lightonai/LightOnOCR-2-1B

[](https://lighton.ai) [](https://www.linkedin.com/company/lighton/) [](https://x.com/LightOnIO) 📄 [Paper](https://arxiv.org/pdf/2601.14251) | 📝 [Blog](https://huggingface.co/blog/lightonai/lightonocr-2) | 🚀 [Demo](https://huggingface.co/spaces/lightonai/LightOnOCR-2-1B-Demo) | 📊 [Dataset](https://huggingface.co/datasets/lightonai/LightOnOCR-mix-0126) | 📓 [Finetuning](https://colab.research.google.com/drive/1WjbsFJZ4vOAAlKtcCauFLn_evo5UBRNa?usp=sharing) **Best OCR model .** LightOnOCR-2-1B is **[LightOn's](https://lighton.ai)** flagship OCR model, refined with RLVR training for maximum accuracy. We recommend this variant for most OCR tasks. LightOnOCR-2 is an efficient end-to-end 1B-parameter vision-language model for converting documents (PDFs, scans, images) into clean, naturally ordered text without relying on brittle pipelines. This second version is trained on a larger and higher-quality corpus with stronger French, arXiv, and scan coverage, improved LaTeX handling, and cleaner normalization. LightOnOCR-2 achieves state-of-the-art performance on OlmOCR-Bench while being ~9× smaller and significantly faster than competing approaches. * ⚡ **Speed:** 3.3× faster than Chandra OCR, 1.7× faster than OlmOCR, 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 * 📍 **Image detection:** Predicts bounding boxes for embedded images (bbox variants) 📄 **[Paper]( https://arxiv.org/pdf/2601.14251)** | 📝 **[Blog Post](https://huggingface.co/blog/lightonai/lightonocr-2)** | 🚀 **[Demo](https://huggingface.co/spaces/lightonai/LightOnOCR-2-1B-Demo)** | 📊 **[Dataset](https://huggingface.co/datasets/lightonai/LightOnOCR-mix-0126)** | 📊 **[BBox Dataset](https://huggingface.co/datasets/lightonai/LightOnOCR-bbox-mix-0126)** | 📓 **[Finetuning Notebook](https://colab.research.google.com/drive/1WjbsFJZ4vOAAlKtcCauFLn_evo5UBRNa?usp=sharing)** | **[LightOn blog entry](https://www.lighton.ai/lighton-blogs/lighton-opens-a-new-field-for-ai-with-lightonocr-2-document-intelligence)** *See the [paper](https://arxiv.org/pdf/2601.14251) for full benchmark details and methodology.* > **Note:** LightOnOCR-2 is avaible in latest transformers release starting from v5. uv pip install transformers # => 5.0.0 uv pip install pillow pypdfium2 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", torch_dtype=dtype).to(device) processor = LightOnOcrProcessor.from_pretrained("lightonai/LightOnOCR-2-1B") 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) vllm serve lightonai/LightOnOCR-2-1B \ --limit-mm-per-prompt '{"image": 1}' --mm-processor-cache-gb 0 --no-enable-prefix-caching import base64 import requests import pypdfium2 as pdfium import io ENDPOINT = "http://localhost:8000/v1/chat/completions" MODEL = "lightonai/LightOnOCR-2-1B" 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) * Render PDFs at 200 DPI to images using a target longest dimension of **1540px** * Maintain aspect ratio to preserve text geometry LightOnOCR-2 is fully differentiable and supports: * LoRA fine-tuning * Domain adaptation (receipts, scientific articles, forms, etc.) * Multilingual fine-tuning with task-specific corpora For fine-tuning, we recommend starting with the **[LightOnOCR-2-1B-base](https://huggingface.co/lightonai/LightOnOCR-2-1B-base)** variant. Apache License 2.0 The project received funding from the BPI Scribe project. @misc{lightonocr2_2026, title = {LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR}, author = {Said Taghadouini and Adrien Cavaill\`{e}s and Baptiste Aubertin}, year = {2026}, howpublished = {\url{https://arxiv.org/abs/2601.14251}} } [](https://huggingface.co/lightonai/LightOnOCR-2-1B) [](https://huggingface.co/lightonai)