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
```

MatteoOmenetti commited on Aug 25, 2025

Commit

83c9645

verified ·

1 Parent(s): f443b0a

Update README.md

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README.md +21 -9

README.md CHANGED Viewed

@@ -1,12 +1,17 @@
----
-base_model:
-- HuggingFaceTB/SmolVLM-256M-Instruct
-language:
-- en
-library_name: transformers
-license: cdla-permissive-2.0
-pipeline_tag: image-text-to-text
----
 <div style="display: flex; align-items: center;">
     <img src="https://huggingface.co/ds4sd/SmolDocling-256M-preview/resolve/main/assets/SmolDocling_doctags1.png" alt="SmolDocling" style="width: 200px; height: auto; margin-right: 20px;">
@@ -376,6 +381,13 @@ DocTags are integrated with Docling, which allows export to HTML, Markdown, and
   </tr>
 </table>
 #### Model Summary
 - **Developed by:** Docling Team, IBM Research

+---
+base_model:
+- HuggingFaceTB/SmolVLM-256M-Instruct
+language:
+- en
+library_name: transformers
+license: cdla-permissive-2.0
+pipeline_tag: image-text-to-text
+datasets:
+- ds4sd/SynthCodeNet
+- ds4sd/SynthFormulaNet
+- ds4sd/SynthChartNet
+- HuggingFaceM4/DoclingMatix
+---
 <div style="display: flex; align-items: center;">
     <img src="https://huggingface.co/ds4sd/SmolDocling-256M-preview/resolve/main/assets/SmolDocling_doctags1.png" alt="SmolDocling" style="width: 200px; height: auto; margin-right: 20px;">
   </tr>
 </table>
+#### 📊 Datasets
+- [SynthCodeNet](https://huggingface.co/datasets/ds4sd/SynthCodeNet)
+- [SynthFormulaNet](https://huggingface.co/datasets/ds4sd/SynthFormulaNet)
+- [SynthChartNet](https://huggingface.co/datasets/ds4sd/SynthChartNet)
+- [DoclingMatix](https://huggingface.co/datasets/HuggingFaceM4/DoclingMatix)
 #### Model Summary
 - **Developed by:** Docling Team, IBM Research