PP-DocLayoutV3_safetensors by PaddlePaddle: Open Model

Runs On

What it takes to serve PP-DocLayoutV3_safetensors (33M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

Precision	Weights	Memory needed	Cheapest setup	Per hour	Also fits
16-bit	0.1 GB	0.1 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
8-bit	0.0 GB	0.0 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
4-bit	0.0 GB	0.0 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 13, 2026.

PP-DocLayoutV3_safetensors on every accelerator the SAVRN Index prices, at every precision

SAVRN's Notes on PP-DocLayoutV3_safetensors

Thirty-three million parameters doing layout detection — finding the blocks on a page before OCR reads them — for PaddleOCR-VL 1.5 and 1.6, GLM-OCR and Xiaomi-OCR-0. At 16-bit it needs 0.1 GB, and the whole repository is one 133.3 MB safetensors file. It is plumbing: run it on the same card as the OCR model it feeds, not on its own MI300X at $1.85 an hour.

The name tells the story: this is the safetensors conversion of PaddlePaddle/PP-DocLayoutV3, published for stacks that load transformers-format weights rather than the original Paddle files. Apache 2.0 permits commercial use. It is young — published January 20, 2026, updated September 30, 2026, described in arXiv 2606.23344 — so pin the revision you validate. At 1.1M downloads a month it is already the default layout front-end for several OCR pipelines.

SAVRN Research

Model Card

By PaddlePaddle, published under apache-2.0, revision e4f489dc5365.

Unified Layout Module for PaddleOCR-VL 1.5/1.6 & GLM-OCR & & Xiaomi-OCR-0.

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Introduction

This is the model weights for PP-DocLayoutv3 in safetensors format. Get PaddlePaddle weights at PP-DocLayoutV3

PP-DocLayoutV3 is specifically engineered to handle non-planar document images. It can directly predict multi-point bounding boxes for layout elements—as opposed to standard two-point boxes—and determine logical reading orders for skewed and curved surfaces within a single forward pass, significantly reducing cascading errors. This model is an essential component of PaddleOCR-VL-1.5, providing crucial layout analysis for the high-precision parsing of various real-world documents in PaddleOCR-VL.

This work has been accepted to ECCV 2026!

Model Architecture

Model Usage

Read the full model card (274 words)

Configuration

Architecture: PPDocLayoutV3ForObjectDetection
Stored precision: float32
Model type: pp_doclayout_v3

Identity and Version

Repository: PaddlePaddle/PP-DocLayoutV3_safetensors
Publisher: PaddlePaddle
Task: Object detection
Modality: Image
Library: transformers
Parameters: 33M parameters
Languages: en, zh
Revision: e4f489dc536556fc1be02b973ba06756e0d4a2ac
First published: 2026-01-20
Last updated: 2026-09-30

Files and Weights

6 files, 133.3 MB in total. The weights are 1 file totalling 133.3 MB in safetensors.

Weights1 file · 133.3 MB

Configuration3 files · 4.5 KB

Documentation1 file · 13.2 KB

Repository1 file · 1.5 KB

Every file

File	Type	Size	SHA-256
model.safetensors	Weights	133.3 MB	5ea422c6cc5f
config.json	Configuration	2.5 KB	—
inference.yml	Configuration	1.5 KB	—
preprocessor_config.json	Configuration	575 B	—
README.md	Documentation	13.2 KB	—
.gitattributes	Repository	1.5 KB	—

License and Download

License: apache-2.0
Access: Open weights, no gate
Download size: 133.3 MB

Download from PaddlePaddle

Released by PaddlePaddle through its official repository on Hugging Face. Read the license.

Built From

Derived from PaddlePaddle/PP-DocLayoutV3
Described by arXiv:2606.23344

Memory Requirements

Precision	Weights in memory
As published	133.3 MB
16-bit	0.1 GB
8-bit	0.0 GB
4-bit	0.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About PP-DocLayoutV3_safetensors

How much GPU memory does PP-DocLayoutV3_safetensors need?

About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (33M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run PP-DocLayoutV3_safetensors on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use PP-DocLayoutV3_safetensors commercially?

Yes. PP-DocLayoutV3_safetensors is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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rf-detr-large

Roboflow

RF-DETR is a real-time detection transformer family introduced in RF-DETR: Neural Architecture Search for Real-Time Detection Transformers by Robinson et al. and integrated in Transformers via PR #36895. RF-DETR is an end-to-end object detection model that combines ideas from LW-DETR and Deformable DETR: a DINOv2-with-registers style ViT backbone (with an RF-DETR windowing pattern for efficient attention), a multi-scale projector between encoder and decoder, and a multi-scale deformable DETR decoder for fast convergence and strong accuracy–latency tradeoffs. You can use the raw model for object detection. See the model hub to look for all available RF-DETR models. Here is how to use this…

Open weights apache-2.0 34M parameters transformers

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Model · Object detection

rf-detr-base

Roboflow

RF-DETR is a real-time detection transformer family introduced in RF-DETR: Neural Architecture Search for Real-Time Detection Transformers by Robinson et al. and integrated in Transformers via PR #36895. RF-DETR is an end-to-end object detection model that combines ideas from LW-DETR and Deformable DETR: a DINOv2-with-registers style ViT backbone (with an RF-DETR windowing pattern for efficient attention), a multi-scale projector between encoder and decoder, and a multi-scale deformable DETR decoder for fast convergence and strong accuracy–latency tradeoffs. You can use the raw model for object detection. See the model hub to look for all available RF-DETR models. Here is how to use this…

Open weights apache-2.0 32M parameters transformers

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Model · Object detection

yolos-small

HUST Vision Lab

YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team. YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). The model is trained using a "bipartite matching loss": one compares the…

Open weights apache-2.0 31M parameters transformers

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Model · Object detection

table-transformer-structure-recognition

Microsoft

Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Smock et al. and first released in this repository. Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. The Table Transformer is equivalent to DETR, a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention. You can use the raw model for detecting the structure (like rows, columns) in tables.…

Open weights mit 29M parameters 1,024 tokens transformers

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Model · Object detection

table-transformer-structure-recognition-v1.1-all

Microsoft

Table Transformer (TATR) model trained on PubTables1M and FinTabNet.c. It was introduced in the paper Aligning benchmark datasets for table structure recognition by Smock et al. and first released in this repository. Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. The Table Transformer is equivalent to DETR, a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention. You can use the raw model for detecting tables in documents. See the documentation for more…

Open weights mit 29M parameters transformers

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Model · Object detection

table-transformer-detection

Microsoft

Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Smock et al. and first released in this repository. Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. The Table Transformer is equivalent to DETR, a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention. You can use the raw model for detecting tables in documents. See the documentation for…

Open weights mit 29M parameters 1,024 tokens transformers

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