Instructions to use docling-project/SmolDocling-256M-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Sharing MERIT Dataset: Support for Key Info Retrieval & Table Extraction
2
#50 opened 9 months ago
by
de-Rodrigo
SmolDocling Visualizer
๐ฅ 1
1
#49 opened 10 months ago
by
Pier-Jean
smoldocling pre tokenizer
#48 opened 10 months ago
by
anakin004
test
#47 opened 11 months ago
by
PeterWJStaar
ONNX Inference Outputting Gibberish?
#46 opened 11 months ago
by
buckeye17-bah
Finetune the model
๐ 8
#42 opened about 1 year ago
by
abdoubtmne
Is the chat_template.json file wrong?
5
#41 opened about 1 year ago
by
mohamed99akram
Improving data extraction from complex forms
#39 opened about 1 year ago
by
nikogamulin
Poor performance with simple table extraction task
๐ 1
2
#34 opened about 1 year ago
by
hanshupe
Simple test on table and Chinese text
๐ 2
#31 opened about 1 year ago
by
luweigen
Basic Questions
10
#28 opened about 1 year ago
by
tintegral
support key information extraction
๐โ 5
1
#27 opened about 1 year ago
by
YTGao
Table columns are not aligned properly.
2
#23 opened about 1 year ago
by
svkoh
Chart-only Process
1
#22 opened about 1 year ago
by
minkhant-kyaw
Support multi-language
๐ฅ 8
12
#17 opened about 1 year ago
by
nvip1204
Greek Support
1
#16 opened about 1 year ago
by
Panindhra
vLLM docker run
1
#14 opened about 1 year ago
by
anhnh2002
Multilingual Support Plans for SmolDocling-256M
10
#13 opened about 1 year ago
by
reix2098
Fast Batch Inference cant run (on Google colab
#11 opened about 1 year ago
by
JustCleanMan