Instructions to use lightonai/LightOnOCR-2-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lightonai/LightOnOCR-2-1B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="lightonai/LightOnOCR-2-1B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForSeq2SeqLM

processor = AutoProcessor.from_pretrained("lightonai/LightOnOCR-2-1B")
model = AutoModelForSeq2SeqLM.from_pretrained("lightonai/LightOnOCR-2-1B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lightonai/LightOnOCR-2-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lightonai/LightOnOCR-2-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lightonai/LightOnOCR-2-1B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/lightonai/LightOnOCR-2-1B

SGLang

How to use lightonai/LightOnOCR-2-1B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lightonai/LightOnOCR-2-1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lightonai/LightOnOCR-2-1B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lightonai/LightOnOCR-2-1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lightonai/LightOnOCR-2-1B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use lightonai/LightOnOCR-2-1B with Docker Model Runner:
```
docker model run hf.co/lightonai/LightOnOCR-2-1B
```

update readme

#23

by IgorC - opened Jan 30

base: refs/heads/main

←

from: refs/pr/23

Discussion Files changed

+23

-4

Files changed (1) hide show

README.md +23 -4

README.md CHANGED Viewed

@@ -23,13 +23,29 @@ tags:
 - forms
 ---
 <div align="center">
   <img src="lightonocr-banner.png" alt="LightOnOCR-2-1B Banner" width="600"/>
 </div>
-# LightOnOCR-2-1B
-**Best OCR model .** LightOnOCR-2-1B is our flagship OCR model, refined with RLVR training for maximum accuracy. We recommend this variant for most OCR tasks.
 ## About LightOnOCR-2
@@ -45,7 +61,7 @@ LightOnOCR-2 is an efficient end-to-end 1B-parameter vision-language model for c
 ---
-📄 **[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)**
 ---
@@ -197,6 +213,9 @@ Apache License 2.0
   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/pdf/2601.14251}}
 }
 ```

 - forms
 ---
+# LightOnOCR-2-1B
+[![Speed](https://img.shields.io/badge/Speed-5.71%20pages%2Fs-brightgreen)](https://huggingface.co/lightonai/LightOnOCR-2-1B)
+[![Model Size](https://img.shields.io/badge/Model%20Size-1B%20params-orange)](https://huggingface.co/lightonai/LightOnOCR-2-1B)
+[![Demo](https://img.shields.io/badge/🚀%20Demo-Spaces-orange)](https://huggingface.co/spaces/lightonai/LightOnOCR-2-1B-Demo)
+[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
+[![SOTA](https://img.shields.io/badge/OlmOCR--Bench-SOTA-gold)](https://huggingface.co/lightonai/LightOnOCR-2-1B)
+[![Transformers](https://img.shields.io/badge/🤗%20Transformers-supported-yellow)](https://github.com/huggingface/transformers)
+[![vLLM](https://img.shields.io/badge/vLLM-supported-purple)](https://github.com/vllm-project/vllm)
+[![Blog](https://img.shields.io/badge/📝%20Blog-HuggingFace-yellow)](https://huggingface.co/blog/lightonai/lightonocr-2)
+[![Dataset](https://img.shields.io/badge/📊%20Dataset-LightOnOCR--mix-blue)](https://huggingface.co/datasets/lightonai/LightOnOCR-mix-0126)
+[![BBox Dataset](https://img.shields.io/badge/📊%20BBox%20Dataset-LightOnOCR--bbox--mix-blue)](https://huggingface.co/datasets/lightonai/LightOnOCR-bbox-mix-0126)
+[![Colab](https://img.shields.io/badge/Finetuning-Notebook-F9AB00?logo=googlecolab)](https://colab.research.google.com/drive/1WjbsFJZ4vOAAlKtcCauFLn_evo5UBRNa?usp=sharing)
+[![arXiv](https://img.shields.io/badge/arXiv-2412.13663-b31b1b.svg)](https://arxiv.org/abs/2412.13663)
+[![Website](https://img.shields.io/badge/LightOn-Website-blue?logo=google-chrome)](https://lighton.ai)
+[![LinkedIn](https://img.shields.io/badge/LightOn-LinkedIn-0A66C2?logo=linkedin)](https://www.linkedin.com/company/lighton/)
+[![X](https://img.shields.io/badge/@LightOnIO-X-black?logo=x)](https://x.com/LightOnIO)
 <div align="center">
   <img src="lightonocr-banner.png" alt="LightOnOCR-2-1B Banner" width="600"/>
 </div>
+# LightOnOCR-2-1B
+**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.
 ## About LightOnOCR-2
 ---
+📄 **[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)**
 ---
   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}}
 }
+[![Downloads](https://img.shields.io/badge/dynamic/json?url=https://huggingface.co/api/models/lightonai/LightOnOCR-2-1B&query=downloads&label=Downloads&color=blue)](https://huggingface.co/lightonai/LightOnOCR-2-1B)
+[![EU](https://img.shields.io/badge/🇪🇺%20Made%20in-Europe-blue)](https://huggingface.co/lightonai)
 ```