LightOnOCR-2-1B API & Inference Endpoint

About LightOnOCR-2

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.

Highlights

⚡ 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)

Model Variants

Table with columns: Variant, Description
Variant	Description
LightOnOCR-2-1B	Best OCR model
LightOnOCR-2-1B-base	Base model, ideal for fine-tuning
LightOnOCR-2-1B-bbox	Best model with image bounding boxes
LightOnOCR-2-1B-bbox-base	Base bbox model, ideal for fine-tuning

Benchmarks

See the paper for full benchmark details and methodology.

Usage with Transformers

Note: LightOnOCR-2 is avaible in latest transformers release starting from v5.

bash

uv pip install transformers # => 5.0.0uv pip install pillow pypdfium2

python

import torchfrom 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)

Usage with vLLM

bash

vllm serve lightonai/LightOnOCR-2-1B \    --limit-mm-per-prompt '{"image": 1}' --mm-processor-cache-gb 0 --no-enable-prefix-caching

python

import base64import requestsimport pypdfium2 as pdfiumimport io ENDPOINT = "http://localhost:8000/v1/chat/completions"MODEL = "lightonai/LightOnOCR-2-1B" # Download PDF from arXivpdf_url = "https://arxiv.org/pdf/2412.13663"pdf_data = requests.get(pdf_url).content # Open PDF and convert first page to imagepdf = pdfium.PdfDocument(pdf_data)page = pdf[0]# Render at 200 DPI (scale factor = 200/72 ≈ 2.77)pil_image = page.render(scale=2.77).to_pil() # Convert to base64buffer = io.BytesIO()pil_image.save(buffer, format="PNG")image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8') # Make requestpayload = {    "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)

Rendering and Preprocessing Tips

Render PDFs at 200 DPI to images using a target longest dimension of 1540px
Maintain aspect ratio to preserve text geometry

Fine-tuning

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 variant.

License

Apache License 2.0

Acknowlegments

The project received funding from the BPI Scribe project.

Citation

bibtex

@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}}}

About LightOnOCR-2

Highlights

⚡ 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)

Model Variants

Table with columns: Variant, Description
Variant	Description
LightOnOCR-2-1B	Best OCR model
LightOnOCR-2-1B-base	Base model, ideal for fine-tuning
LightOnOCR-2-1B-bbox	Best model with image bounding boxes
LightOnOCR-2-1B-bbox-base	Base bbox model, ideal for fine-tuning

Benchmarks

See the paper for full benchmark details and methodology.

Usage with Transformers

Note: LightOnOCR-2 is avaible in latest transformers release starting from v5.

bash

uv pip install transformers # => 5.0.0uv pip install pillow pypdfium2

python

import torchfrom 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)

import torchfrom 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)

Usage with vLLM

bash

vllm serve lightonai/LightOnOCR-2-1B \    --limit-mm-per-prompt '{"image": 1}' --mm-processor-cache-gb 0 --no-enable-prefix-caching

python

import base64import requestsimport pypdfium2 as pdfiumimport io ENDPOINT = "http://localhost:8000/v1/chat/completions"MODEL = "lightonai/LightOnOCR-2-1B" # Download PDF from arXivpdf_url = "https://arxiv.org/pdf/2412.13663"pdf_data = requests.get(pdf_url).content # Open PDF and convert first page to imagepdf = pdfium.PdfDocument(pdf_data)page = pdf[0]# Render at 200 DPI (scale factor = 200/72 ≈ 2.77)pil_image = page.render(scale=2.77).to_pil() # Convert to base64buffer = io.BytesIO()pil_image.save(buffer, format="PNG")image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8') # Make requestpayload = {    "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)

import base64import requestsimport pypdfium2 as pdfiumimport io ENDPOINT = "http://localhost:8000/v1/chat/completions"MODEL = "lightonai/LightOnOCR-2-1B" # Download PDF from arXivpdf_url = "https://arxiv.org/pdf/2412.13663"pdf_data = requests.get(pdf_url).content # Open PDF and convert first page to imagepdf = pdfium.PdfDocument(pdf_data)page = pdf[0]# Render at 200 DPI (scale factor = 200/72 ≈ 2.77)pil_image = page.render(scale=2.77).to_pil() # Convert to base64buffer = io.BytesIO()pil_image.save(buffer, format="PNG")image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8') # Make requestpayload = {    "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)

Rendering and Preprocessing Tips

Render PDFs at 200 DPI to images using a target longest dimension of 1540px
Maintain aspect ratio to preserve text geometry

Fine-tuning

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 variant.

License

Apache License 2.0

Acknowlegments

The project received funding from the BPI Scribe project.

Citation

bibtex

@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}}}

@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}}}

LightOnOCR-2-1B

README

About LightOnOCR-2

Highlights

Model Variants

Benchmarks

Usage with Transformers

Usage with vLLM

Rendering and Preprocessing Tips

Fine-tuning

License

Acknowlegments

Citation

Explore FriendliAI today

README

About LightOnOCR-2

Highlights

Model Variants

Benchmarks

Usage with Transformers

Usage with vLLM

Rendering and Preprocessing Tips

Fine-tuning

License

Acknowlegments

Citation