LightOnOCR-3-4B: LightOn's most accurate OCR model. Page to Markdown, bounding boxes, charts to tables. Images work.

LightOnOCR-3-4B

LightOnOCR-3-4B: LightOn’s most accurate OCR model. Page to Markdown, bounding boxes, charts to tables. Images work.

🚀 Overview

GGUF build of lightonai/LightOnOCR-3-4B, LightOn’s most accurate LightOnOCR-3 model (Qwen3.5 vision-language architecture): end-to-end OCR with layout and visual understanding. It ships with its vision projector, so image input works in Ollama.

Prompts: send the image with an empty prompt for plain transcription (Markdown), or with the prompt grounding to get every block with a label and a 0–1000 bounding box, image descriptions and chart data as tables. LightOn notes that other instructions are out of distribution. Recommended sampling: temperature 0.2, top_p 0.9 (set in the Ollama tags).

🎯 Key Features

Most accurate of the LightOnOCR-3 family — LightOn’s recommendation for most OCR tasks
Two modes: plain transcription, or grounding with labels and bounding boxes for every block
Visual understanding: short descriptions for images, charts turned into tables of their data
Tables, receipts, forms, multi-column layouts and math notation
Apache-2.0: research and commercial use

🏷️ Available Versions

Tag	Size (+ 0.67 GB projector)	BPW	Notes
IQ3_M	2.2 GB	3.66	Smallest
IQ4_XS	2.5 GB	4.25	Great quality-size balance
latest / Q4_K_M	2.7 GB	4.85	Recommended
Q5_K_M	3.1 GB	5.68	Higher quality
Q6_K	3.5 GB	6.56	Very high quality
Q8_0	4.5 GB	8.5	Near-lossless

💻 Quick Start

ollama run richardyoung/lightonocr-3-4b
# then drag an image file into the prompt, or send it in the API "images" field

🛠️ Use Cases

Digitizing scanned documents and PDFs locally
Layout-aware extraction with bounding boxes
Chart and table data extraction for RAG

✅ Verified

Tested through Ollama before upload (Q4_K_M and Q8_0) on a synthetic invoice with an empty prompt; Q4_K_M returned: ‘INVOICE 4471 / Date: 2026-09-28 / Widgets x3 ……. \(42.00 / TOTAL: \)86.20’.

🔧 Technical Details

Base Model: lightonai/LightOnOCR-3-4B
Parameters: 4.2B
Context Length: 256K tokens
Quantization: GGUF via llama.cpp with an importance matrix (calibration_datav3); vision projector at F16; converted with --no-mtp
License: Apache-2.0 (inherited from the base model)

🙏 Acknowledgments

Base Model: LightOn
Quantization: llama.cpp

Built & maintained by Richard Young · DeepNeuro