SmolDocling-256M-preview-GGUF

<span style="color: #7FFF7F;">SmolDocling-256M-preview GGUF Models</span>

<span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using llama.cpp at commit 6adc3c3e.

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<!--Begin Original Model Card--> <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;"> <div> <h3>SmolDocling-256M-preview</h3> <p>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 <strong>DoclingDocuments</strong>.</p> </div> </div>

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.):

<details> <summary>📄 Single page image inference using Tranformers 🤖</summary>

# 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())

</details> <details> <summary> 🚀 Fast Batch Inference Using VLLM</summary>

# 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"<|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"))])

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")

</details> <details> <summary> ONNX Inference</summary>

# 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("<end_of_utterance>")

# 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())

</details>

💻 Local inference on Apple Silicon with MLX: see here

DocTags

<img src="https://huggingface.co/ds4sd/SmolDocling-256M-preview/resolve/main/assets/doctags_v2.png" width="800" height="auto" alt="Image description"> 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

<table> <tr> <td><b>Description</b></td> <td><b>Instruction</b></td> <td><b>Comment</b></td> </tr> <tr> <td><b>Full conversion</b></td> <td>Convert this page to docling.</td> <td>DocTags represetation</td> </tr> <tr> <td><b>Chart</b></td> <td>Convert chart to table.</td> <td>(e.g., &lt;chart&gt;)</td> </tr> <tr> <td><b>Formula</b></td> <td>Convert formula to LaTeX.</td> <td>(e.g., &lt;formula&gt;)</td> </tr> <tr> <td><b>Code</b></td> <td>Convert code to text.</td> <td>(e.g., &lt;code&gt;)</td> </tr> <tr> <td><b>Table</b></td> <td>Convert table to OTSL.</td> <td>(e.g., &lt;otsl&gt;) OTSL: <a href="https://arxiv.org/pdf/2305.03393">Lysak et al., 2023</a></td> </tr> <tr> <td rowspan=4><b>Actions and Pipelines</b></td> <td>OCR the text in a specific location: &lt;loc_155&gt;&lt;loc_233&gt;&lt;loc_206&gt;&lt;loc_237&gt;</td> <td></td> </tr> <tr> <td>Identify element at: &lt;loc_247&gt;&lt;loc_482&gt;&lt;10c_252&gt;&lt;loc_486&gt;</td> <td></td> </tr> <tr> <td>Find all 'text' elements on the page, retrieve all section headers.</td> <td></td> </tr> <tr> <td>Detect footer elements on the page.</td> <td></td> </tr> </table>

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

<!--End Original Model Card-->

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<span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
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  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
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Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
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🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

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1. "Give me info on my websites SSL certificate"
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4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

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