lightonai
/

LightOnOCR-1B-1025

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 ---
+license: apache-2.0
+language:
+- en
+- fr
+- de
+- es
+- it
+- nl
+- pt
+- sv
+- da
+base_model: lightonai/LightOnOCR-1B-1025
+library_name: vllm
+tags:
+- ocr
+- document-understanding
+- vision-language
+- pdf
+- tables
+- forms
 ---
+# LightOnOCR-1B-1025
+Full BF16 version of the model. We recommend this variant for further fine-tuning or research use.
+**LightOnOCR-1B** is a compact, end-to-end vision–language model for Optical Character Recognition (OCR) and document understanding. It achieves state-of-the-art accuracy in its weight class while being several times faster and cheaper than larger general-purpose VLMs.
+📝 **[Read the full blog post](https://huggingface.co/blog/lightonai/lightonocr/)**
+**Highlights**
+* ⚡ **Speed:** 5× faster than dots.ocr, 2× faster than PaddleOCR-VL-0.9B, 1.73× faster than DeepSeekOCR
+* 💸 **Efficiency:** Processes 5.71 pages/s on a single H100 (~493k pages/day) for **<$0.01 per 1,000 pages**
+* 🧠 **End-to-End:** Fully differentiable, no external OCR pipeline
+* 🧾 **Versatile:** Handles tables, receipts, forms, multi-column layouts, and math notation
+* 🌍 **Compact variants:** 32k and 16k vocab options for European languages
+---
+## Model Overview
+**LightOnOCR** combines a high-performance Vision Transformer encoder with a lightweight text decoder distilled from high-quality open VLMs.
+It is optimized for document parsing tasks, producing accurate, layout-aware text extraction from high-resolution pages.
+* **Architecture:** ViT-based vision encoder + lean text decoder
+* **Training Objective:** End-to-end OCR + layout-aware text generation
+* **Performance:** Stable under rotation, skew, and low-contrast scans
+---
+## Benchmarks
+| Model              | ArXiv | Old Scans | Math | Tables | Multi-Column | Tiny Text | Base | Overall |
+| :----------------- | :---: | :-------: | :--: | :----: | :----------: | :-------: | :--: | :-----: |
+| [LightOnOCR-1B-1025](https://huggingface.co/lightonai/LightOnOCR-1B-1025) (151k vocab) | 81.4 | 71.6 | 76.4 | 35.2 | 80.0 | 88.7 | 99.5 | **76.1** |
+| [LightOnOCR-1B-32k](https://huggingface.co/lightonai/LightOnOCR-0.9B-32k-1025) (32k vocab) | 80.6 | 66.2 | 73.5 | 33.5 | 71.2 | 87.6 | 99.5 | **73.1** |
+| [LightOnOCR-1B-16k](https://huggingface.co/lightonai/LightOnOCR-0.9B-16k-1025) (16k vocab) | 82.3 | 72.9 | 75.3 | 33.5 | 78.6 | 85.1 | 99.8 | **75.4** |
+All benchmarks evaluated using standardized LightOnOCR inference via **vLLM** on the LightOn internal OCR test suite (2025-10).
+---
+## Installation
+```bash
+uv venv --python 3.12 --seed
+source .venv/bin/activate
+uv pip install -U vllm \
+    --torch-backend=auto \
+    --extra-index-url https://wheels.vllm.ai/nightly \
+    --prerelease=allow
+uv pip install pypdfium2 pillow requests
+```
+## Start Server
+```bash
+vllm serve lightonai/LightOnOCR-0.9B-32k-1025 \
+    --limit-mm-per-prompt '{"image": 1}' \
+    --async-scheduling
+```
+## PDF Inference
+```python
+import base64
+import requests
+import pypdfium2 as pdfium
+import io
+ENDPOINT = "http://localhost:8000/v1/chat/completions"
+MODEL = "lightonai/LightOnOCR-0.9B-32k-1025"
+# Download PDF from arXiv
+pdf_url = "https://arxiv.org/pdf/2412.13663"
+pdf_data = requests.get(pdf_url).content
+# Open PDF and convert first page to image
+pdf = pdfium.PdfDocument(pdf_data)
+page = pdf[0]
+# Render at 300 DPI (scale factor = 300/72 ≈ 4.17)
+pil_image = page.render(scale=4.17).to_pil()
+# Convert to base64
+buffer = io.BytesIO()
+pil_image.save(buffer, format="PNG")
+image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
+# Make request
+payload = {
+    "model": MODEL,
+    "messages": [{
+        "role": "user",
+        "content": [{
+            "type": "image_url",
+            "image_url": {"url": f"data:image/png;base64,{image_base64}"}
+        }]
+    }],
+    "max_tokens": 6500,
+    "temperature": 0.2,
+    "top_p": 0.9,
+}
+response = requests.post(ENDPOINT, json=payload)
+text = response.json()['choices'][0]['message']['content']
+print(text)
+```
+---
+## Rendering and Preprocessing Tips
+* Render PDFs to **PNG** or **JPEG** at a target longest dimension of **1280–1300 px**
+* Maintain aspect ratio to preserve text geometry
+* LightOnOCR is robust to moderate skew; heavy rotation correction is optional
+* Use one image per page; batching supported by vLLM
+---
+## Variants
+| Variant                                                                            | Description                                   |
+| :--------------------------------------------------------------------------------- | :-------------------------------------------- |
+| **[LightOnOCR-1B-1025](https://huggingface.co/lightonai/LightOnOCR-1B-1025)**      | Full multilingual model (default)             |
+| **[LightOnOCR-1B-32k](https://huggingface.co/lightonai/LightOnOCR-0.9B-32k-1025)** | Compact vocabulary optimized for EU languages |
+| **[LightOnOCR-1B-16k](https://huggingface.co/lightonai/LightOnOCR-0.9B-16k-1025)** | Ultra-fast variant for Latin scripts          |
+---
+## Fine-tuning
+**Transformers integration is coming soon for training.**
+LightOnOCR is fully differentiable and supports:
+* LoRA / QLoRA fine-tuning
+* Domain adaptation (receipts, scientific articles, forms, etc.)
+* Multilingual fine-tuning with task-specific corpora
+Example fine-tuning configurations will be released alongside the dataset.
+---
+## Data
+Trained on a diverse large-scale PDF corpus covering:
+* Scientific papers, books, receipts, invoices, tables, forms, and handwritten text
+* Multiple languages (Latin alphabet dominant)
+* Real and synthetic document scans
+The dataset will be released under an open license.
+---
+## License
+Apache License 2.0
+---
+## Citation
+```
+@misc{lightonocr2025,
+  title        = {LightOnOCR-1B: End-to-End and Efficient Domain-Specific Vision-Language Models for OCR},
+  author       = {LightOn},
+  year         = {2025},
+  howpublished = {\url{https://huggingface.co/lightonai/LightOnOCR-1B-1025}}
+}
+```