docling-project/SmolDocling-256M-preview

📢 New Release: We’ve released granite-docling-258M, the successor to SmolDocling. It will now receive updates and support, check it out!

SmolDocling-256M-preview

SmolDocling is a multimodal Image-Text-to-Text model designed for efficient document conversion. It retains Docling's most popular features while ensuring full compatibility with Docling through seamless support for DoclingDocuments.

This model was presented in the paper SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion.

🚀 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.
• 📐 Layout and Localization – Preserves document structure and document element bounding boxes.
• 💻 Code Recognition – Detects and formats code blocks including identation.
• 🔢 Formula Recognition – Identifies and processes mathematical expressions.
• 📊 Chart Recognition – Extracts and interprets chart data.
• 📑 Table Recognition – Supports column and row headers for structured table extraction.
• 🖼️ Figure Classification – Differentiates figures and graphical elements.
• 📝 Caption Correspondence – Links captions to relevant images and figures.
• 📜 List Grouping – Organizes and structures list elements correctly.
• 📄 Full-Page Conversion – Processes entire pages for comprehensive document conversion including all page elements (code, equations, tables, charts etc.)
• 🔲 OCR with Bounding Boxes – OCR regions using a bounding box.
• 📂 General Document Processing – Trained for both scientific and non-scientific documents.
• 🔄 Seamless Docling Integration – Import into Docling and export in multiple formats.
• 💨 Fast inference using VLLM – Avg of 0.35 secs per page on A100 GPU.

🚧 Coming soon!

• 📊 Better chart recognition 🛠️
• 📚 One shot multi-page inference ⏱️
• 🧪 Chemical Recognition
• 📙 Datasets

⌨️ Get started (code examples)

You can use transformers, vllm, or onnx to perform inference, and Docling to convert results to variety of output formats (md, html, etc.):

📄 Single page image inference using Tranformers 🤖

```python # Prerequisites: # pip install torch # pip install docling_core # pip install transformers

import torch from docling_core.types.doc import DoclingDocument from docling_core.types.doc.document import DocTagsDocument from transformers import AutoProcessor, AutoModelForVision2Seq from transformers.image_utils import load_image from pathlib import Path

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

Load images

image = load_image("https://upload.wikimedia.org/wikipedia/commons/7/76/GazettedeFrance.jpg")

Initialize processor and model

processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview") model = AutoModelForVision2Seq.from_pretrained( "ds4sd/SmolDocling-256M-preview", torch_dtype=torch.bfloat16, _attn_implementation="flash_attention_2" if DEVICE == "cuda" else "eager", ).to(DEVICE)

Create input messages

messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Convert this page to docling."} ] }, ]

Prepare inputs

prompt = processor.apply_chat_template(messages, add_generation_prompt=True) inputs = processor(text=prompt, images=[image], return_tensors="pt") inputs = inputs.to(DEVICE)

Generate outputs

generated_ids = model.generate(**inputs, max_new_tokens=8192) prompt_length = inputs.input_ids.shape[1] trimmed_generated_ids = generated_ids[:, prompt_length:] doctags = processor.batch_decode( trimmed_generated_ids, skip_special_tokens=False, )[0].lstrip()

Populate document

doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([doctags], [image]) print(doctags) # create a docling document doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")

export as any format

# HTML # Path("Out/").mkdir(parents=True, exist_ok=True) # output_path_html = Path("Out/") / "example.html" # doc.save_as_html(output_path_html) # MD print(doc.export_to_markdown()) ```

🚀 Fast Batch Inference Using VLLM

```python # Prerequisites: # pip install vllm # pip install docling_core # place page images you want to convert into "img/" dir

import time import os from vllm import LLM, SamplingParams from PIL import Image from docling_core.types.doc import DoclingDocument from docling_core.types.doc.document import DocTagsDocument from pathlib import Path

Configuration

MODEL_PATH = "ds4sd/SmolDocling-256M-preview" IMAGE_DIR = "img/" # Place your page images here OUTPUT_DIR = "out/" PROMPT_TEXT = "Convert page to Docling."

Ensure output directory exists

os.makedirs(OUTPUT_DIR, exist_ok=True)

Initialize LLM

llm = LLM(model=MODEL_PATH, limit_mm_per_prompt={"image": 1})

sampling_params = SamplingParams( temperature=0.0, max_tokens=8192)

chat_template = f"User:{PROMPT_TEXT} Assistant:"

image_files = sorted([f for f in os.listdir(IMAGE_DIR) if f.lower().endswith((".png", ".jpg", ".jpeg"))])

start_time = time.time() total_tokens = 0

for idx, img_file in enumerate(image_files, 1): img_path = os.path.join(IMAGE_DIR, img_file) image = Image.open(img_path).convert("RGB")

llm_input = {"prompt": chat_template, "multi_modal_data": {"image": image}} output = llm.generate([llm_input], sampling_params=sampling_params)[0] doctags = output.outputs[0].text img_fn = os.path.splitext(img_file)[0] output_filename = img_fn + ".dt" output_path = os.path.join(OUTPUT_DIR, output_filename)

with open(output_path, "w", encoding="utf-8") as f: f.write(doctags)

To convert to Docling Document, MD, HTML, etc.:

doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([doctags], [image]) doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document") # export as any format # HTML # output_path_html = Path(OUTPUT_DIR) / f"{img_fn}.html" # doc.save_as_html(output_path_html) # MD output_path_md = Path(OUTPUT_DIR) / f"{img_fn}.md" doc.save_as_markdown(output_path_md) print(f"Total time: {time.time() - start_time:.2f} sec") ```

ONNX Inference

```python # Prerequisites: # pip install onnxruntime # pip install onnxruntime-gpu from transformers import AutoConfig, AutoProcessor from transformers.image_utils import load_image import onnxruntime import numpy as np import os from docling_core.types.doc import DoclingDocument from docling_core.types.doc.document import DocTagsDocument

os.environ["OMP_NUM_THREADS"] = "1" # cuda os.environ["ORT_CUDA_USE_MAX_WORKSPACE"] = "1"

1. Load models

## Load config and processor model_id = "ds4sd/SmolDocling-256M-preview" config = AutoConfig.from_pretrained(model_id) processor = AutoProcessor.from_pretrained(model_id)

Load sessions

# !wget https://huggingface.co/ds4sd/SmolDocling-256M-preview/resolve/main/onnx/vision_encoder.onnx # !wget https://huggingface.co/ds4sd/SmolDocling-256M-preview/resolve/main/onnx/embed_tokens.onnx # !wget https://huggingface.co/ds4sd/SmolDocling-256M-preview/resolve/main/onnx/decoder_model_merged.onnx # cpu # vision_session = onnxruntime.InferenceSession("vision_encoder.onnx") # embed_session = onnxruntime.InferenceSession("embed_tokens.onnx") # decoder_session = onnxruntime.InferenceSession("decoder_model_merged.onnx"

cuda

vision_session = onnxruntime.InferenceSession("vision_encoder.onnx", providers=["CUDAExecutionProvider"]) embed_session = onnxruntime.InferenceSession("embed_tokens.onnx", providers=["CUDAExecutionProvider"]) decoder_session = onnxruntime.InferenceSession("decoder_model_merged.onnx", providers=["CUDAExecutionProvider"])

Set config values

num_key_value_heads = config.text_config.num_key_value_heads head_dim = config.text_config.head_dim num_hidden_layers = config.text_config.num_hidden_layers eos_token_id = config.text_config.eos_token_id image_token_id = config.image_token_id end_of_utterance_id = processor.tokenizer.convert_tokens_to_ids("")

2. Prepare inputs

## Create input messages messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Convert this page to docling."} ] }, ]

Load image and apply processor

image = load_image("https://ibm.biz/docling-page-with-table") prompt = processor.apply_chat_template(messages, add_generation_prompt=True) inputs = processor(text=prompt, images=[image], return_tensors="np")

Prepare decoder inputs

batch_size = inputs['input_ids'].shape[0] past_key_values = { f'past_key_values.{layer}.{kv}': np.zeros([batch_size, num_key_value_heads, 0, head_dim], dtype=np.float32) for layer in range(num_hidden_layers) for kv in ('key', 'value') } image_features = None input_ids = inputs['input_ids'] attention_mask = inputs['attention_mask'] position_ids = np.cumsum(inputs['attention_mask'], axis=-1)

3. Generation loop

max_new_tokens = 8192 generated_tokens = np.array([[]], dtype=np.int64) for i in range(max_new_tokens): inputs_embeds = embed_session.run(None, {'input_ids': input_ids})[0]

if image_features is None: ## Only compute vision features if not already computed image_features = vision_session.run( ['image_features'], # List of output names or indices { 'pixel_values': inputs['pixel_values'], 'pixel_attention_mask': inputs['pixel_attention_mask'].astype(np.bool_) } )[0] ## Merge text and vision embeddings inputs_embeds[inputs['input_ids'] == image_token_id] = image_features.reshape(-1, image_features.shape[-1])

logits, *present_key_values = decoder_session.run(None, dict( inputs_embeds=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, **past_key_values, ))

Update values for next generation loop

input_ids = logits[:, -1].argmax(-1, keepdims=True) attention_mask = np.ones_like(input_ids) position_ids = position_ids[:, -1:] + 1 for j, key in enumerate(past_key_values): past_key_values[key] = present_key_values[j]

generated_tokens = np.concatenate([generated_tokens, input_ids], axis=-1) if (input_ids == eos_token_id).all() or (input_ids == end_of_utterance_id).all(): break # Stop predicting

doctags = processor.batch_decode( generated_tokens, skip_special_tokens=False, )[0].lstrip()

print(doctags)

doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([doctags], [image]) print(doctags) # create a docling document doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")

print(doc.export_to_markdown()) ```

💻 Local inference on Apple Silicon with MLX: see here

DocTags

DocTags create a clear and structured system of tags and rules that separate text from the document's structure. This makes things easier for Image-to-Sequence models by reducing confusion. On the other hand, converting directly to formats like HTML or Markdown can be messy—it often loses details, doesn’t clearly show the document’s layout, and increases the number of tokens, making processing less efficient. DocTags are integrated with Docling, which allows export to HTML, Markdown, and JSON. These exports can be offloaded to the CPU, reducing token generation overhead and improving efficiency.

Supported Instructions

Description · Instruction · Comment ·

Full conversion · Convert this page to docling. · DocTags represetation ·

Chart · Convert chart to table. · (e.g., <chart>) ·

Formula · Convert formula to LaTeX. · (e.g., <formula>) ·

Code · Convert code to text. · (e.g., <code>) ·

Table · Convert table to OTSL. · (e.g., <otsl>) OTSL: Lysak et al., 2023 ·

Actions and Pipelines · OCR the text in a specific location: <loc_155><loc_233><loc_206><loc_237> · ·

Identify element at: <loc_247><loc_482><10c_252><loc_486> · ·

Find all 'text' elements on the page, retrieve all section headers. · ·

Detect footer elements on the page. · ·

📊 Datasets

• SynthCodeNet
• SynthFormulaNet
• SynthChartNet
• DoclingMatix

Model Summary

• Developed by: Docling Team, IBM Research
• Model type: Multi-modal model (image+text)
• Language(s) (NLP): English
• License: Apache 2.0
• Architecture: Based on Idefics3 (see technical summary)
• Finetuned from model: Based on SmolVLM-256M-Instruct

Repository: Docling

Paper: arXiv

Project Page: Hugging Face

Citation: `` @misc{nassar2025smoldoclingultracompactvisionlanguagemodel, title={SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion}, author={Ahmed Nassar and Andres Marafioti and Matteo Omenetti and Maksym Lysak and Nikolaos Livathinos and Christoph Auer and Lucas Morin and Rafael Teixeira de Lima and Yusik Kim and A. Said Gurbuz and Michele Dolfi and Miquel Farré and Peter W. J. Staar}, year={2025}, eprint={2503.11576}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2503.11576}, } `` Demo: HF Space