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.
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
).
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