Instructions to use docling-project/SmolDocling-256M-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use docling-project/SmolDocling-256M-preview with Transformers:

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

pipe = pipeline("image-text-to-text", model="docling-project/SmolDocling-256M-preview")
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("docling-project/SmolDocling-256M-preview")
model = AutoModelForImageTextToText.from_pretrained("docling-project/SmolDocling-256M-preview")
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

vLLM

How to use docling-project/SmolDocling-256M-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "docling-project/SmolDocling-256M-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "docling-project/SmolDocling-256M-preview",
		"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/docling-project/SmolDocling-256M-preview

SGLang

How to use docling-project/SmolDocling-256M-preview 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 "docling-project/SmolDocling-256M-preview" \
    --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": "docling-project/SmolDocling-256M-preview",
		"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 "docling-project/SmolDocling-256M-preview" \
        --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": "docling-project/SmolDocling-256M-preview",
		"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 docling-project/SmolDocling-256M-preview with Docker Model Runner:
```
docker model run hf.co/docling-project/SmolDocling-256M-preview
```

Is the chat_template.json file wrong?

#41

by mohamed99akram - opened Apr 18, 2025

Discussion

mohamed99akram

Apr 18, 2025

•

edited Apr 19, 2025

The chat_template.json you provided seems to output

"<|im_start|>User:{PROMPT_TEXT} <image><end_of_utterance>\nAssistant:"

although your example is

"<|im_start|>User:<image>{PROMPT_TEXT}<end_of_utterance>\nAssistant:"

Shouldn't <image> be before the prompt?
I am using PROMPT_TEXT="Convert this page to docling." and it is not accurate.

When I try to put this template as a jinja file for vLLM it gives bad output.
And when trying this template:

<|im_start|>{% for message in messages %}{{message['role'] | capitalize}}:{% for line in message['content'] %}{% if line['type'] == 'image' or line['type'] == 'image_url' %}{{ '<image>' }}{% endif %}{% endfor %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{ line['text'] }}{% endif %}{% endfor %}<end_of_utterance>
{% endfor %}{% if add_generation_prompt %}Assistant:{% endif %}

which works in vLLM.
So, in summary, Is there a problem with the provided chat template json file?
Thanks.

nitincypher

Jun 17, 2025

The chat_template.json you provided seems to output
"<|im_start|>User:{PROMPT_TEXT} <image><end_of_utterance>\nAssistant:"
although your example is
"<|im_start|>User:<image>{PROMPT_TEXT}<end_of_utterance>\nAssistant:"
Shouldn't <image> be before the prompt?
I am using PROMPT_TEXT="Convert this page to docling." and it is not accurate.

When I try to put this template as a jinja file for vLLM it gives bad output.
And when trying this template:
<|im_start|>{% for message in messages %}{{message['role'] | capitalize}}:{% for line in message['content'] %}{% if line['type'] == 'image' or line['type'] == 'image_url' %}{{ '<image>' }}{% endif %}{% endfor %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{ line['text'] }}{% endif %}{% endfor %}<end_of_utterance>
{% endfor %}{% if add_generation_prompt %}Assistant:{% endif %}
which works in vLLM.
So, in summary, Is there a problem with the provided chat template json file?
Thanks.

Hi, can you please what configuration of chat_template.json and chat_template = f"<|im_start|>User:{PROMPT_TEXT}\nAssistant:" works for you?

I'm also trying to run the vllm example and it is not following the doctags properly.

I'm getting output like this "58>61>432>113>On December 15 and 16, 2023"

nitincypher

Jun 17, 2025

•

edited Jun 17, 2025

Another example

<text>125>113>215>120>February 15, 2025</text>

MatteoOmenetti

Docling org Jun 17, 2025

Hello @nitincypher ,
Regarding your second problem of getting outputs like this <text>125>113>215>120>February 15, 2025</text>, when you decode you need to set skip_special_characters to False.

mohamed99akram

Jun 17, 2025

•

edited Jun 17, 2025

Did you try the one I provided in my question? Making <image> before prompt text?
Also, the example of vLLM library they provided seems to work. But for a vLLM docker instance, were you able to make sure it loads the file correctly and outputs the template in logs?

dionki

Jun 26, 2025

Hello @nitincypher ,
Regarding your second problem of getting outputs like this <text>125>113>215>120>February 15, 2025</text>, when you decode you need to set skip_special_characters to False.

where is that to be set?

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