yangzhou0011/layoutgrok-qwen3-vl-cp500

Fine-tuned Qwen3-VL model for converting complex scanned PDFs, multi-column papers, and tabular reports into Markdown + layout JSON.

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LayoutGrok Qwen3-VL CP500

LayoutGrok Qwen3-VL CP500 is a document-vision model fine-tuned from Qwen/Qwen3-VL-8B-Instruct using LoRA SFT. This release uses checkpoint-500 and is optimized for page-level document transcription, reading-order preservation, and lightweight layout structuring.

The model is designed to convert visually complex document pages—such as scanned PDFs, dense tables, multi-column articles, and report pages—into structured, machine-readable outputs. Its primary use case is preparing document content for downstream search, indexing, RAG, data extraction, and human review workflows.

LayoutGrok does not guarantee error-free OCR or eliminate all downstream hallucinations. It is intended to reduce layout-induced parsing errors by grounding transcription in the original page image.

Core Capabilities

  • Scanned PDF Transcription Transcribes document page images, including degraded scans, low-resolution pages, and multi-generation copies.

  • Dense Table Reconstruction Preserves table structure where possible, including compact financial tables, balance sheets, audit files, and borderless layouts.

  • Multi-Column Reading Order Follows natural reading order for academic papers, legal documents, reports, and multi-section pages.

  • Chart and Figure Context Extraction Extracts visible text from figures, captions, legends, and chart-heavy report pages.

  • Structured Output Modes Supports clean Markdown transcription and optional compact Layout JSON for downstream layout-aware processing.

Intended Use

LayoutGrok is suitable for:

  • OCR preprocessing for document search and RAG systems
  • Markdown conversion of scanned or visually complex PDFs
  • Table-aware document transcription
  • Dataset construction for document understanding tasks
  • Internal document parsing pipelines requiring local or private deployment

It is not intended to be used as the sole source of truth for high-stakes financial, legal, clinical, or compliance decisions without human verification.

Quick Start

The model accepts a document page image and a text prompt, then returns structured transcription output.

Markdown + Compact Layout JSON

Use this prompt when layout structure matters:

Transcribe this document page into clean Markdown. Preserve tables, equations, and reading order. Then output compact Layout JSON.

Markdown Only

Use this prompt for standard OCR-style transcription or RAG preprocessing:

Transcribe this document page into clean Markdown.

Replicate API Usage

Model Endpoint

yangzhou0011/layoutgrok-qwen3-vl-cp500

Input Example

{
  "image": "https://example.com/your-document-page.jpg",
  "prompt": "Transcribe this document page into clean Markdown. Preserve tables, equations, and reading order. Then output compact Layout JSON.",
  "max_new_tokens": 1024,
  "temperature": 0
}

Parameter Notes

  • temperature: 0 is recommended for deterministic transcription.
  • Increase max_new_tokens for dense tables, long pages, or pages with extensive Layout JSON output.
  • For best results, provide a single page image rather than a full multi-page PDF.

Training Summary

Item Description
Base model Qwen/Qwen3-VL-8B-Instruct
Fine-tuning method LoRA supervised fine-tuning
Selected checkpoint checkpoint-500
Task focus Document-page transcription, reading-order recovery, table preservation, and compact layout structuring
Dataset profile Curated multi-domain document page images with structured transcription targets generated through multimodal teacher-assisted annotation and filtering

Enterprise and Private Deployment

For organizations that process sensitive documents—such as financial statements, patient records, legal audits, proprietary research, or internal compliance files—LayoutGrok can be packaged for private local deployment.

Available Deployment Options

  • Local Model Package Merged FP16 weights and optional quantized variants for local inference.

  • Private Inference Container Docker-based deployment with an OpenAI-compatible local API using a high-throughput inference backend such as vLLM or SGLang.

  • Custom Fine-Tuning Domain adaptation on private document templates, internal report formats, or specialized OCR/layout tasks.

  • Commercial Licensing One-time or custom licensing options for internal production use.

For enterprise licensing, private deployment, or custom fine-tuning inquiries, contact:

joeytech.studio@gmail.com

Limitations

  • Image Quality Severe blur, skew, low contrast, occlusion, or heavy compression may cause transcription errors.

  • Complex Tables Deeply nested, irregular, merged-cell, or highly asymmetric tables may require post-processing or manual verification.

  • Charts and Figures The model can extract visible chart text and captions, but numerical interpretation of plots may be unreliable without additional validation.

  • Long Documents This model operates on page images. Multi-page document consistency, cross-page references, and global document reasoning should be handled by a separate pipeline.

  • High-Stakes Use Outputs used in financial, legal, medical, or regulatory workflows should be reviewed by qualified humans before use.

License

This model is based on Qwen/Qwen3-VL-8B-Instruct, which is distributed under the Apache 2.0 license.

This release should be used in accordance with the upstream model license and the license terms of any additional dependencies.

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