docling-ibm-models
This package contains the AI models used by the Docling PDF conversion package
Decision gist · record as of 2026-08-14
Yes, if you are building a document processing pipeline that needs table and layout detection. The package is actively maintained, has no known vulnerabilities, and integrates with the broader ecosystem. The MIT license poses no barrier. Install friction is low, though torch and transformers are substantial dependencies—acceptable for ML workloads but not for lightweight applications. Suitable for production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- torch and torchvision are heavy dependencies; accelerate is optional but recommended for inference speed.
- Low friction install with a pure-Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions—suitable for most projects.
last release 2026-08-11 (3 days) · last repo commit 2026-08-11 · 208 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,514,585 downloads/mo, #2,595 on PyPI
Alternatives
Verify before relying
pip install "docling-ibm-models[opencv-python-headless]"
from docling_ibm_models import LayoutPredictor
predictor = LayoutPredictor.from_pretrained()
result = predictor.predict(image)- Whether pre-trained model weights are automatically downloaded or require manual setup beyond huggingface_hub.
- Performance characteristics (inference latency, memory footprint) for typical document sizes.
- Whether CPU-only inference is supported or CUDA is required.
What it is and what it does
This package provides pre-trained neural networks for understanding document structure, with two main components: TableFormer, which identifies table boundaries and cell structure in document images, and a Layout model that detects tables and other page elements. It wraps transformer-based models trained on datasets including PubTabNet, FinTabNet, and TableBank, designed to integrate with document conversion pipelines.
The package depends on torch, transformers, and huggingface_hub to load and run inference. You install it with an optional choice between opencv-python and opencv-python-headless, then instantiate predictors and call them on image data. Models are downloaded from huggingface_hub on first use.
Use it for
- Extract table structure and cell locations from scanned PDF pages for downstream OCR or data extraction.
- Detect and segment page layout regions to guide document parsing and conversion.
- Batch process document images to identify which pages contain tables before specialized recognition.
- Build a document conversion pipeline that preserves table formatting when converting PDFs to structured formats.
- Analyze financial or scientific documents where accurate table extraction is critical for data integrity.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building a document processing pipeline that needs table and layout detection.
The package is actively maintained, has no known vulnerabilities, and integrates with the broader ecosystem. The MIT license poses no barrier. Install friction is low, though torch and transformers are substantial dependencies—acceptable for ML workloads but not for lightweight applications. Suitable for production use.
Install
docling-ibm-models on PyPI
Before you install
Low friction install with a pure-Python wheel. Active maintenance with a release 3 days old. Requires 13 runtime dependencies including torch, transformers, and huggingface_hub, which are substantial but standard for ML inference workloads.
Requires Python 3.10 or later. torch and torchvision are heavy dependencies; accelerate is optional but recommended for inference speed.
License in practice
MIT license permits commercial and private use with minimal restrictions—suitable for most projects.
Quickstart
pip install "docling-ibm-models[opencv-python-headless]"
from docling_ibm_models import LayoutPredictor
predictor = LayoutPredictor.from_pretrained()
result = predictor.predict(image)Verify before relying
- Whether pre-trained model weights are automatically downloaded or require manual setup beyond huggingface_hub.
- Performance characteristics (inference latency, memory footprint) for typical document sizes.
- Whether CPU-only inference is supported or CUDA is required.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 13 packagestorchtorchvisionjsonlinesPillowtqdmhuggingface_hubsafetensorspydanticdocling-coretransformersnumpyrtreeaccelerate |
| Maintenance | Actively maintained 3 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,514,585 / month, #2,595 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: docling_ibm_models-3.14.0-py3-none-any.whl
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See also docling · surya-ocr · marker-pdf · docling-core · docling-slim · unstructured-inference · pdfplumber · pymupdf-layout · docling-parse · layoutparser