This repository contains GGUF format quantizations (Q4_K_M, Q5_K_M, Q6_K, Q8_0) of Qianfan-OCR, a 4B-parameter end-to-end document intelligence model developed by the Baidu Qianfan Team. It unifies document parsing, layout analysis, and document understanding within a single vision-language architecture.

Model Details

Model Description

Qianfan-OCR is designed to replace traditional multi-stage OCR pipelines. Instead of chaining separate layout detection and text recognition modules, it performs direct image-to-Markdown conversion. It introduces Layout-as-Thought, an optional thinking phase where the model generates structured layout representations before producing the final output.

Developed by: Baidu Qianfan Team
Shared by: Abhiray
Model type: Multimodal Vision-Language Model (VLM)
Text Backbone: Qwen3-4B
Vision Encoder: Qianfan-ViT (AnyResolution up to 4K)
Language(s) (NLP): Multilingual (192 languages supported)
License: Apache-2.0

Model Sources

Repository: Baidu Qianfan-OCR
Paper: Qianfan-OCR: A Unified End-to-End Model for Document Intelligence

Uses

Direct Use

Document Parsing: High-fidelity Image-to-Markdown conversion.
Table/Formula Recognition: Extracting complex tables (merged cells) and LaTeX formulas.
Key Information Extraction (KIE): Structured data extraction from receipts, invoices, and IDs.
Visual Question Answering (DocVQA): Reasoning over charts, graphs, and structured documents.

Out-of-Scope Use

The model is not intended for generating creative fiction or conversational roleplay; it is optimized for high-accuracy document intelligence and visual grounding.

How to Get Started with the Model

1. Requirements

You need both a Quantized Model File (.gguf) and the Vision Projector (qianfan-ocr-mmproj.gguf).

2. Running with Koboldcpp (Recommended)

bash

./koboldcpp \
  --model qianfan-ocr-4b-Q4_K_M.gguf \
  --mmproj qianfan-ocr-mmproj.gguf \
  --usecuda 0 \
  --gpulayers 99 \
  --contextsize 8192 \
  --remotetunnel

Abiray/Qianfan-OCR-GGUF

Model Card for Qianfan-OCR-4B-GGUF