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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("docling-project/SmolDocling-256M-preview")
model = AutoModelForMultimodalLM.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 Settings

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
```

Add link to paper and project page

by nielsr HF Staff - opened Mar 18, 2025

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+11

-7

Files changed (1) hide show

README.md +11 -7

README.md CHANGED Viewed

@@ -1,10 +1,10 @@
 ---
-library_name: transformers
-license: apache-2.0
-language:
-- en
 base_model:
 - HuggingFaceTB/SmolVLM-256M-Instruct
 pipeline_tag: image-text-to-text
 ---
@@ -16,6 +16,8 @@ pipeline_tag: image-text-to-text
     </div>
 </div>
 ### 🚀 Features:
 - 🏷️ **DocTags for Efficient Tokenization** – Introduces DocTags an efficient and minimal representation for documents that is fully compatible with **DoclingDocuments**.
 - 🔍 **OCR (Optical Character Recognition)** – Extracts text accurately from images.
@@ -39,7 +41,6 @@ pipeline_tag: image-text-to-text
 - 🧪 **Chemical Recognition**
 - 📙 **Datasets**
 ## ⌨️ Get started (code examples)
 You can use **transformers** or **vllm** to perform inference, and [Docling](https://github.com/docling-project/docling) to convert results to variety of ourput formats (md, html, etc.):
@@ -145,7 +146,8 @@ sampling_params = SamplingParams(
     temperature=0.0,
     max_tokens=8192)
-chat_template = f"<|im_start|>User:<image>{PROMPT_TEXT}<end_of_utterance>\nAssistant:"
 image_files = sorted([f for f in os.listdir(IMAGE_DIR) if f.lower().endswith((".png", ".jpg", ".jpeg"))])
@@ -253,6 +255,8 @@ DocTags are integrated with Docling, which allows export to HTML, Markdown, and
 **Paper:** [arXiv](https://arxiv.org/abs/2503.11576)
 **Citation:**
 ```
 @misc{nassar2025smoldoclingultracompactvisionlanguagemodel,
@@ -265,4 +269,4 @@ DocTags are integrated with Docling, which allows export to HTML, Markdown, and
       url={https://arxiv.org/abs/2503.11576},
 }
 ```
-**Demo:** [Coming soon]

 ---
 base_model:
 - HuggingFaceTB/SmolVLM-256M-Instruct
+language:
+- en
+library_name: transformers
+license: apache-2.0
 pipeline_tag: image-text-to-text
 ---
     </div>
 </div>
+This model was presented in the paper [SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion](https://huggingface.co/papers/2503.11576).
 ### 🚀 Features:
 - 🏷️ **DocTags for Efficient Tokenization** – Introduces DocTags an efficient and minimal representation for documents that is fully compatible with **DoclingDocuments**.
 - 🔍 **OCR (Optical Character Recognition)** – Extracts text accurately from images.
 - 🧪 **Chemical Recognition**
 - 📙 **Datasets**
 ## ⌨️ Get started (code examples)
 You can use **transformers** or **vllm** to perform inference, and [Docling](https://github.com/docling-project/docling) to convert results to variety of ourput formats (md, html, etc.):
     temperature=0.0,
     max_tokens=8192)
+chat_template = f"<|im_start|>User:<image>{PROMPT_TEXT}<end_of_utterance>
+Assistant:"
 image_files = sorted([f for f in os.listdir(IMAGE_DIR) if f.lower().endswith((".png", ".jpg", ".jpeg"))])
 **Paper:** [arXiv](https://arxiv.org/abs/2503.11576)
+**Project Page:** [Hugging Face](https://huggingface.co/ds4sd/SmolDocling-256M-preview)
 **Citation:**
 ```
 @misc{nassar2025smoldoclingultracompactvisionlanguagemodel,
       url={https://arxiv.org/abs/2503.11576},
 }
 ```
+**Demo:** [Coming soon]