lightonai/LightOnOCR-1B-1025

> [!NOTE] > 🚀 **LightOnOCR-2 is now available and state-of-the-art on [OlmOCR-bench](https://huggingface.co/datasets/allenai/olmOCR-bench)**, with new image detection variants! > Check it out here: **[lightonai/LightOnOCR-2-1B](https://huggingface.co/lightonai/LightOnOCR-2-1B)** Full BF16 version of the model. We recommend this variant for inference and further fine-tuning. **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. [](https://colab.research.google.com/#fileId=https%3A//huggingface.co/lightonai/LightOnOCR-1B-1025/blob/main/notebook.ipynb) 📄 **[Paper]( https://arxiv.org/pdf/2601.14251)** | 📝 **[Read the full blog post](https://huggingface.co/blog/lightonai/lightonocr/)** | 🚀 **[Try the demo](https://huggingface.co/spaces/lightonai/LightOnOCR-1B-Demo-zero)** | 📓 **[Finetuning notebook](https://colab.research.google.com/drive/1WjbsFJZ4vOAAlKtcCauFLn_evo5UBRNa?usp=sharing)** **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 **LightOnOCR** combines a Vision Transformer encoder(Pixtral-based) with a lightweight text decoder(Qwen3-based) distilled from high-quality open VLMs. It is optimized for document parsing tasks, producing accurate, layout-aware text extraction from high-resolution pages. All benchmarks evaluated using **vLLM** on the Olmo-Bench. [2025/11/24] 🚀 LightOnOCR is now officially supported in vLLM v0.11.1 🚀 uv venv --python 3.12 --seed source .venv/bin/activate uv pip install vllm==0.11.2 uv pip install pypdfium2 pillow requests **Start Server** vllm serve lightonai/LightOnOCR-1B-1025 \ --limit-mm-per-prompt '{"image": 1}' --mm-processor-cache-gb 0 --no-enable-prefix-caching **PDF Inference** import base64 import requests import pypdfium2 as pdfium import io ENDPOINT = "http://localhost:8000/v1/chat/completions" MODEL = "lightonai/LightOnOCR-1B-1025" pdf_url = "https://arxiv.org/pdf/2412.13663" pdf_data = requests.get(pdf_url).content pdf = pdfium.PdfDocument(pdf_data) page = pdf[0] pil_image = page.render(scale=2.77).to_pil() buffer = io.BytesIO() pil_image.save(buffer, format="PNG") image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8') payload = { "model": MODEL, "messages": [{ "role": "user", "content": [{ "type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"} }] }], "max_tokens": 4096, "temperature": 0.2, "top_p": 0.9, } response = requests.post(ENDPOINT, json=payload) text = response.json()['choices'][0]['message']['content'] print(text) > **Note:** LightOnOCR-2 requires transformers installed from source (not yet in a stable release). uv pip install git+https://github.com/huggingface/transformers import torch from transformers import LightOnOcrForConditionalGeneration, LightOnOcrProcessor device = "mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu" dtype = torch.float32 if device == "mps" else torch.bfloat16 model = LightOnOcrForConditionalGeneration.from_pretrained("lightonai/LightOnOCR-2-1B-base", torch_dtype=dtype).to(device) processor = LightOnOcrProcessor.from_pretrained("lightonai/LightOnOCR-2-1B-base") url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ocr/resolve/main/SROIE-receipt.jpeg" conversation = [{"role": "user", "content": [{"type": "image", "url": url}]}] inputs = processor.apply_chat_template( conversation, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ) inputs = {k: v.to(device=device, dtype=dtype) if v.is_floating_point() else v.to(device) for k, v in inputs.items()} output_ids = model.generate(**inputs, max_new_tokens=1024) generated_ids = output_ids[0, inputs["input_ids"].shape[1]:] output_text = processor.decode(generated_ids, skip_special_tokens=True) print(output_text) * Render PDFs to **PNG** or **JPEG** at a target longest dimension of **1540px** * Maintain aspect ratio to preserve text geometry * Use one image per page; batching supported by vLLM **Transformers integration is coming soon for training and inference.** LightOnOCR is fully differentiable and supports: * LoRA fine-tuning * Domain adaptation (receipts, scientific articles, forms, etc.) * Multilingual fine-tuning with task-specific corpora 📓 **[Finetuning notebook](https://colab.research.google.com/drive/1WjbsFJZ4vOAAlKtcCauFLn_evo5UBRNa?usp=sharing)** 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. Apache License 2.0 The project received funding from the BPI Scribe project. @misc{lightonocr2025, title = {LightOnOCR-1B: End-to-End and Efficient Domain-Specific Vision-Language Models for OCR}, author = {Said Taghadouini and Baptiste Aubertin and Adrien Cavaillès}, year = {2025}, howpublished = {\url{https://huggingface.co/blog/lightonai/lightonocr}} }