# PP-DocLayoutV3_safetensors by PaddlePaddle: Open Model Source: https://savrn.com/models/pp-doclayoutv3-safetensors Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## 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](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 24, 2026. [PP-DocLayoutV3_safetensors on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/pp-doclayoutv3-safetensors/gpus) ## 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. [![repo](https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf)](https://github.com/PaddlePaddle/PaddleOCR) 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[![Discord](https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white)](https://discord.gg/JPmZXDsEEK) [![X](https://img.shields.io/badge/X-PaddlePaddle-6080F0)](https://x.com/PaddlePaddle) [![License](https://img.shields.io/badge/license-Apache_2.0-green)](./LICENSE) ** [Official Website](https://www.paddleocr.com)** | ** [Technical Report](https://arxiv.org/abs/2606.23344)** ### Introduction This is the model weights for PP-DocLayoutv3 in safetensors format. Get PaddlePaddle weights at [PP-DocLayoutV3](https://savrn.com/models/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)](https://savrn.com/models/pp-doclayoutv3-safetensors/card) ## 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](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3_safetensors) Released by PaddlePaddle through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [PaddlePaddle/PP-DocLayoutV3](https://savrn.com/models/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. ## Similar Models Model · Object detection ### [rf-detr-large](https://savrn.com/models/rf-detr-large) [Roboflow](https://savrn.com/model-publishers/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. 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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 [View model](https://savrn.com/models/table-transformer-structure-recognition-v1-1-all) ## PaddlePaddle [All models and datasets](https://savrn.com/model-publishers/paddlepaddle) ## Versions - [e4f489dc5365](https://savrn.com/models/pp-doclayoutv3-safetensors/versions/e4f489dc5365) · current 2026-10-10 - [97d101e6db26](https://savrn.com/models/pp-doclayoutv3-safetensors/versions/97d101e6db26) 2026-09-30 ## Explore More - [All object detection models](https://savrn.com/models/tasks/object-detection) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-01. - [Hugging Face record](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3_safetensors) - [How the hub is built](https://savrn.com/model-hub/methodology)