 89.4%               |
 |     | SimpleQA Verified | 43.9%   | 32.4%            | 36.9%     | 38.7%     | 38.1%   | 57.0%           | 77.3%                 | 68.3%                | 71.6%               |

 |     |     | Inkling | Nemotron 3 Ultra | Kimi K2.5 | Kimi K2.6 | GLM 5.2 | DeepSeek V4 Pro | Gemini 3.1 Pro (high) | Claude Fable 5 (max) | GPT 5.6 Sol (xhigh) |
 |-----|-----|---------|------------------|-----------|-----------|---------|-----------------|-----------------------|----------------------|---------------------|
 | **Reasoning** |     |         |                  |           |           |         |                 |                       |                      |                     |
+|     | HLE (text only) | 29.7%   | 26.6%            | 29.4%     | 35.9%     | 40.1%   | 35.9%           | 44.7%                 | 53.3%                | 47.2%               |
 |     | HLE (with tools) | 46.0%   | 37.4%            | 50.2%     | 54.0%     | 54.7%   | 48.2%           | 51.4%                 | 64.5%                | 55.0%               |
 |     | AIME 2026 | 97.1%   | 94.2%            | 95.8%     | 96.4%     | 99.2%   | 96.7%           | 98.3%                 | –                    | 99.9%               |
+|     | GPQA Diamond | 87.2%   | 86.7%            | 87.9%     | 91.1%     | 89.5%   | 88.8%           | 94.1%                 | 92.6%                | 94.1%               |
 | **Agentic (coding)** |     |         |                  |           |           |         |                 |                       |                      |                     |
 |     | SWEBench Verified | 77.6%   | 70.7%            | 76.8%     | 80.2%     | –       | 80.6%           | 80.6%                 | 95.0%                | –                   |
 |     | SWEBench Pro (Public) | 54.3%   | 46.4%            | 50.7%     | 58.6%     | 62.1%   | 55.4%           | 54.2%                 | 80.0%                | 64.6%               |
 |     | GDPVal-AA v2 | 1233    | 1164             | 1009         | 1190      | 1514    | 1307            | 962                   | 1760                 | 1748                |
 | **Agentic (general)** |     |         |                  |           |           |         |                 |                       |                      |                     |
 |     | MCP Atlas | 74.1%   | 44.7%                | 64.0%     | 68.1%     | 77.8%   | 73.2%           | 78.2%                 | 83.3%                | 81.8%               |
+|     | Tau 3 Banking | 23.7%   | 13.8%            | 13.2%     | 20.6%     | 26.8%   | 25.8%           | 16.5%                 | 26.8%                | 33.0%               |
 | **Factuality** |     |         |                  |           |           |         |                 |                       |                      |                     |
 |     | BrowseComp (w/ Ctx) | 77.1%   | –                | 74.9%     | 83.2%     | –       | 83.4%           | 85.9%                 | 88.0%                | 89.4%               |
 |     | SimpleQA Verified | 43.9%   | 32.4%            | 36.9%     | 38.7%     | 38.1%   | 57.0%           | 77.3%                 | 68.3%                | 71.6%               |